Artificial Intelligence (AI)

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 Peoples 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.

Pulmonary hypertension is a condition that is often underdiagnosed due to its many causes. Researchers are evaluating an artificial intelligence-enabled electrocardiogram AI-ECG to screen for elevated pulmonary arterial pressure PAP in patients at high risk. This approach aims to identify pulmonary hypertension earlier, leading to further testing and potentially improving heart-related outcomes. The study is a randomized controlled trial sponsored by the National Defense Medical Center, Taiwan. Participants are divided into two groups. One group is screened with the AI-ECG system, and if they are identified as high-risk for pulmonary hypertension, they receive an echocardiogram to confirm the diagnosis and help guide treatment. The other group undergoes AI-ECG screening but continues with usual clinical care without additional echocardiography. The study uses this comparison to assess the value of AI-ECG-guided screening. During the study, participants will have ECGs and may have echocardiograms depending on their group and risk. Researchers will monitor heart measurements like pulmonary arterial pressure, left atrial size, right ventricular size, and left ventricular function within 90 days after starting the study. The main focus is on whether pulmonary arterial pressure exceeds 50 mmHg. The study lasts at least three months from randomization, with detailed heart imaging and function assessments to understand the AI-ECGs effectiveness in detecting pulmonary hypertension early.

Researchers are investigating the use of transcranial direct current stimulation tDCS combined with cognitive training to address symptoms in adolescents with autism spectrum disorder ASD. ASD is characterized by challenges in communication, social skills, repetitive behaviors, and emotional distress triggered by environmental changes. This study aims to determine if baseline resting-state EEG and clinical data can predict which individuals respond to tDCS treatment, helping to improve intervention strategies. Participants will receive active tDCS over 10 sessions during two weeks, with one session per day on consecutive working days. Each session includes 20 minutes of tDCS combined with an online cognitive training program consisting of five exercises targeting information processing speed and executive function. After treatment, participants will be classified as responders or non-responders based on changes in social responsiveness scores. During the study, assessments will measure changes in social communication and repetitive behaviors using the Social Responsiveness Scale, cognitive function through various CANTAB tests, and neurophysiological measures at the start and immediately after the intervention. Researchers will monitor behavioral changes and executive function outcomes to evaluate the impact of the combined treatment over the study period ending in 2026.

Researchers are conducting a multi-center clinical study to assess artificial intelligence AI algorithms for measuring heart function and size using echocardiography. The study aims to compare AI measurements with those of physicians at different experience levels, evaluate the accuracy and stability of AI, and explore its use in complex heart conditions like cardiomyopathy, valve disease, and coronary heart disease. The goal is to improve diagnostic consistency and clinical workflows across medical centers. The study involves measuring cardiac chamber size and function in 1600 participants using AI, senior physicians, and intermediate physicians. All measurements are made with Mindray ultrasonic machines. AI and intermediate physician results are completed within one day after data collection, while senior physician results are completed within one month. The study will establish a standardized reference system for AI-assisted echocardiographic measurements and evaluate AIs performance in special cases. Participants will undergo echocardiographic scans with measurements of left and right ventricular size and function, Doppler ultrasound indicators, and valve annulus displacements. Researchers will analyze data to compare AI and physician measurements, assess measurement deviations, and evaluate AIs efficiency in reducing analysis time. The study will run until July 2026, with ongoing data collection and analysis across multiple centers, aiming to promote wider clinical application of AI technology for cardiovascular disease diagnosis.

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 evaluating the accuracy of a new artificial intelligence system called EndoAIM for measuring colorectal polyp size during real-time endoscopy. Accurate measurement of polyp size, especially at the 10 mm threshold, is important for assessing the risk of advanced neoplasia and colorectal cancer, as well as deciding the best removal method. Current visual estimates by doctors can be inaccurate, leading to risks of delayed diagnosis or unnecessary procedures. In this study, EndoAIM will be used during the withdrawal phase of colonoscopy procedures to automatically estimate polyp diameters. This device aims to improve measurement accuracy compared to visual estimates or non-calibrated tools. The study focuses on real patients undergoing colonoscopy, assessing EndoAIMs performance in real-time clinical settings. Participants will undergo colonoscopy with EndoAIM used to measure polyps during the procedure. Researchers will compare measurement accuracy at 10 mm and 5 mm thresholds and evaluate the correctness of size estimations. The primary outcome is the difference in percentage accuracy at the 10 mm threshold during colonoscopy. Secondary outcomes include accuracy at the 5 mm threshold and the proportion of polyps and subjects measured correctly. The study includes adults aged 18 and older and involves monitoring during the colonoscopy procedure.

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 conducting a randomized controlled trial to compare the outcomes of screening mammography exams interpreted with and without the help of an FDA-cleared artificial intelligence AI decision-support tool in real-world U.S. settings. The study aims to determine if using AI improves breast cancer detection and recall rates during screening. This trial includes all adult patients undergoing screening mammography and all interpreting radiologists across six regional health systems. During the trial, each 3D screening mammogram will be randomly assigned to either the intervention group, where radiologists receive assistance from the AI decision-support tool, or the usual care group, where radiologists interpret the images alone. Randomization happens at the exam level immediately after image acquisition, and patients returning for screening in the second year will be re-randomized. Radiologists will see AI information only when it is available during interpretation, but they maintain full control over their final diagnosis. Participants will undergo routine screening mammograms, with data collected on cancer detection rates within 90 days and recall rates over about one year. Additional measures include false-positive rates, interval cancer rates, and trust in AI over several years. The study plans to assess these outcomes across approximately 400,000 screening exams, with monitoring done through the participating health systems. Radiologists and patients will contribute perspectives on AI use in medical imaging during the trial period.

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 models 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 models 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 tools 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.