Machine Learning

This research aims to evaluate how full-arch implant-supported prostheses affect lip support in patients undergoing complex dental rehabilitation. The study focuses on how different clinical and laboratory decisions between diagnosis and prosthesis delivery may influence the prosthetic design and the patient's facial profile. Advanced 3D facial scanning and superimposition techniques allow precise measurement of vertical occlusion and lip support. Participants will undergo removal of their full-arch implant-supported prostheses, with digital facial images captured before and after this procedure using a facial scanner. This observational study leverages modern digital technology to measure changes objectively and does not involve experimental treatments. During the study, researchers will assess changes in the volume around the mouth area, distances between the lips and esthetic lines on the facial profile, and angles related to the nose and lips. These measurements occur on the same day as the prosthesis removal. The study helps improve understanding of facial changes related to dental prostheses and involves participants collaborating with the research protocol through imaging and clinical assessments.

Researchers are evaluating a new deep learning model that uses ultrasound video data to estimate blood volume before surgery. This prospective, single-center observational study focuses on adult patients scheduled for surgery, aiming to provide an accurate, non-invasive way to assess blood volume, which is important to prevent complications during surgery. The study is led by Shanghai 6th People's Hospital and addresses the current lack of practical methods for direct blood volume measurement. Participants will undergo preoperative ultrasound scans capturing videos of four major blood vessels: the Internal Jugular Vein, Subclavian Vein, Inferior Vena Cava, and Common Carotid Artery. The true blood volume is calculated using a clinical method involving hemoglobin concentration changes before and after acute normovolemic hemodilution. The collected ultrasound videos will be used to train and validate a deep learning model that combines convolutional and temporal analysis techniques to estimate blood volume from the ultrasound data. During the study, participants will have ultrasound video clips taken shortly before surgery. Researchers will analyze these images alongside blood volume values determined by the clinical method. The main outcome measured is the accuracy of the blood volume estimate within 30 minutes before surgery. The study does not involve treatment changes but focuses on data collection and model validation. Participation lasts through the preoperative period up to surgery, with no additional follow-up specified.

Researchers are studying adolescents and young adults with autism spectrum disorder (ASD), a condition marked by difficulties in communication, social skills, and repetitive behaviors. The study aims to understand how a brain stimulation technique called transcranial direct current stimulation (tDCS) might reduce symptoms like anxiety and impulsivity. The research also seeks to use brain activity data and clinical information to predict who will respond well to this treatment. Participants will receive active tDCS for 10 sessions over two weeks, one session per day on working days. During each 20-minute session, they will perform exercises designed to improve processing speed and executive function while receiving brain stimulation. After treatment, participants will be classified as responders or non-responders based on improvements in social responsiveness scores. Throughout the study, participants will undergo various assessments including behavioral scales, cognitive tests, and neurophysiological measurements at the start and after the treatment period. Researchers will track changes in social communication, repetitive behaviors, and brain function. This will help determine the effects of tDCS and identify characteristics that predict treatment response, with the total study duration extending up to the final follow-up in 2026.

Pregnant women with aplastic anemia (AA), a rare blood disorder causing bone marrow failure and low blood counts, face serious risks including heavy bleeding, infections, and complications for their babies like preterm birth and restricted growth. This research aims to develop and validate a tool to predict adverse pregnancy outcomes in women with AA, helping guide early clinical decisions and improve health for mothers and infants. The study combines retrospective and prospective data collection across multiple centers to address the current lack of comprehensive data in this area. The study involves observing pregnant women diagnosed with AA either before or during their pregnancy. Researchers will gather baseline information and diagnostic data, then follow participants regularly through questionnaires, phone calls, video consultations, online platforms, and in-person visits. They will record treatments, other health conditions, and pregnancy outcomes, aiming to build a prediction model for adverse outcomes. Participants will be monitored from their first hospital visit during pregnancy until 42 days after delivery. Data collected includes clinical assessments and neonatal Apgar scores shortly after birth. The study will provide detailed information on the health and risks for mothers and babies affected by AA during pregnancy, helping improve care strategies. The total participation duration varies depending on the timing of enrollment and delivery.

Researchers are conducting a real-world, observational study to evaluate the efficacy and safety of immune checkpoint inhibitors (ICIs) as the first-line treatment for advanced malignant tumors. The study includes patients with stage IV solid tumors such as non-small-cell lung carcinoma, stomach, breast, and urinary system cancers. The goal is to build a database combining immunohistochemistry, gene detection, imaging data, and survival outcomes to develop predictive models for immunotherapy biomarkers and treatment responses. The study collects various data points including immunohistochemistry results, gene testing, imaging, overall survival, progression-free survival, and objective response rate. It also monitors immune-related adverse events over time. This observational study does not involve experimental treatments but instead observes patients receiving ICIs as part of their standard care. The study spans at least two years for some outcome measures to assess long-term effects and safety. Participants will undergo regular assessments including survival evaluations and monitoring for treatment-related side effects. Data on progression-free survival will be collected at six months, with overall survival and immune-related adverse events followed for up to two years. The study aims to validate the predictive value of biomarker systems for immunotherapy and improve understanding of treatment effects in advanced cancer patients. Total participation duration will vary depending on individual treatment and follow-up schedules.

Researchers are developing an artificial intelligence tool to predict adverse outcomes in patients with acute pulmonary embolism using data from CT pulmonary angiography. This observational study collects clinical, laboratory, and CT scan information from patients admitted with acute pulmonary embolism to better understand and forecast complications within 30 days of hospital admission. The goal is to improve early risk assessment by comparing the new AI-based prediction model with established risk systems. The study divides participants into two groups: a derivation cohort used to create and test a logistic regression model, and a validation cohort to confirm the model's consistency. No interventions or treatments are given as part of the study. Data collected include CT parameters, echocardiography results, and blood markers such as cardiac troponin I and NT-pro BNP. The model's accuracy is assessed by comparing predicted outcomes with actual adverse events. Participants will have their clinical and imaging data analyzed from hospital admission, with follow-up to track treatment-emergent adverse events within 30 days and up to 2 years. Researchers monitor the incidence of these adverse events to evaluate the AI tool's predictive value. The entire participation involves data collection without experimental treatment, and the study spans several years to gather long-term safety information.

Atopic dermatitis (AD) is a long-lasting skin condition marked by repeated rashes and itching, which seriously impacts patients' quality of life and creates a significant economic burden. Researchers are evaluating an Artificial Intelligence assistant decision-making system (AIADMS) app that integrates the Treat to Target (T2T) strategy to improve disease control and clinical outcomes for patients with moderate to severe AD. This project aims to develop, train, and test the AIADMS app through clinical trials to verify its effectiveness and safety in managing AD. The AIADMS app is designed for both patients and clinicians with features such as reminders, photo uploads, self-evaluation, and data management. The app uses a deep learning model trained on over 10,000 clinical images to assess AD severity and clinical signs. Participants in the treatment group will use the app for disease management and be followed up at scheduled times up to 12 months after treatment, while the control group will receive routine face-to-face care. The app supports Android and iOS and provides automated assessments using tools like EASI, POEM, SCORAD, PP-NRS, and DLQI to help patients and clinicians monitor progress. Participants will have follow-up visits at 2 weeks, 4 weeks, 8 weeks, 12 weeks, 6 months, and 12 months after treatment. During these visits, evaluations using five treatment objectives will be performed. Data collected will include clinical scores, economic consumption, and satisfaction evaluations. The primary outcome is the overall efficiency rate of treatment objectives at 12 weeks. The study also involves tracking safety and patient adherence, with the entire study period lasting up to 12 months.

This research aims to investigate whether artificial intelligence (AI) can detect imaging features typical of Intensive Care Unit-acquired Weakness (ICUAW) using neuromuscular ultrasound. The study focuses on evaluating if AI-based image analysis can identify and monitor ICU patients with ICUAW and whether these AI results correlate with muscle weakness severity, visual muscle echogenicity grading, and 30- and 90-day patient outcomes. ICUAW is a common neuromuscular complication in critically ill patients, often difficult to assess due to patient sedation and limited cooperation during clinical exams. Participants will undergo non-invasive neuromuscular ultrasound of peripheral muscles in the upper and lower limbs. The ultrasound images will be processed using AI, specifically Convolutional Neural Networks, to classify muscle weakness severity. Explainable AI techniques will also be used to highlight the areas within the ultrasound images that contribute to the AI's decisions, helping to understand muscle changes. The study includes groups of critically ill patients with and without ICUAW as well as healthy controls. During the study, researchers will assess muscle echogenicity abnormalities by ultrasound on Day 14 and measure ICUAW severity through various scales. Additional outcomes such as ventilation duration, hospital stay length, survival, frailty, and overall recovery will be evaluated at 30 and 90 days. Data collection involves clinical examinations, scoring systems, and AI image analysis to improve diagnosis and monitoring of muscle weakness in ICU patients.

Researchers are evaluating a facial image-based artificial intelligence (AI) algorithm designed to screen for coronary artery disease (CAD) in high-risk community populations, including those with diabetes, hypertension, or aged over 65. The study aims to verify if the AI can accurately identify high-risk and low-risk groups by comparing the actual CAD prevalence, and to compare the AI screening detection rate with natural detection rates in a real-world cohort. Participants are divided into two groups. One group undergoes AI-based facial image screening followed by Coronary Computed Tomography Angiography (CCTA) to confirm CAD diagnosis. The other group represents a real-world setting without the AI screening and is observed to track natural CAD detection and major adverse cardiovascular events (MACE) over a six-month follow-up period. During the study, all participants receive monitoring for six months to assess CAD presence and cardiovascular outcomes, including heart attacks and revascularization procedures. Researchers will measure differences in CAD prevalence between AI-identified risk groups, detection rates of CAD, and the incidence of cardiovascular events and deaths. This follow-up period helps evaluate the AI screening’s performance compared to usual detection methods.

This research aims to develop and evaluate an AI-assisted nursing care robot called the "E-Nursing Assistant" to help address workforce shortages and heavy workloads faced by nurses. The study focuses on improving nursing work efficiency and quality by reducing nurses' workload using this technology. The trial is led by National Taiwan University Hospital and involves both nursing staff and patients in a clinical ward setting. The study tests the programming framework of the "E-Nursing Assistant," which performs functions like guiding patients to specific ward locations, providing equipment usage instructions, playing educational care videos, reminding patients about examination precautions, offering health education on specimen collection, and answering medical questions through an expert-developed Q&A system. This intervention is implemented in the ward for nursing care tasks that do not involve patient safety. Participants will be observed regarding nursing staff workload and stress levels before and after the intervention, as well as the time and frequency spent on nursing tasks. Feedback is gathered through interviews. Outcomes include measuring workload-related stress in nursing staff, system usability of the robot, and the number of patients served. The study includes adults aged 18 and older and involves both nurses actively working in wards and patients or caregivers willing to accept the intervention.