[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100557583":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":20,"centralContacts":21,"locations":20,"responsibleParty":27,"collaborators":31,"id":36,"slug":37,"hasResults":38,"nctId":39,"briefTitle":40,"officialTitle":40,"acronym":41,"eligibilityCriteria":42,"healthyVolunteers":43,"sex":44,"minAge":45,"maxAge":46,"enrollmentInfo":47,"targetDuration":20,"studyType":50,"phases":51,"briefSummary":53,"conditions":54,"keywords":56,"overallStatus":58,"whyStopped":20,"lastUpdateSubmitDate":59,"lastUpdatePostDateStruct":60,"startDateStruct":63,"completionDateStruct":65,"leadSponsor":67,"locationsCount":20},{"fullName":5,"class":6},"Tan Tock Seng Hospital","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Patients will undergo both automated and manual visual acuity testing","EXPERIMENTAL","Patient will perform manual visual acuity first, then be guided to another room to have the visual acuity tested on the automated VA device",[13],"Device: Automated visual acuity",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":20},"DEVICE","Automated visual acuity","The automated visual acuity device is developed in collaboration with Tan Tock Seng Hospital, Singapore Institute of Technology and Nanyang Technological University. It uses artificial intelligence for pose estimation and speech recognition to infer if the participant is reading the correct letters displayed on the screen.",[9],null,[22],{"name":23,"role":24,"phone":25,"phoneExt":20,"email":26},"Kelvin Z Li., MBBS, MTech, FRCOphth","CONTACT","+6562566011","contact@ttsh.com.sg",{"type":28,"investigatorFullName":29,"investigatorTitle":30,"investigatorAffiliation":5,"oldNameTitle":20,"oldOrganization":20},"PRINCIPAL_INVESTIGATOR","Li Zhenghao Kelvin","Consultant",[32,34],{"name":33,"class":6},"Singapore Institute of Technology",{"name":35,"class":6},"Nanyang Technological University","100557583","development-and-validation-of-an-automated-self-administered-visual-acuity-system-100557583",false,"NCT06540001","Development and Validation of an Automated Self-administered Visual Acuity System","AutoVA","Inclusion Criteria:\n\n1. Patients age \\>21 and able to give consent\n2. Patients who have at least counting finger vision\n3. Patients who is able to speak in an audible and clear voice\n4. Patients who is able to use a digital device independently (e.g. handphone)\n\nExclusion Criteria:\n\n1. Patients on wheelchair\u002F walking aids\n2. Patients with hearing difficulties\n3. Patients with speech difficulties\n4. Patients who have cognitive impairment\n5. Patients who are hemiplegic\u002F motor dysfunction\n6. Patients who have vision worse than counting fingers\n7. Patients who are pregnant",true,"ALL","21 Years","100 Years",{"count":48,"type":49},100,"ESTIMATED","INTERVENTIONAL",[52],"NA","Visual acuity tests, commonly conducted in clinics and used for health screenings, are becoming more in demand due to an aging population. Current online self-eye check apps are limited as they don\\'t accurately reflect true distance vision assessed in clinical settings. These tests, performed by trained personnel, are time-consuming and can cause delays in clinics. This project aims to develop an automated Visual Acuity (VA) station using AI technologies like speech-to-text and computer vision, hypothesizing that it can match the accuracy of manual assessments by clinic staff, thus potentially reducing waiting times and improving efficiency.",[55],"Visual Impairment",[57],"Visual acuity","NOT_YET_RECRUITING","2024-08-03",{"date":61,"type":62},"2024-08-06","ACTUAL",{"date":64,"type":49},"2024-08-01",{"date":66,"type":49},"2025-08-01",{"name":5,"class":6}]