Deep Learning Model

The aim of this study is to develop and evaluate an artificial intelligence-based model capable of analyzing periapical radiographs of maxillary and mandibular molars to predict the difficulty level of non-surgical root canal retreatment. By integrating deep learning techniques with routinely acquired periapical radiographs, this study aims to enhance diagnostic support, improve clinical decision-making, and facilitate appropriate case selection or referral in endodontic practice.

The goal of this observational study is to explore whether a Raman-based, deep learning-assisted approach can be used to develop an effective method for early pan-cancer screening. The study includes healthy individuals, patients at risk of cancer, and patients with diagnosed cancers. The main questions it aims to answer are: * Evaluating the deep-learning model's accuracy and specificity in identifying cancer-specific features in Raman spectral data and determining whether this method can accurately classify patients based on risk. * Identifying which model is more adaptable to the Raman spectrum * Providing an interpretable analysis of the model-generated diagnosis Participants are already being diagnosed and follow-up to determine the type of cancer.

Pulse pressure variation is a monitoring index that indicates the response to fluid therapy in patients receiving mechanical ventilation, and is used as a reference for patients with unstable hemodynamic conditions. However, it is invasive because it requires arterial puncture to collect it. In a previous study by the investigators, the investigators developed and verified an artificial intelligence model that predicts stroke volume variation, in real time using only the central venous pressure waveform. However, since a large vein such as the jugular vein must be punctured to collect the central venous pressure waveform, it is still invasive, and its clinical utility is low. Therefore, in this study, the investigators collected waveforms from peripheral veins that are less invasive and can be a wide range of applications because all surgical patients have them. The investigators aimed to develop and verify an artificial intelligence model that predicts pulse pressure variation obtained from peripheral venous waveforms .