[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"deep-learning\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:deep-learning":27},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,20,0,[8,44,74,105,132,158,184,207,235,256,276,300,327,351,376,408,430,455,487,509],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":28,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":32,"lastUpdatePostDateStruct":33,"startDateStruct":36,"completionDateStruct":38,"leadSponsor":40,"locationsCount":43},"100454949","impact-of-comorbidities-after-radical-cystectomy-using-a-predictive-method-with-artificial-intelligence-100454949",false,"NCT05204186","Impact of COMORBIDities After Radical Cystectomy Using a Predictive Method With Artificial Intelligence","Evaluation of the Impact of COMORBIDities on Morbidity and Mortality After Radical Cystectomy for Cancer Using a Predictive Method With Artificial Intelligence","COMORBID-AI","Inclusion Criteria:\n\n* 18 years and older\n* Patient treated by radical cystectomy for bladder cancer\n\nExclusion Criteria:\n\n* Computed tomography\u002Fmagnetic resonance evidence of distant metastases.","ALL","18 Years",{"count":20,"type":21},500,"ESTIMATED","OBSERVATIONAL","Clinician and the multidisciplinary team meeting in oncologic urology (MMO) play a key-role in the decision making. An unexplained surgeon attributable variance, probably linked to the subjective \"eyeball test\" effect, was identified as a strongest factor underlying non-compliance with guide line recommendations in the management of bladder cancer. So high-quality studies that identify barriers and modulators (such as comorbidities) of provider-level adoption of guidelines and how comorbidities are associated in making therapeutic choice and their impact in bladder cancer specific survival and overall survival, are crucial. To identify patients at high risk of early death, and to improve specific guideline for treatment might be decisive.\n\nIn order to assess survival, where mortality events compete, it will be more appropriate to compute a Cumulative Incidence Function (namely CIF). The investigators will compare outcomes across patient populations to obtain information to improve clinical decision-making. Such learning will be done through the use of neural networks or by applying population-based approaches, such as Genetic Algorithms (GA), Ant Colony Systems (ACS) and Particle Swarm Optimization (PSO), using as a four-stage based approach.\n\nFirst, the investigators propose a \"pretopology space\" in order to study a dynamic phenomenon. Second, the investigators recall that the K-means approach remains one of the most used approaches for classifying a set of elements (patients \u002F persons \u002F others) into K (disjunctive) clusters. Third, the investigators propose a learning pretopology space for enhancing the clustering. Such an approach can be assimilated in spirit to one applied with high success on deep learning. Fourth and last, the investigators propose a reactive method that is able to include some new elements or remove some contained elements",[25,26,27],"Bladder Cancer","Comorbidity","Deep Learning",[25,26,29,30],"Deep learning","PSO-Particle swarm optimization","RECRUITING","2026-06-12",{"date":34,"type":35},"2026-06-15","ACTUAL",{"date":37,"type":35},"2021-08-12",{"date":39,"type":21},"2028-12",{"name":41,"class":42},"Centre Hospitalier Universitaire, Amiens","OTHER",1,{"id":45,"slug":46,"hasResults":11,"nctId":47,"briefTitle":48,"officialTitle":49,"acronym":4,"eligibilityCriteria":50,"healthyVolunteers":51,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":52,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":54,"conditions":55,"keywords":60,"overallStatus":64,"whyStopped":4,"lastUpdateSubmitDate":65,"lastUpdatePostDateStruct":66,"startDateStruct":68,"completionDateStruct":70,"leadSponsor":72,"locationsCount":4},"100643796","non-contrast-breast-mri-diagnosis-and-risk-stratification-using-dwi-generated-synthetic-contrast-enhancement-100643796","NCT07598084","Non-Contrast Breast MRI Diagnosis and Risk Stratification Using DWI-Generated Synthetic Contrast Enhancement","Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI: Development and Clinical Validation of a Diffusion-Weighted Imaging-Based Synthetic Contrast-Enhanced MRI System for Non-Contrast Breast Cancer Diagnosis and Risk Stratification","Inclusion Criteria:\n\n1. Complete breast MRI data;\n2. Negative pathology biopsy results or negative follow-up examinations for at least 12 months for non-cancer cases;\n3. Positive biopsy results that meet the requirements for the pathological subtype of cancer for cancer cases;\n4. Original data that can be used to verify clinical status, including radiological and pathological reports;\n\nExclusion Criteria:\n\n1. Partial mastectomy or puncture biopsy on the diseased side of the breast prior to breast MRI examination;\n2. Poor image quality;\n3. Implants in the affected breast;",true,{"count":53,"type":21},12000,"This study is conducted under the ethics-approved project titled \"Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI''.The goal of this observational study is to develop an integrated breast MRI system that uses diffusion-weighted imaging (DWI) to create synthetic contrast-enhanced images. This system aims to diagnose and screen for breast cancer without the need for contrast agents, while using a generated risk score to perform imaging-based triage and risk stratification.\n\nParticipants will include people aged 18 and older who require a breast MRI either for evaluation of a suspicious finding or for high-risk screening.\n\nThis study seeks to answer two main questions:\n\n* Can synthetic contrast-enhanced images generated from DWI match real contrast-enhanced images in their ability to distinguish benign from malignant breast lesions?\n* Can the risk score derived from DWI-based synthetic images enable imaging-level risk stratification, allowing people at lower risk to avoid contrast agent injection? Researchers will compare the quality of synthetic images against real contrast-enhanced images and will recruit radiologists to assess how well these images perform for diagnostic and screening tasks. MRI data from participants undergoing breast MRI will be used to train, validate, and test this integrated system.",[56,57,58,59,27],"Breast Neoplasms","Artificial Intelligence (AI)","Magnetic Resonance Imaging (MRI)","Diffusion Magnetic Resonance Imaging",[61,62,63,29],"Breast","Magnetic Resonance Imaging","Artificial Intelligence","NOT_YET_RECRUITING","2026-06-05",{"date":67,"type":35},"2026-06-09",{"date":69,"type":21},"2026-06",{"date":71,"type":21},"2027-05",{"name":73,"class":42},"Peking University People's Hospital",{"id":75,"slug":76,"hasResults":11,"nctId":77,"briefTitle":78,"officialTitle":79,"acronym":4,"eligibilityCriteria":80,"healthyVolunteers":11,"sex":17,"minAge":81,"maxAge":82,"enrollmentInfo":83,"targetDuration":4,"studyType":85,"phases":86,"briefSummary":88,"conditions":89,"keywords":93,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":96,"lastUpdatePostDateStruct":97,"startDateStruct":99,"completionDateStruct":101,"leadSponsor":103,"locationsCount":43},"100586098","deep-learning-using-chest-x-rays-to-identify-high-risk-patients-for-lung-cancer-screening-ct-100586098","NCT06910956","Deep Learning Using Chest X-Rays to Identify High Risk Patients for Lung Cancer Screening CT","Deep Learning Using Routine Chest X-Rays and Electronic Medical Record Data to Identify High Risk Patients for Lung Cancer Screening CT","Major Inclusion Criteria:\n\n* Scheduled outpatient appointment with participating provider.\n* 50- to 77-year-old who currently or formerly smoked, to include persons potentially eligible for lung screening based on Medicare guidelines.\n* Recent (within 2 years) PA chest radiograph.\n\nExclusion Criteria:\n\n• History or signs\u002Fsymptoms of lung cancer. Recent (within 2 years) chest CT. Clinical indication for chest CT beyond lung cancer screening.","50 Years","77 Years",{"count":84,"type":21},1500,"INTERVENTIONAL",[87],"NA","The goal of this clinical trial is to evaluate whether an AI tool that alerts providers to patients at high 6-year risk of lung cancer based on their chest x-ray images will improve lung cancer screening CT participation. The main question it aims to answer is: Does the AI tool improve lung cancer screening CT participation at 6 months after the baseline outpatient visit?\n\nThe intervention is an alert to the provider to discuss lung cancer screening CT eligibility, for patients considered at high risk of lung cancer based on CXR-LC AI tool. Intervention and non-intervention arms will be compared to determine if lung cancer screening CT participation increases.\n\nIndividuals who are considered high-risk by the tool, but who do not meet the Medicare\u002FUSPSTF pack-year or quit-date lung screening eligibility criteria may be offered research lung screening CT.",[90,91,92,27],"Lung Cancer","Health Screening","Early Cancer Detection",[29,94,95],"AI","Chest x-ray","2026-05-06",{"date":98,"type":35},"2026-05-11",{"date":100,"type":35},"2025-05-20",{"date":102,"type":21},"2027-07-01",{"name":104,"class":42},"Massachusetts General Hospital",{"id":106,"slug":107,"hasResults":11,"nctId":108,"briefTitle":109,"officialTitle":110,"acronym":111,"eligibilityCriteria":112,"healthyVolunteers":51,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":113,"targetDuration":4,"studyType":85,"phases":115,"briefSummary":116,"conditions":117,"keywords":121,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":122,"lastUpdatePostDateStruct":123,"startDateStruct":125,"completionDateStruct":127,"leadSponsor":129,"locationsCount":131},"100558090","locally-optimised-contouring-with-ai-technology-for-radiotherapy-100558090","NCT06546592","Locally Optimised Contouring With AI Technology for Radiotherapy","LOCATOR - Locally Optimised Contouring With AI Technology for Radiotherapy","LOCATOR","Inclusion Criteria:\n\n* 18 years and older who are planned for primary breast malignancy\n* ECOG performance 0-2\n* Ability to understand and willingness to sign a written informed consent document\n* The target volume must be able to be objectively reviewed by current published national or international clinical guidelines\n\nExclusion Criteria:\n\n* Patients under 18 years of age\n* Patients unable to understand consent documents",{"count":114,"type":21},444,[87],"LOCATOR is a multicentre phase II randomised clinical trial that is looking at the process of contouring in radiation treatment for breast cancer patients. This study looks at whether contouring aided by artificial intelligence (AI) is comparable in quality to that of contouring done completely manually by a radiation oncologist. We are also looking at whether AI assisted contouring saves radiation oncologists time when compared to fully manual contouring.\n\nLOCATOR uses the LOCATOR software which is an in-house software developed locally and trained on local data.",[118,119,120,63,27],"Contouring","Segmentation","Radiation Therapy",[118,119,120,63,27],"2026-01-27",{"date":124,"type":35},"2026-01-29",{"date":126,"type":35},"2025-02-11",{"date":128,"type":21},"2030-04-30",{"name":130,"class":42},"Royal North Shore Hospital",3,{"id":133,"slug":134,"hasResults":11,"nctId":135,"briefTitle":136,"officialTitle":137,"acronym":138,"eligibilityCriteria":139,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":140,"targetDuration":4,"studyType":85,"phases":142,"briefSummary":143,"conditions":144,"keywords":149,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":150,"lastUpdatePostDateStruct":151,"startDateStruct":153,"completionDateStruct":155,"leadSponsor":157,"locationsCount":43},"100597694","evaluation-of-left-ventricular-ejection-fraction-using-an-accelerated-cardiac-cine-mri-sequence-with-deep-learning-based-image-reconstructions-100597694","NCT07061821","Evaluation of Left Ventricular Ejection Fraction Using an Accelerated Cardiac Cine-MRI Sequence With Deep Learning-based Image Reconstructions","Evaluation of Left Ventricular Ejection Fraction Using an Accelerated Cardiac Cine-MRI Sequence With Deep Learning-based Image Reconstructions Compared to the Reference Cine-MRI Sequence in the Assessment or Follow-up of Left Ventricular Hypertrophy","HVGLD","Inclusion Criteria:\n\n* Patient referred for cardiac MRI as part of the assessment or follow-up of left ventricular hypertrophy\n* Age ≥ 18 years old\n* Ability of the subject to understand and express his consent\n* Affiliation to the social security scheme\n\nExclusion Criteria:\n\n* Severe obesity (\\>140 kg) preventing the patient from entering the scanner bore, which has a diameter of less than 70 cm\n* Age ≥ 18 years old\n* Person under guardianship or curators, or deprived of liberty\n* Pregnant or breastfeeding woman\n* Known allergy to gadolinium chelates\n* Claustrophobia\n* Any contraindication to MRI\n* Arrhythmia\n* Inability to hold breath for more than 10 seconds",{"count":141,"type":21},61,[87],"Left ventricular hypertrophy (LVH) is a common condition that may result from hypertension, hypertrophic cardiomyopathy, aortic valve stenosis, or certain metabolic disorders. Cardiac imaging is essential for diagnosis, prognostic assessment, and quantification of cardiac function. While transthoracic echocardiography remains widely used, it is limited by acoustic window dependence and inter-observer variability. Cardiovascular Magnetic Resonance (CMR) imaging currently serves as the reference standard for measuring left ventricular ejection fraction (LVEF), cardiac volumes, and tissue characterization. However, conventional cine-CMR sequences require repeated breath-holds, which are often challenging for elderly or dyspneic patients, generating respiratory motion artifacts that compromise image quality. Accelerated cine-CMR sequences with deep learning-based image reconstructions offer a promising alternative by significantly reducing acquisition time while preserving image quality. This study aims to evaluate whether these accelerated cine-CMR sequences provide LVEF measurements concordant with conventional cine-CMR sequences, with potential to improve patient comfort and reduce examination time.",[145,146,27,147,148],"Left Ventricular Ejection Fraction","Cardiac Magnetic Resonance Imaging","Image Reconstruction","Left Ventricular Hypertrophy",[148,145,146,27,147],"2026-01-15",{"date":152,"type":35},"2026-01-16",{"date":154,"type":35},"2025-08-05",{"date":156,"type":21},"2026-09",{"name":41,"class":42},{"id":159,"slug":160,"hasResults":11,"nctId":161,"briefTitle":162,"officialTitle":163,"acronym":164,"eligibilityCriteria":165,"healthyVolunteers":51,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":166,"targetDuration":4,"studyType":85,"phases":168,"briefSummary":169,"conditions":170,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":174,"lastUpdatePostDateStruct":175,"startDateStruct":177,"completionDateStruct":179,"leadSponsor":181,"locationsCount":183},"100525215","ideal-study-blinded-rct-for-the-impact-of-ai-model-for-cerebral-aneurysms-detection-on-patients-diagnosis-and-outcomes-100525215","NCT06118840","IDEAL Study: Blinded RCT for the Impact of AI Model for Cerebral Aneurysms Detection on Patients' Diagnosis and Outcomes","Assessing the Impact of an Artificial Intelligence-Based Model for Intracranial Aneurysm Detection in CT Angiography on Patients' Diagnosis and Outcomes: The IDEAL Study - A Web-Based Multicenter, Double-Blinded Randomized Controlled Trial","IDEAL","Inclusion Criteria:\n\n* Adult inpatients and outpatients who are scheduled for head CTA scanning.\n\nExclusion Criteria:\n\n* Age under 18 years.\n* Patients with contraindications to CTA.\n* Modified Rankin Scale (mRS) score \\> 3.\n* Refuse to sign informed consent.\n* Participation in other clinical studies of intracranial aneurysms.\n* Patients with failed head CTA scanning or incomplete image data, or poor image quality.",{"count":167,"type":21},6450,[87],"This study (IEDAL study) intends to prospectively enroll more than 6450 patients who will undergo head CT angiography (CTA) scanning in the outpatient clinic. It will be carried out in 21 hospitals in more than 10 provinces in China. The patient's head CTA images will be randomly assigned to the True-AI and Sham-AI group with a ratio of 1:1, and the patients and radiologists are unaware of the allocation. The primary outcomes are sensitivity and specificity of detecting intracranial aneurysms. The secondary outcomes focus on the prognosis and outcomes of the patients.",[171,172,27,173],"Intracranial Aneurysm","CT Angiography","Double Bind Interaction","2025-10-01",{"date":176,"type":35},"2025-10-07",{"date":178,"type":35},"2024-05-20",{"date":180,"type":21},"2026-12",{"name":182,"class":42},"Jinling Hospital, China",21,{"id":185,"slug":186,"hasResults":11,"nctId":187,"briefTitle":188,"officialTitle":188,"acronym":4,"eligibilityCriteria":189,"healthyVolunteers":51,"sex":17,"minAge":18,"maxAge":190,"enrollmentInfo":191,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":193,"conditions":194,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":198,"lastUpdatePostDateStruct":199,"startDateStruct":201,"completionDateStruct":203,"leadSponsor":205,"locationsCount":43},"100605740","deep-learning-for-automated-discrimination-between-stage-t1-t2-and-t3-renal-cell-carcinoma-on-contrast-enhanced-ct-100605740","NCT07166445","Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT","Inclusion Criteria:\n\n1. Histopathologically confirmed renal cell carcinoma on postoperative specimen.\n2. Preoperative contrast-enhanced CT performed at our institution with slice thickness ≤ 1 mm and complete DICOM datasets.\n3. Postoperative pathologic staging clearly defined as pT1a-T2b or pT3a.\n4. CT image quality deemed adequate for analysis.\n\nExclusion Criteria:\n\n* 1\\. Pathologic subtype other than RCC. 2. Images with severe artifacts.","85 Years",{"count":192,"type":21},1000,"This study aims to develop and validate a contrast-enhanced CT-based deep-learning model for automatic and accurate preoperative discrimination between T1-T2 and T3 renal cell carcinoma. By quantifying the model's diagnostic performance on an independent test set-using AUC, sensitivity, specificity, positive\u002Fnegative predictive values, and decision-curve analysis-we will establish a decision-support tool that can be seamlessly integrated into clinical PACS, thereby reducing staging errors, refining surgical planning, and improving patient outcomes.",[195,196,197,27],"Carcinoma, Renal Cell","Diagnostic Imaging","Pathology","2025-09-03",{"date":200,"type":35},"2025-09-10",{"date":202,"type":35},"2024-09-01",{"date":204,"type":21},"2027-12-01",{"name":206,"class":42},"Peking University First Hospital",{"id":208,"slug":209,"hasResults":11,"nctId":210,"briefTitle":211,"officialTitle":212,"acronym":4,"eligibilityCriteria":213,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":214,"enrollmentInfo":215,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":217,"conditions":218,"keywords":222,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":226,"lastUpdatePostDateStruct":227,"startDateStruct":229,"completionDateStruct":231,"leadSponsor":233,"locationsCount":43},"100604224","predictive-performance-of-a-generative-model-for-corneal-tomography-after-icl-implantation-100604224","NCT07146737","Predictive Performance of a Generative Model for Corneal Tomography After ICL Implantation","Predictive Performance of a Generative Model for Corneal Tomography After Implantable Collamer Lens Implantation","Inclusion Criteria:(1) stable myopia (≤0.50D\u002Fyear change for 2 years), (2) ACD ≥2.80mm, (3) intact corneal endothelium (≥2000 cells\u002Fmm²), and (4) no confounding ocular\u002Fsystemic conditions.\n\nExclusion Criteria:(1) glaucoma-spectrum disorders or retinal vasculopathies, (2) prior corneal\u002Fintraocular surgery, (3) compromised corneal endothelium, (4) uncontrolled systemic diseases, and (5) pregnancy\u002Flactation.","45 Years",{"count":216,"type":21},818,"To evaluate the efficacy of a corneal tomography Imaging model in predicting postoperative vault based on preoperative corneal topography in Implantable Collamer Lens (ICL) surgery.",[219,220,27,221],"ICL","Vault","AI (Artificial Intelligence)",[223,224,225,63,27],"Implantable Collamer Lens (ICL) implantation surgery","Corneal tomography","vault","2025-08-21",{"date":228,"type":35},"2025-08-28",{"date":230,"type":35},"2025-07-01",{"date":232,"type":21},"2028-12-31",{"name":234,"class":42},"Second Affiliated Hospital of Nanchang University",{"id":236,"slug":237,"hasResults":11,"nctId":238,"briefTitle":239,"officialTitle":240,"acronym":241,"eligibilityCriteria":242,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":190,"enrollmentInfo":243,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":245,"conditions":246,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":248,"lastUpdatePostDateStruct":249,"startDateStruct":251,"completionDateStruct":253,"leadSponsor":255,"locationsCount":43},"100601505","construction-of-a-deep-learning-based-precise-diagnostic-framework-for-bladder-tumors-using-ultrasound-a-multicenter-ambispective-cohort-study-100601505","NCT07111364","Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound: A Multicenter, Ambispective Cohort Study","Construction of a Deep Learning-Based Precise Diagnostic Framework for Bladder Tumors Using Ultrasound","BCA-AI-US","Inclusion Criteria:① Suspected bladder mass detected by abdominal ultrasound (age ≥18 years);② Patients scheduled for surgical treatment of bladder tumors.\n\nExclusion Criteria:\n\n* Age \\>85 years;\n\n  * Patients unable to undergo abdominal\u002Ftransrectal ultrasound (e.g., uncooperative individuals, technically inadequate images);\n\n    * History of bladder tumor surgery, radiotherapy, chemotherapy, or systemic therapy within 3 months; ④ Patients with indwelling medical devices (e.g., double-J ureteral stents, urinary catheters);\n\n      * Failure to undergo bladder tumor surgery within 2 weeks post-ultrasound; ⑥ Non-urothelial carcinoma or pathologically unconfirmed diagnoses.",{"count":244,"type":21},400,"This study aims to develop an ultrasound image-based deep learning system to enable automatic segmentation, T-staging, and pathological grading prediction of bladder tumors. It seeks to enhance the objectivity, accuracy, and efficiency of bladder cancer diagnosis, reduce reliance on physician experience, and provide support for precision medicine and resource optimization.",[27,247,25],"Ultrasound","2025-08-13",{"date":250,"type":35},"2025-08-17",{"date":252,"type":35},"2025-05-27",{"date":254,"type":21},"2026-05-31",{"name":206,"class":42},{"id":257,"slug":258,"hasResults":11,"nctId":259,"briefTitle":260,"officialTitle":261,"acronym":4,"eligibilityCriteria":262,"healthyVolunteers":51,"sex":17,"minAge":18,"maxAge":214,"enrollmentInfo":263,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":265,"conditions":266,"keywords":269,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":271,"lastUpdatePostDateStruct":272,"startDateStruct":273,"completionDateStruct":274,"leadSponsor":275,"locationsCount":43},"100602778","diagnostic-performance-of-an-ai-based-model-for-tcm-constitution-classification-using-ophthalmic-imaging-100602778","NCT07127939","Diagnostic Performance of an AI-based Model for TCM Constitution Classification Using Ophthalmic Imaging","Diagnostic Performance of an Artificial Intelligence Driven Model for Traditional Chinese Medicine Constitution Classification Using Ophthalmic Imaging","Inclusion Criteria:\n\n1. Age: Participants aged between 18 and 60 years;\n2. Chronic Systemic Diseases: Defined according to the standards of the United States National Center for Chronic Disease Prevention and Health Promotion and the \"Healthy China 2030\" planning outline issued by the State Council. These are characterized as conditions lasting one year or longer that require continuous medical care, limit daily activities, or both. The four major chronic diseases in China include:Cardiovascular and cerebrovascular diseases,Malignant neoplasms,Diabetes mellitus and Chronic respiratory diseases;\n3. Cardiovascular and Cerebrovascular Diseases: This category includes:Hypertension: Stable hypertension without acute episodes, defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or currently undergoing antihypertensive treatment.Coronary Heart Disease: Diagnosed based on a history of coronary heart disease or confirmed through imaging evidence such as coronary angiography or CT angiography.\n\n   Heart Failure: Classified as New York Heart Association (NYHA) functional class II-IV with a left ventricular ejection fraction \\\u003C40%.Stroke: Diagnosed based on a history of ischemic or hemorrhagic stroke or confirmed through imaging evidence such as MRI or CT scans;\n4. Malignant Neoplasms: Participants with newly diagnosed, recurrent, metastatic malignant tumors, or those currently receiving specific treatments (e.g., chemotherapy, radiotherapy, immunotherapy), confirmed by histological or cytological examinations;\n5. Diabetes Mellitus: Participants meeting at least one of the following criteria:\n\n   Fasting blood glucose ≥7.0 mmol\u002FL (126 mg\u002FdL),2-hour post-oral glucose tolerance test blood glucose ≥11.1 mmol\u002FL (200 mg\u002FdL),Hemoglobin A1c ≥6.5%,Random blood glucose ≥11.1 mmol\u002FL (200 mg\u002FdL) accompanied by typical symptoms (e.g., polydipsia, polyuria, weight loss);\n6. Chronic Respiratory Diseases: This category includes:Chronic Obstructive Pulmonary Disease (COPD): Post-bronchodilator FEV₁\u002FFVC \\\u003C0.70 and FEV₁ \\\u003C80% of the predicted value, accompanied by at least one clinical symptom such as chronic cough, sputum production, or dyspnea.Asthma: Reversible airflow limitation, defined as a post-bronchodilator FEV₁ increase of ≥12% and ≥200 ml, accompanied by at least one clinical symptom such as recurrent wheezing, dyspnea, chest tightness, or cough;\n7. Healthy Subjects: Individuals who have not experienced any systemic diseases within the past year that require continuous medical care, limit daily activities, or both;\n8. Clear Diagnosis Based on Traditional Chinese Medicine and Western Medicine: Participants must have a definitive diagnosis established through both Traditional Chinese Medicine and Western Medicine methodologies;\n9. Absence of Other Ocular or Systemic Organic Diseases: Participants should have no other ocular diseases and\u002For systemic organic lesions that significantly affect the acquisition of ocular imaging.\n\nExclusion Criteria:\n\n1. Incomplete Clinical Data Supporting Diagnosis: Participants for whom the clinical data necessary to substantiate the diagnosis are incomplete;\n2. Significant Opacification of Other Ocular Optical Systems: Individuals presenting with pronounced obscurations in other ocular optical systems (e.g., severe corneal opacification, nuclear cataract grade IV or higher, severe vitreous hemorrhage or opacification) that impede the completion of comprehensive ocular examinations;\n3. Acute Systemic Organic Diseases: Patients concurrently suffering from acute systemic organic conditions (such as acute infections or decompensated organ failure) that prevent participation in the constitution identification procedures;\n4. Pregnancy or Lactation: Individuals who are pregnant or currently breastfeeding.",{"count":264,"type":21},1024,"To evaluate the diagnostic performance of a multimodal deep learning model for identifying biased Traditional Chinese Medicine (TCM) constitutions using ophthalmic imaging",[267,27,57,268],"Ophthalmic Imaging","Traditional Chinese Medicine Constitution",[267,27,63,270],"Traditional Chinese medicine constitution","2025-08-11",{"date":250,"type":35},{"date":230,"type":35},{"date":232,"type":21},{"name":234,"class":42},{"id":277,"slug":278,"hasResults":11,"nctId":279,"briefTitle":280,"officialTitle":280,"acronym":281,"eligibilityCriteria":282,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":283,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":285,"conditions":286,"keywords":290,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":291,"lastUpdatePostDateStruct":292,"startDateStruct":294,"completionDateStruct":296,"leadSponsor":298,"locationsCount":43},"100599735","deep-learning-model-predicts-pathological-complete-response-of-esophageal-squamous-cell-carcinoma-following-neoadjuvant-immunochemotherapy-100599735","NCT07088354","Deep Learning Model Predicts Pathological Complete Response of Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunochemotherapy","DL-ESCC","Inclusion Criteria:\n\n1. Pathologically confirmed esophageal squamous cell carcinoma (ESCC).\n2. Received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy.\n3. Underwent contrast-enhanced chest CT before initiation of neoadjuvant treatment.\n4. Underwent contrast-enhanced chest CT after completion of neoadjuvant treatment and prior to surgery.\n\nExclusion Criteria:\n\n1. Diagnosis of other malignancies.\n2. Received other anti-tumor therapies before or during neoadjuvant chemo-immunotherapy.\n3. Incomplete clinical data.\n4. Poor-quality CT imaging.",{"count":284,"type":21},300,"This study aims to develop and validate a deep learning model to predict pathological complete response (pCR) in patients with esophageal squamous cell carcinoma who have undergone neoadjuvant immunochemotherapy. Clinical, imaging, and pathological data from previously treated patients will be collected and analyzed. The model is expected to assist in predicting treatment outcomes and guide personalized therapeutic strategies.",[287,288,289,27],"Esophageal Squamous Cell Carcinoma","Neoadjuvant Immunochemotherapy","Pathological Complete Response",[287,288,27,289],"2025-07-24",{"date":293,"type":35},"2025-07-28",{"date":295,"type":35},"2025-03-01",{"date":297,"type":21},"2026-12-01",{"name":299,"class":42},"Tongji Hospital",{"id":301,"slug":302,"hasResults":11,"nctId":303,"briefTitle":304,"officialTitle":305,"acronym":306,"eligibilityCriteria":307,"healthyVolunteers":11,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":308,"targetDuration":310,"studyType":22,"phases":4,"briefSummary":311,"conditions":312,"keywords":316,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":318,"lastUpdatePostDateStruct":319,"startDateStruct":321,"completionDateStruct":323,"leadSponsor":325,"locationsCount":43},"100596869","bladder-cancer-staging-and-prediction-of-new-adjuvant-chemotherapy-efficacy-based-on-deep-learning-and-transfer-learning-in-ultrasound-magnetic-resonance-pathology-multimodal-multiscale-100596869","NCT07051083","Bladder Cancer Staging and Prediction of New Adjuvant Chemotherapy Efficacy Based on Deep Learning and Transfer Learning in Ultrasound-Magnetic Resonance-Pathology Multimodal Multiscale","Intelligent Diagnosis of Bladder Cancer Staging and Prediction of New Adjuvant Chemotherapy Efficacy Based on Deep Learning and Transfer Learning in Ultrasound-Magnetic Resonance-Pathology Multimodal Multiscale","MICS-BC","Inclusion Criteria:\n\n1. Ultrasound and other imaging examinations (CT, MR, etc.) suggest bladder masses and are suspicious for bladder cancer patients.\n2. The bladder is well filled, and no allergic reactions to ultrasound contrast agents are found.\n3. No surgery or radiotherapy\u002Fchemotherapy has been performed.\n4. Patients who meet the indications for surgical resection and are planned for surgical treatment, including one of the following:\n\n   1. Clinical symptoms consistent with suspected bladder cancer (such as gross hematuria, etc.);\n   2. Patients with confirmed primary or recurrent bladder cancer by cystoscopic biopsy;\n   3. Rapid urine cytology and urine cytology FISH testing suggest malignancy.\n\nExclusion Criteria:\n\n1. Individuals unable to tolerate surgery;\n2. Individuals allergic to ultrasound contrast agents, unable to undergo ultrasound contrast examination;\n3. Unsuccessful preoperative ultrasound contrast examination or non-compliant patients;\n4. Postoperative pathology does not indicate bladder cancer;\n5. Patients who have undergone chemotherapy or radiation therapy.",{"count":309,"type":21},480,"5 Years","Bladder cancer is the most common malignant tumor of the urinary system. The presence or absence of muscle invasion in early bladder cancer is an independent prognostic factor. The involvement of muscle invasion affects the choice of surgical methods and treatment. Preoperatively, the precise assessment of bladder cancer staging has important practical value. A more accurate preoperative assessment of bladder cancer staging can reduce overtreatment and provide a favorable basis for clinicians to choose more reasonable and effective surgical methods. Clinically, there has been a longstanding desire to diagnose the staging of bladder cancer through a simple, convenient, effective, and non-invasive examination. As relevant research progresses, a multi-omics diagnostic model will be beneficial in improving diagnostic efficiency. This project aims to establish a multi-omics artificial intelligence system based on deep learning and transfer learning to accurately diagnose the staging of bladder cancer and predict the efficacy of neoadjuvant chemotherapy. This system will assist in clinical treatment decision-making.",[25,313,27,314,315],"Staging","Neoadjuvant Chemotherapy","Contrast Enhanced Ultrasound",[25,317,313,27,314,315],"Multi-omics","2025-06-26",{"date":320,"type":35},"2025-07-03",{"date":322,"type":35},"2024-01-01",{"date":324,"type":21},"2026-12-31",{"name":326,"class":42},"Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University",{"id":328,"slug":329,"hasResults":11,"nctId":330,"briefTitle":331,"officialTitle":331,"acronym":4,"eligibilityCriteria":332,"healthyVolunteers":11,"sex":17,"minAge":333,"maxAge":4,"enrollmentInfo":334,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":336,"conditions":337,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":342,"lastUpdatePostDateStruct":343,"startDateStruct":345,"completionDateStruct":347,"leadSponsor":349,"locationsCount":43},"100591328","validation-of-the-prognostic-impact-of-a-retinal-photograph-based-cardiovascular-disease-risk-stratification-system-in-de-novo-hfref-100591328","NCT06978998","Validation of the Prognostic Impact of a Retinal Photograph-based Cardiovascular Disease Risk Stratification System in de Novo HFrEF","Inclusion Criteria:\n\n* Patients aged between 20 and 79 years with a left ventricular ejection fraction of 40% or less (assessed by transthoracic echocardiography), who have provided written consent for participation, have the capability to consent voluntarily\n\nExclusion Criteria:\n\n* Inability to obtain high-quality fundus photographs due to severe ophthalmologic conditions\n* Presence of extensive retinal diseases that significantly impair visualization of the retinal vasculature\n* Decline to provide informed consent for study participation, including:\n* Pregnant individuals\n* Individuals lacking decision-making capacity","20 Years",{"count":335,"type":21},100,"\"Despite significant advances in pharmacologic and device-based therapies, heart failure (HF) remains a major public health burden, with persistently high rates of hospitalization, impaired quality of life, and excess mortality-often exceeding those of leading malignancies. Prognosis in HF is shaped by its underlying etiology: ischemic HF often responds to revascularization strategies, whereas non-ischemic HF, particularly due to idiopathic or genetic cardiomyopathies, demonstrates highly variable outcomes and limited responsiveness to guideline-directed medical therapy (GDMT). Although left ventricular reverse remodeling (LVRR) is associated with favorable outcomes, only 40-50% of non-ischemic HF patients achieve meaningful LVRR with GDMT alone.\n\nIn this context of therapeutic uncertainty and prognostic heterogeneity, there is a critical need for novel, non-invasive risk stratification tools. Retinal imaging offers a unique advantage, enabling direct, in vivo visualization of systemic microvascular and neurovascular integrity. Prior work from our group has demonstrated that deep learning algorithms applied to retinal fundus photographs can estimate physiologic and metabolic markers-including CAC scores-and predict future cardiovascular events. The Reti-CVD scoring system, derived from these models, has been externally validated in independent populations.\n\nIn the present study, we aim to evaluate the prognostic utility of the Reti-CVD model in a cohort of patients with newly diagnosed HF and reduced ejection fraction. Specifically, we will assess whether retinal-derived risk scores at baseline are associated with adverse clinical outcomes, including cardiovascular events and all-cause mortality, and whether prognostic performance varies according to HF etiology.\"",[338,339,340,27,341],"Heart Failure","Cardiomyopathies","Retinal Photograph","Reti-CVD","2025-05-11",{"date":344,"type":35},"2025-05-18",{"date":346,"type":35},"2024-12-10",{"date":348,"type":21},"2030-12-10",{"name":350,"class":42},"Yonsei University",{"id":352,"slug":353,"hasResults":11,"nctId":354,"briefTitle":355,"officialTitle":356,"acronym":4,"eligibilityCriteria":357,"healthyVolunteers":51,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":358,"targetDuration":4,"studyType":85,"phases":360,"briefSummary":361,"conditions":362,"keywords":365,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":367,"lastUpdatePostDateStruct":368,"startDateStruct":370,"completionDateStruct":372,"leadSponsor":374,"locationsCount":43},"100590793","ai-assisted-smart-interactive-healthcare-robot-100590793","NCT06972043","AI-Assisted Smart Interactive Healthcare Robot","Development and Testing of an AI-Assisted Smart Interactive Healthcare Robot Program","1. Nurse\n\n   Inclusion Criteria:\n   * Aged 18 years or older.\n   * Currently employed as a ward nurse.\n\n   Exclusion Criteria:\n   * Nurses who do not actively participate in ward care, such as nursing unit supervisors.\n2. Patients\u002FCaregivers\n\nInclusion Criteria:\n\n* Aged 18 years or older.\n* Willing to accept the \"E-Nursing Assistant\" intervention for care tasks that do not involve patient safety.\n\nExclusion Criteria:\n\n* Patients or caregivers who are unconscious or unable to communicate verbally or in writing to complete the research interview.",{"count":359,"type":21},160,[87],"To address workforce shortages and increasing workloads in nursing, technological solutions and AI-powered robots for ward navigation have been introduced. However, limitations remain in their application to clinical care. This study aims to develop and test a programming framework for an AI-assisted nursing care robot (\"E-Nursing Assistant\") to reduce nurses' workload and improve the efficiency and quality of care.",[363,57,27,364],"Nursing","Mechine Learning",[363,57,366],"Medical Science and Technology","2025-05-06",{"date":369,"type":35},"2025-05-14",{"date":371,"type":35},"2025-04-30",{"date":373,"type":21},"2026-03-27",{"name":375,"class":42},"National Taiwan University Hospital",{"id":377,"slug":378,"hasResults":11,"nctId":379,"briefTitle":380,"officialTitle":381,"acronym":382,"eligibilityCriteria":383,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":384,"enrollmentInfo":385,"targetDuration":387,"studyType":22,"phases":4,"briefSummary":388,"conditions":389,"keywords":394,"overallStatus":64,"whyStopped":4,"lastUpdateSubmitDate":401,"lastUpdatePostDateStruct":402,"startDateStruct":404,"completionDateStruct":406,"leadSponsor":407,"locationsCount":4},"100576961","ga-68-dolacga-pet-scan-in-hcc-under-rfa-100576961","NCT06792097","Ga-68 Dolacga PET Scan in HCC Under RFA","The Role of Ga-68 Dolacga PET Scan in Patients With Hepatocellular Carcinoma Under Radiofrequency Ablation","HCC; RFA","Inclusion Criteria:\n\n* Patients with hepatocellular carcinoma (BCLC stage 0 and stage A) eligible for radiofrequency ablation (RFA) treatment exhibit the following characteristics:\n\n  1. A single liver tumor, ≤ 2 cm, classified as BCLC stage 0 (very early stage).\n  2. Tumors ≤ 3 in number, each ≤ 3 cm, or a single tumor ≤ 5 cm, classified as BCLC stage A (early stage) liver cancer.\n  3. ECOG performance status of 0.\n  4. Age ≥ 18 years.\n\n     Exclusion Criteria:\n* Intermediate (BCLC B stage) and advanced (BCLC C stage) liver cancer patients:\n\n  1. A single tumor \\> 5 cm, or multiple tumors \\> 3 cm.\n  2. Diffuse hepatocellular carcinoma.\n  3. Vascular invasion (e.g., portal vein obstruction).\n  4. Extrahepatic tumor spread.\n* Early-stage liver cancer (BCLC A stage):\n\n  1. Contraindications for radiofrequency ablation (RFA).\n  2. Target lesions previously treated with local therapies, including surgical resection, percutaneous ethanol injection (PEI), or liver transplantation.","80 Years",{"count":386,"type":21},10,"2 Years","This study aims to investigate the use of Ga-68 Dolacga PET scan technology to assess treatment response and liver function changes in patients of early-stage liver cancer receiving RFA. The main questions it aims to answer are:\n\n1. How to assess treatment response and liver function changes in hepatocellular carcinoma patients undergo RFA via Ga-68 Dolacga PET scan?\n2. Compared with computed tomography (CT) scans, how effective is Ga-68 Dolacga PET scan for treatment response assessment?\n3. What is the correlation between Ga-68 Dolacga PET scan findings and patient treatment outcomes by tracking liver function and tumor recurrence after RFA?\n\nParticipants will:\n\n1. Undergo Ga-68 Dolacga PET scans and computed tomography before and one month after RFA treatment, followed by monitoring every three months thereafter.\n2. Total liver functional volume and residual liver functional volume are obtained from Ga-68 Dolacga PET scan",[390,391,392,393,27],"Hepatocellular Carcinoma (HCC)","Radiofrequency Ablation","PET Scan","Computed Tomography",[395,396,397,398,399,400],"Hepatocellular carcinoma","Radiofrequency ablation","Ga-68 Dolacga PET scan","Liver function","Neural network","Radiomics","2025-01-19",{"date":403,"type":35},"2025-01-24",{"date":405,"type":21},"2025-02-01",{"date":324,"type":21},{"name":375,"class":42},{"id":409,"slug":410,"hasResults":11,"nctId":411,"briefTitle":412,"officialTitle":413,"acronym":4,"eligibilityCriteria":414,"healthyVolunteers":11,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":415,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":417,"conditions":418,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":421,"lastUpdatePostDateStruct":422,"startDateStruct":424,"completionDateStruct":426,"leadSponsor":428,"locationsCount":43},"100532316","artificial-intelligence-for-screening-of-multiple-corneal-diseases-100532316","NCT06211218","Artificial Intelligence for Screening of Multiple Corneal Diseases","Application of Deep Learning for Screening Multiple Corneal Diseases","Inclusion Criteria:\n\n1. The quality of slit-lamp images should clinical acceptable.\n2. More than 90% of the slit-lamp image area including three main regions (sclera, pupil, and lens) are easy to read and discriminate.\n\nExclusion Criteria:\n\n1）Insufficient information for diagnosis.",{"count":416,"type":21},3000,"This study developed a deep learning algorithm based on anterior segment images and prospectively validated its ability to identify corneal diseases.The effectiveness and accuracy of this algorithm was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve.",[27,419,420],"Corneal Disease","Screening","2024-10-31",{"date":423,"type":35},"2024-11-04",{"date":425,"type":35},"2020-12-06",{"date":427,"type":21},"2024-12-06",{"name":429,"class":42},"Tianjin Eye Hospital",{"id":431,"slug":432,"hasResults":11,"nctId":433,"briefTitle":434,"officialTitle":434,"acronym":4,"eligibilityCriteria":435,"healthyVolunteers":51,"sex":17,"minAge":18,"maxAge":436,"enrollmentInfo":437,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":438,"conditions":439,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":446,"lastUpdatePostDateStruct":447,"startDateStruct":449,"completionDateStruct":451,"leadSponsor":453,"locationsCount":43},"100566307","development-and-demonstration-of-intelligent-assessment-based-on-multi-modal-information-fusion-for-tumor-risk-and-diagnosis-and-treatment-100566307","NCT06653478","Development and Demonstration of Intelligent Assessment Based on Multi-modal Information Fusion for Tumor Risk and Diagnosis and Treatment","Inclusion Criteria:\n\n1. Participants with the suspected of lung cancer\u002Fnode, or stomach cancer\u002Flesion, or colorectal cancer\u002Fleision\n2. Participants that have signed informed consent.\n3. Participants with detailed electronic medical records, image records, pathological records, multi-omics information, and other important clinical diagnostic information.\n4. Healthy participants with no clinical diagnosis of lung cancer\u002Fnode, or stomach cancer\u002Flesion, or colorectal cancer\u002Fleision.\n\nExclusion Criteria:\n\n1. Participants with primary clinical and pathological data missing.\n2. Participants lost to follow-up.\n3. Participants with too poor medical image quality to perform segment and mark ROI accurately","75 Years",{"count":416,"type":21},"To improve the accuracy of risk prediction, screening and treatment outcome of cancer, we aim to establish a medical database that includes standardized and structured clinical diagnosis and treatment information, image features, pathological features, and multi-omics information and to develop a multi-modal data fusion-based technology system using artificial intelligence technology based on database.",[63,27,90,440,441,442,443,444,445],"Lung; Node","Stomach Cancer","Colon Cancer","Cancer Risk","Cancer Screening","Cancer, Treatment-Related","2024-10-20",{"date":448,"type":35},"2024-10-22",{"date":450,"type":35},"2022-06-01",{"date":452,"type":21},"2026-10-01",{"name":454,"class":42},"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology",{"id":456,"slug":457,"hasResults":11,"nctId":458,"briefTitle":459,"officialTitle":459,"acronym":460,"eligibilityCriteria":461,"healthyVolunteers":11,"sex":462,"minAge":18,"maxAge":463,"enrollmentInfo":464,"targetDuration":310,"studyType":22,"phases":4,"briefSummary":465,"conditions":466,"keywords":469,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":477,"lastUpdatePostDateStruct":478,"startDateStruct":480,"completionDateStruct":482,"leadSponsor":484,"locationsCount":486},"100563656","implementation-of-surgical-safety-and-intraoperative-metastasis-identification-through-deep-learning-multicentric-video-collection-for-minimally-invasive-sentinel-lymph-node-dissection-in-uterine-malignancies-100563656","NCT06619002","Implementation of Surgical Safety and Intraoperative Metastasis Identification Through Deep Learning: Multicentric Video Collection for Minimally Invasive Sentinel Lymph Node Dissection in Uterine Malignancies","LYSE","Inclusion Criteria:\n\n* Women undergoing MIS sentinel lymph node dissection for endometrial or cervical cancers\n* Availability of video\n* Age \\>18 years\n* Willingness to participate in the study and to provide informed consent\n\nExclusion Criteria:\n\n* Previous pelvic radiotherapy treatments\n* Severe endometriosis or other conditions able to alter the pelvic anatomy","FEMALE","99 Years",{"count":335,"type":21},"The loco-regional metastatic or non-metastatic status of lymph nodes (LN) is critical for the therapeutic management of most cancer patients. Indeed, the presence or absence of lymphatic metastasis is essential for the accurate staging of the disease and strongly influence the prognosis and adjuvant treatment regimens. An important revolution in oncological surgery has been the introduction of the concept of sentinel lymph node (SLN) biopsy to reduce the complications of extensive loco-regional lymphadenectomies. SLN identification through ICG- based near-infrared fluorescence (NIR) cervical injection and its dissection is now recommended by European guidelines to stage uterine malignancies (endometrial and cervical cancers). However, SLN procedures have several limitations. In 11.2% of cases intra- or postoperative complications are reported due to anatomical structures injuries (vessels, nerves and lymphatic channels disruptions). Common mistakes, especially when the learning curve is not completed (at least 40 procedures), include mapping failure (25%) and removal of second\u002Fthird-level nodes and\u002For empty nodes packets (8-14%). Additionally the intraoperative accuracy of frozen section is still far to be adequate with only the 65% of SLN metastasis detection.\n\nThese limitations are a result of the lack of precision of current SLN localization and analysis as well as of the overall difficulty of visualizing lymph nodes and other critical structures in the retroperitoneum.\n\nCurrently, studies on the safety of surgical procedures are based on perioperative clinical information and postoperative reports written by the surgeons themselves. Today, videos guiding minimally invasive surgical interventions allow for objective documentation of the procedure and provide opportunities to explore solutions for enhancing safety in the operating room. With an increasing use of endoscopic systems across different specialties, there is a need for standardization of training, assessment, testing and sign-off as a competent surgeon in order to improve patient safety. In laparoscopic lymph node dissection in endometrial and cervical cancer, a standardize stepwise approach to the procedure is highly recommended, by identifying key anatomic landmarks and structures, in various scenarios, that could prevent vascular, nervous and ureters injuries and enhance the mapping rate. Therefore, quantifying and studying intraoperative events such as the rate of achieving the right space dissections and anatomic structures visualization as a recommended step for safety and proficiency, would enable the examination of how best to implement guideline recommendations and seek new solutions to reduce operative risks. These videos could be utilized to train and validate artificial intelligence (AI) algorithms, with the potential to assist surgeons in the operating room and make the procedures safer. Additionally, the visual information (ICG intensity) could hide data that the AI can analyze and correlate with anatomopathological reports. By the integration of AI tool with laparoscopic\u002Frobotic platform it is possible to enhance MIS video streams in real time with surgical phases detection, events recognition, ICG signal intensity, anatomical structure identification and auto-targeting",[467,468,27,63],"Endometrial Cancer","Cervical Cancer",[470,471,472,473,474,475,476],"endometrial cancer","cervical cancer","deep learning","computer vision","sentinel lymph node","artificial intelligence","indocyanin green","2024-10-01",{"date":479,"type":35},"2024-10-02",{"date":481,"type":35},"2024-09-17",{"date":483,"type":21},"2027-09-30",{"name":485,"class":42},"Fondazione Policlinico Universitario Agostino Gemelli IRCCS",2,{"id":488,"slug":489,"hasResults":11,"nctId":490,"briefTitle":491,"officialTitle":492,"acronym":4,"eligibilityCriteria":493,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":436,"enrollmentInfo":494,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":496,"conditions":497,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":500,"lastUpdatePostDateStruct":501,"startDateStruct":503,"completionDateStruct":505,"leadSponsor":507,"locationsCount":43},"100552774","deep-learning-for-preoperative-pulmonary-assessment-in-thoracic-ct-100552774","NCT06477458","Deep Learning for Preoperative Pulmonary Assessment in Thoracic CT","Application of Deep Learning in CT Imaging of Elective Thoracic Surgery Patients: Assessing Preoperative Abnormal Pulmonary Function","Inclusion Criteria:\n\n* (1) Signing of the informed consent form;\n* (2) Male or female, aged 18-75 years;\n* (3) Undergoing elective thoracic surgery;\n* (4) Good preoperative pulmonary function cooperation and complete reporting;\n* (5) Preoperative chest single\u002Fdual phase CT scans without significant artefacts and with complete imaging;\n* (6) The interval between preoperative pulmonary function and single\u002Fdual phase CT scans does not exceed one month.\n\nExclusion Criteria:\n\n* (1) Poor preoperative pulmonary function cooperation or missing reports;\n* (2) Preoperative chest single\u002Fdual phase CT scans exhibit significant artefacts or image omission;\n* (3) The interval between preoperative pulmonary function and single\u002Fdual phase CT scans exceeds one month;\n* (4) Complication with severe respiratory disorders (such as lung transplantation, pneumothorax, giant bullae, etc.);\n* (5) Coexisting with other severe functional impairments;\n* (6) Patients with obstructive lesions such as airway or esophageal stenosis;\n* (7) Height beyond the predicted equation range (Female \\\u003C 1.45m; Male \\\u003C 1.55m);\n* (8) Medication use before pulmonary function testing that does not meet the cessation guidelines;\n* (9) Pulmonary function report quality graded D-F.",{"count":495,"type":21},2000,"The trial was designed as a single-centre, non-interventional prospective observational study to utilize deep learning technology combined with computed tomography (CT) images to precisely predict the pulmonary function indicators of thoracic surgery preoperative patients.",[498,499,27],"Elective Thoracic Surgery","Pulmonary Function","2024-06-26",{"date":502,"type":35},"2024-06-27",{"date":504,"type":35},"2023-10-01",{"date":506,"type":21},"2024-12-30",{"name":508,"class":42},"The First Affiliated Hospital of Guangzhou Medical University",{"id":510,"slug":511,"hasResults":11,"nctId":512,"briefTitle":513,"officialTitle":514,"acronym":4,"eligibilityCriteria":435,"healthyVolunteers":51,"sex":17,"minAge":18,"maxAge":436,"enrollmentInfo":515,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":438,"conditions":516,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":517,"lastUpdatePostDateStruct":518,"startDateStruct":520,"completionDateStruct":521,"leadSponsor":523,"locationsCount":43},"100471996","artificial-intelligence-system-for-assessment-of-tumor-risk-and-diagnosis-and-treatment-100471996","NCT05426135","Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment","Development of an Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment Based on Multimodal Data Fusion Using Deep Learning Technology",{"count":416,"type":21},[63,27,90,440,441,442,443,444,445],"2022-06-17",{"date":519,"type":35},"2022-06-21",{"date":450,"type":35},{"date":522,"type":21},"2026-10",{"name":454,"class":42}]