Clinical Research Directory

Technology Use, Safe Health Information, and Artificial Intelligence Among Older Adults

Objective The aim of this research is to evaluate how improving the technology use skills of older adults in nursing homes changes their perceptions of access to safe health information and the use of artificial intelligence. Hypotheses H1: Improving the technology use skills of older adults in nursing homes changes their perceptions of access to safe health information and the use of artificial intelligence.

MRI-Based Artificial Intelligence Prediction of Fluoroscopic C-Arm Angles for Lumbar Transforaminal Epidural Steroid Injections

This study aims to prospectively evaluate the accuracy of artificial intelligence (AI) in predicting fluoroscopic C-arm oblique and craniocaudal angles from pre-procedural lumbar magnetic resonance (MR) images for lumbar transforaminal epidural steroid injections (TFESI). The AI predictions will be compared to the final reference C-arm angles determined by an experienced operator during the procedure.

Benchmarking Large Language Models Against Tumour Boards for Oncology Treatment Recommendations

BEACON (Benchmarking AI for Clinical Oncology decisioNmaking) is a prospective, multicentre, comparative, blinded, non-interventional benchmark comparing the treatment recommendations of six frontier large language models (LLMs) with those of multidisciplinary tumour boards (RCP) in oncology treatment planning. One hundred standardised synthetic cases (20 per localisation, across breast, lung, urological, digestive and gynaecological cancers) are submitted as identical structured input to two independent tumour boards per localisation and to six frontier LLMs. Every recommendation, human or model, is decomposed into five predefined decision domains (intent, surgery, radiotherapy, systemic therapy with its line, work-up and biomarkers). For each domain, concordance between two recommendations is scored 2 when the same options are selected, 1 when at least one option is shared, and 0 when none is shared; the score is computed automatically from the recommendation forms, without human scoring. The primary endpoint is the mean domain score between each LLM and each tumour board, read against the same score computed between the two tumour boards of the same case, so that humans and models are measured by an identical rule and no respondent contributes to its own reference. A co-primary safety endpoint captures the proportion of recommendations carrying serious harm potential, because concordance alone can conceal dangerous errors. Where the two boards select different options, the case-domain is classified EQUIPOISE and, like every discordance, is adjudicated by an independent panel blinded to the source of the recommendation. Tumour boards record their recommendations during the meeting itself, on a dedicated platform (https://etude-beacon.fr) that reveals a case only once it is presented and time-stamps that reveal, so that no board can read a case in advance. BEACON is designed as reusable, openly licensed, pre-registered infrastructure: all synthetic cases, evaluation rubrics, scoring algorithms, verbatim prompts and raw model outputs are released for full reproducibility.

AI-Assisted 3D Reconstruction for Lung Resection Planning

This prospective observational study aims to validate an artificial intelligence (AI)-based system for automated three-dimensional (3D) reconstruction of pulmonary anatomy from preoperative chest computed tomography (CT) scans in patients undergoing lung resection. The AI system will automatically identify and reconstruct relevant pulmonary anatomical structures, including pulmonary arteries, veins, bronchi, lobes, and segments. AI-generated 3D reconstructions will be compared with expert manual 3D reconstructions and with anatomical findings documented during surgery. The primary objective is to assess the anatomical accuracy of AI-generated 3D reconstructions in identifying surgically relevant bronchovascular anatomy and anatomical variants. Secondary objectives include evaluating accuracy for individual anatomical structures, discrepancies between automated and manual reconstructions, concordance with intraoperative findings, and the time required to generate and review the automated reconstruction. The study is observational and will not modify the planned surgical procedure or standard patient care.

AI-Assisted Versus Independent Heart Sound Screening for Congenital Heart Disease in Children

This is a prospective, school-based, cluster-randomized controlled trial conducted in Qinghai Province, China. The study will evaluate whether artificial intelligence (AI) can help primary care physicians identify congenital heart disease (CHD) during large-scale screening of children and adolescents. Eligible schools, rather than individual participants, will be randomly assigned to one of two screening approaches: independent cardiac auscultation by primary care physicians or AI-assisted cardiac auscultation. In both groups, primary care physicians will perform cardiac auscultation and record heart sounds. In the AI-assisted group, the physicians will also receive AI analysis of the recorded heart sounds to support their final screening decision. Participants will undergo medical history collection, cardiac auscultation, heart-sound recording, and echocardiography. Echocardiography and expert review will serve as the reference standard. The primary outcome is the sensitivity of each screening approach for detecting CHD that may require clinical intervention or specialist management. The study will also assess detection of all CHD, referral completion, confirmed diagnoses after referral, and related screening outcomes.

Realistic in Generation of HEp-2 Cell Images Using Latent Diffusion Models: a Multi-center Visual Turing Test

The objective of this prospective observational study is to rigorously examine the feasibility and efficacy of utilizing latent diffusion models for data augmentation in anti-nuclear antibody (ANA) Hep-2 cell immunofluorescence images. The main question it aims to answer is: Can the application of such models potentially enhance the data quality, increase sample diversity, or improve the accuracy and efficiency of subsequent analytical processes (like disease diagnosis and classification) when utilized with ANA-related images?

AI in SK Histopathological Diagnosis

The aim of this study is to evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm in the histopathological diagnosis of Seborrheic keratosis compared to Certified Dermatopathologists.

Evaluation of Different Color Determination Methods

With the increasing aesthetic demands in dental practice, the accuracy and consistency of tooth color determination methods have gained importance for clinical success. This study aims to compare different tooth color determination methods in terms of accuracy and repeatability under in vivo conditions. A spectrophotometric system (VITA Easyshade V) will be used as the reference method. In comparison, the results obtained through visual assessment by observers, a digital intraoral scanner (TRIOS 3), and artificial intelligence-based color analysis systems will be evaluated. Statistical analyses will be conducted based on ΔE, L\*, a\*, b\*, and shade code data obtained from the measurements, and the agreement among the methods as well as their correlations with the spectrophotometer will be examined. This study aims to demonstrate the clinical validity of digital and AI-assisted methods and to emphasize the importance of objective approaches in tooth shade selection. The aim of this study is to compare four different tooth color determination methods used in aesthetic dentistry-spectrophotometric system, observer-based visual assessment, digital intraoral scanner, and artificial intelligence-assisted analysis system-in terms of accuracy and repeatability under in vivo conditions. The study seeks to statistically evaluate and compare the color measurement values obtained from these methods with those of the spectrophotometric system, which is considered the reference method, and to determine the level of agreement among the methods. In addition, it aims to assess the clinical applicability and reliability of digital technologies and artificial intelligence-based systems in tooth color selection.

AI Based Muscular Ultrasound to Assess Intensive Care Unit-acquired Weakness

The aim of this observational case-control study is to investigate, whether artificial intelligence can detect ultrasound-derived imaging characteristics typical for intensive care unit-acquired weakness. The main questions it aims to answer are: 1. Is the evaluation of specific parameters of neuromuscular ultrasound using AI-based image analysis suitable for detecting and monitoring critically ill ICU patients with ICUAW? 2. Do the results of AI-based ultrasound image analysis correlate with: (A) the severity of ICUAW (B) the visual grading of muscle echogenicity (C) the 30- and 90-day-outcome?

Construction and Validation of a Coronary Artery Disease-Specific Large Language Model and Its Evaluation Framework (CorAI)

Coronary artery disease (CAD) affects an estimated 11.39 million people in China, and mortality has continued to rise since 2012. Quality-of-care data indicate persistent gaps in diagnostic and therapeutic guideline adherence, incomplete implementation of secondary prevention, and suboptimal risk-factor control. Large language models (LLMs) can rapidly retrieve and integrate literature, medical records, and multimodal clinical data, and may support clinical decision-making. However, most existing medical LLMs are general-purpose, are trained predominantly on published literature and web text rather than authentic clinical records, and have not been validated with real patient data. No disease-specific LLM for CAD currently exists, and no evaluation framework tailored to CAD has been established; existing benchmarks are general-purpose, lack specialty-specific risk grading, are not rigorously anchored to current guidelines, and emphasize literal accuracy over clinically critical reasoning steps and prescription concordance. This prospective, multicenter, observational study will enroll approximately 640 patients with confirmed or suspected CAD at Fuwai Hospital and collaborating centers. No study-specific intervention, examination, biospecimen collection, or additional follow-up visit is performed; all clinical decisions are made independently by the treating physician according to routine standards of care. For consenting participants, routinely generated clinical documentation from the index outpatient visit or hospitalization - including medical history, laboratory results, imaging and coronary angiography/coronary CT angiography reports, physician notes, online consultation records, and follow-up records - will be de-identified and used to construct a high-quality multimodal CAD dataset. This dataset will support the development of a CAD-specific large language model (CorAI), the construction of an evaluation framework spanning eight predefined clinical scenarios and multiple assessment dimensions, and external validation of the model at participating centers. The primary objectives are to characterize the completeness and quality of the constructed prospective cohort dataset, and to quantify the guideline concordance of CorAI-generated clinical recommendations as adjudicated by a blinded panel of cardiologists.

Evaluating AI for Diagnosing and Treating Gummy Smiles: A Pilot Study

Some people have a "gummy smile," where too much gum tissue shows when they smile. Doctors currently diagnose this by hand, using rulers and visual judgment. This study is testing whether a computer program using artificial intelligence (AI) can help identify and measure gummy smiles from photographs, as accurately as expert dentists do it manually. Patients seen at Cairo University's Periodontics and Orthodontics clinics will have photos of their smile taken. Researchers will use these photos to first "teach" the AI program how to recognize and measure a gummy smile. Then, the AI will be tested on a new set of patient photos, and its measurements will be compared to measurements made by expert dentists, to see how closely they match. The goal is to find out whether this AI tool could one day help doctors diagnose gummy smiles more quickly and consistently, which may improve care for patients.

Smartphone vs Manual Interpretation of Biomarkers for Ovulation and Luteal Phase Detection (SMOM Study)

This study will compare different combinations of fertility signs (cervical mucus (CM), luteinizing hormone \[LH\], pregnanediol glucuronide \[PDG\], and basal body temperature \[BBT\]) to determine which are most reliable for identifying ovulation and luteal phase length. Thirty existing Premom App users will track daily observations for three menstrual cycles. Participants will record mucus, perform urine tests, upload test strip photos to the Premom App, and measure BBT. Both participant readings and AI-assisted app readings will be analyzed. The main goal is to find which marker pairings give the most accurate picture of ovulation timing and luteal phase length. Secondary goals include understanding ease of use, the number of tests required, and whether the app improves accuracy.

Validating AI for Gender Prediction Using Morphometric Analysis of the Mandible

This retrospective validity study evaluates the accuracy of artificial intelligence (AI) in determining gender from mandibular morphometric linear measurements. The study utilizes pre-existing Cone Beam Computed Tomography (CBCT) scans of adult Egyptian dental patients. After automatic segmentation of the mandible from these scans, a radiologist will manually perform measurements from certain anatomical points. These measurements will be the reference standard for the AI models. A three-dimensional deep learning model will be developed to perform two tasks: 1. To identify the anatomical points and make the specified linear measurements from these points on the segmented mandibles 2. To accurately predict gender based on these measurements. (Main Objective) The primary objective of this study is to evaluate the accuracy of machine learning algorithms in gender identification from linear morphometric measurements of the mandible. The known gender from patient records will serve as the reference standard. This study will assess the reliability of AI as an objective tool for gender determination for forensic purposes.

Study Comparing Two Image Acquisition Modalities for Second-trimester Pregnancy Screening Ultrasound

The second-trimester morphology ultrasound is a key examination in obstetric monitoring that aims to assess fetal growth, identify any structural abnormalities, and inspect anexes such as placenta, umbilical cord, cervix,... Several studies suggest that a significant proportion of fetal malformations can be detected during this time frame if a complete morphological analysis is performed. However, the reliability of the screening depends on the quality of the equipment, the operator's level of expertise, and adherence to protocols that define the necessary scans. In France, since the first reports of the National Technical Committee on Prenatal Screening Ultrasound (2005), particular attention has been paid to standardizing practices. More recently, the French National Conference on Obstetric and Fetal Ultrasound (CNEOF) published new recommendations (2022, revised in 2023) including the development of reference silhouettes for the second-trimester examination, proposing 26 views (22 required and 4 additional). However, the CNEOF does not formalize quality criteria for evaluating the conformity of these images; this task has been taken over by the French College of Fetal Ultrasound (CFEF), which has established a scoring and validation grid for each fetal slice (see CFEF 2022 document). In parallel, artificial intelligence (AI) is gradually becoming established as a decision support and automation tool in medical imaging, particularly in ultrasound. Deep learning algorithms are capable of identifying anatomical structures, positioning measurement markers, and selecting the most optimal slice, reducing inter-operator variability and streamlining workflow. In the field of obstetric ultrasound, some companies have launched systems capable of detecting or annotating fetal structures in real time, potentially improving diagnostic reliability and reproducibility. Samsung has developed a system called Live View Assist, available on its latest generation ultrasound scanners, which uses AI to automatically recognize and freeze the required fetal slices in real time. The tool also offers automated validation: if the detected slice conforms to the expected standards, it is directly checked off on a checklist. This innovation promises time savings, a reduced risk of missing certain complex slices, and improved standardization. However, there is little data, particularly in France, regarding to the actual performance of this tool in a routine screening context. Before considering the integration of Live View Assist and AI into daily practice, it is therefore essential to evaluate the quality of the images it acquires, the feasibility of a complete examination assisted by AI, as well as the potential impact on examination time and improvement of the workload for sonographers. The aim of this study is to evaluate whether the quality of the 20 mandatory images automatically validated by Live View Assist is not inferior to that of the 20 mandatory images acquired and validated manually by an ultrasound technician, according to the CFEF quality criteria based on the silhouettes recommended by the CNEOF.

AI vs. Anesthesiologists in Preoperative Triage

Accurate preoperative risk stratification is essential for perioperative planning, resource allocation, and patient safety. The American Society of Anesthesiologists Physical Status (ASA-PS) classification remains the most widely used global system for assessing preoperative health status. However, ASA classification relies on clinician judgment and may demonstrate inter-observer variability. Recent advances in artificial intelligence (AI), particularly large language models (LLMs), have shown potential for assisting clinical decision-making by synthesizing structured and unstructured medical information. In perioperative medicine, AI systems may support more standardized risk assessment and laboratory testing strategies. The objective of this observational study is to evaluate the agreement between ASA classifications assigned by anesthesiologists and those generated by a large language model (ChatGPT-5) using anonymized preoperative clinical information. The study will also examine differences in laboratory test recommendations and explore the relationship between clinician- and AI-generated risk assessments and perioperative erythrocyte suspension utilization. Adult patients scheduled for elective surgery who undergo routine preoperative anesthesia assessment will be included. For each patient, the ASA classification assigned by the anesthesiologist will be recorded and compared with the classification generated by the AI system using the same anonymized clinical information. This study aims to assess whether AI-assisted preoperative evaluation may support more consistent risk stratification and potentially contribute to more standardized perioperative resource utilization.

Large Language Models Versus Human Examiners for Grading Physiotherapy Clinical Cases

This study evaluates whether large language models (LLMs) can reliably assess written clinical-reasoning case examinations completed by undergraduate physiotherapy students, compared with faculty assessment. In the course "Specific Methods in Physiotherapy" (third year of the Physiotherapy Degree), students solve complex clinical cases that require clinical reasoning, technical knowledge, and therapeutic decision-making. These cases are traditionally graded by faculty, a time-consuming process that may show inter-rater variability. A set of de-identified student case examinations will be assessed using the rubric currently applied in the course, which covers clarity and structure of clinical reasoning, integration of the biopsychosocial model (ICF and APTA frameworks), accuracy in identifying pain mechanisms, coherence between diagnosis, hypotheses, and treatment, originality and depth of analysis, and professional writing. Each examination will be scored independently by three LLMs (for example, Claude, ChatGPT, and Gemini), each receiving an identical standardized prompt that embeds the same rubric, and by faculty serving as the reference standard. To avoid overloading faculty, full double human grading may not be feasible; the human reference will therefore consist of expert faculty grading by one independent rater or, when resources allow, two independent raters. In contrast, paired assessment is fully implemented across the AI models: each examination is scored by several LLMs, and each model is queried in duplicate, allowing the study to estimate agreement between models and the test-retest stability of each model. The primary aim is to quantify agreement between LLM-generated scores and the faculty reference score. Secondary aims include agreement among the LLMs, test-retest reliability of each model, criterion-level agreement, the quality and usefulness of the qualitative feedback generated, the time and cost associated with each approach, and students' perceptions of the usefulness of human versus AI feedback. The findings will clarify the strengths and limitations of LLMs as supportive tools for formative assessment in health-professions education and will inform criteria for their responsible and effective use. No LLM output will affect students' official grades, which remain the sole responsibility of faculty.

Bispectral Index in Patients Undergoing Vertebral Surgery Using Artificial Intelligence Programs: A Methodological Study

This study aims to interpret the Bispectral Index (BIS) monitoring method, which we routinely use for monitoring in scoliosis surgery, with artificial intelligence (AI) tools and to determine the accuracy and reliability of AI tools in clinical practice by comparing this interpretation with the interpretations of two clinicians experienced in BIS.

Development of a Mobile Terminal-Based Intelligent Detection System for Multiple Anterior Segment Diseases of the Eye

This is a multi-center, cross-sectional study evaluating a smartphone-based artificial intelligence (AI) system for anterior segment eye disease screening. The system is designed to identify 16 clinically important anterior segment conditions from images captured using a standard Android smartphone. A core design feature of the system is that all image analysis is performed entirely on the smartphone itself, without requiring internet connectivity or cloud-based server infrastructure. The study is motivated by a structural challenge in the deployment of medical AI: systems that depend on cloud infrastructure for inference are non-functional in settings without reliable internet access, which disproportionately excludes populations in low-resource regions where the burden of preventable eye disease is highest. This study evaluates whether an on-device AI system, designed with operational constraints as a primary engineering objective, can deliver clinically acceptable diagnostic performance while remaining operable under real-world connectivity limitations. The study comprises five evaluation components. First, the diagnostic performance of the AI system is benchmarked against board-certified ophthalmologists of varying seniority on a standardized set of smartphone-captured anterior segment images. Second, the usability of the system is evaluated among non-medical users who perform self-administered screening with minimal instruction, with per-screening time recorded across consecutive attempts to characterize the learning curve. Third, a head-to-head field trial directly compares the on-device AI system against a functionally equivalent cloud-based deployment of the same model architecture across key operational dimensions including screening duration, diagnostic performance, and user acceptability. Fourth, population-level screening is conducted among consecutively enrolled community residents at two low-resource sites, with per-disease sensitivity and specificity calculated against reference-standard slit-lamp examinations. Fifth, pre-specified health-economic and environmental analyses compare the two deployment modalities in terms of per-person screening cost, cost-effectiveness, per-inference electricity consumption, and projected carbon emissions at scale. The reference standard for all diagnostic comparisons is slit-lamp biomicroscopic examination performed by board-certified ophthalmologists. The study is designed and reported in accordance with the DECIDE-AI reporting guideline for early-stage clinical evaluation of AI-driven decision-support systems.

Smartphone Based Digital Screening for Aortic Valve Stenosis

Heart valve diseases are among the most serious cardiovascular conditions in older age. One of the most common forms is aortic valve stenosis, a narrowing of the valve opening between the left ventricle and the main artery. As the valve becomes tighter, the heart must work harder and harder to pump blood through the body. This process often develops slowly over many years and initially causes no clear symptoms. As a result, the condition is frequently detected only in advanced stages, when warning signs such as shortness of breath, chest pain, or dizziness appear. Without treatment, aortic valve stenosis can become life-threatening. If detected early, however, very effective treatment options are available today. Up to now, the disease has been reliably diagnosed mainly through echocardiography. Yet this method is complex, costly, and requires specialized medical staff. A simple, affordable, and broadly accessible screening option does not yet exist. The interdisciplinary clinical research project explores whether conventional smartphones could fill this gap. Almost all modern devices are equipped with sensors such as microphones, accelerometers, and gyroscopes. These can capture both heart sounds and subtle vibrations of the chest. The research team is investigating whether reliable diagnostic information for the diagnosis of aortic valve stenosis can be extracted from such recordings. To achieve this, the signals are processed with newly developed methods and analyzed using artificial intelligence. For the study, several hundred patients with and without valve disease will be examined. The smartphone results will be compared with established diagnostic standards, particularly echocardiography, to test accuracy and reliability. If successful, the approach could enable a straightforward, digital heart check at home using nothing more than a conventional smartphone. Such a tool would provide an accessible, low-cost, and widely available method for early detection, helping more people receive timely and potentially life-saving treatment.

AID-FOG: Artificial Intelligence-Driven Freezing of Gait Detection in the Home

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease increases the risk of falling. Despite being a common symptom, it is still difficult to evaluate freezing of gait quickly and accurately. Currently, the gold-standard method to determine the severity of FOG is a manual analysis of video footage by an experienced assessor, collected during standardized FOG-provoking walking tests. Because this is a very time-intensive process, where different assessors sometimes obtain different results, our team at KU Leuven have developed an artificial-intelligent (AI) algorithm trained to identify FOG episodes based on wearable inertial measurement unit (IMU) sensor data. The AI algorithm has already undergone initial validation during laboratory testing, yielding promising results. The aim of this study is to investigate whether the AI algorithm can accurately detect FOG episodes in a less controlled environment, namely the home environment. In a second phase, the investigators will also use the collected data to improve the AI algorithm for automated FOG detection in the home. Finally, the investigators want to explore whether the AI algorithm can detect FOG in real-time.

AI-SUPPORTED FLIPPED LEARNING IN BREAST SELF-EXAMINATION TRAINING

The global increase in cancer cases has made breast cancer the second most common cancer after lung cancer and a primary health problem among women. Early diagnosis is the most critical factor in improving survival rates and quality of life in breast cancer. Breast self-examination (BSE), which enables individuals to notice changes in their own breast tissue during the early diagnosis process, is a low-cost and effective awareness method. It is essential that nurses, who play a key role in raising public awareness on this issue, and nursing students, who are the future healthcare professionals, have sufficient knowledge and practical skills in BSE. However, the literature shows that even if students have theoretical knowledge, their application rates are low. In this context, the "AI-Supported Flipped Learning" model, which goes beyond traditional methods and supports active learning, personalized feedback, and digital literacy, has the potential to be an innovative solution in nursing education. Objective: This study aims to evaluate the effect of AI-supported flipped learning model and traditional education on the knowledge levels and performance skills of nursing students regarding BSE knowledge and skills.

Develop and Evaluate An Artificial Intelligence Assisted Prehabilitation Program for Returning to Work and Cost-effectiveness Analysis in Patients With Oral Cancer

The goal of this clinical trial is to develop and evaluate an Artificial Intelligence Assisted Prehabilitation Program (AI APP) for returning to work and cost-effectiveness analysis in patients with oral cancer (OC). The main questions it aims to answer are: * What kinds of needs are related to returning to work (RTW) in patients with OC from diagnosis to survival that we can incorporate into the development of AI APP to assist this population ? * How is the effect of the AI APP that based on findings from the first question for patients with OC on physical and psychological distress, fear of recurrence, self-efficacy in coping with cancer, communication, motor function, quality of life, and RTW? * How is the effect of the RTW AI prediction model to identify high-risk groups ? And how is the comprehensive cost effectiveness of benefits and quality of life of the AI APP for OC population? Researchers will compare patients without using AI APP to see if the AI APP works to assist with coping physical and psychological distress, communication, motor function, quality of life, and RTW issues for individuals with OC? Participants will: * Be asked to fulfill a structural questionnaire, or engage in a semi-structured one-by-one interview or a focus group to assess their physical, psychological, and social support needs in the first stage. * Be invited to participant the pilot testing of AI APP in the second stage. * Be provided and trained by 3-month AI APP for 3 months or cared as usual in the third stage. * Complete a structural questionnaire and follow up one year, including the baseline (before using the AI app) and at 1-2 weeks, 3 months, 6 months, 9 months, and 12 months after the baseline. * Engage in one-by-one interview or a focus group to assess user experiences of the AI APP.

Qatar Cardiometabolic Retrospective Cohort-Analysis Using Artificial Intelligence

Cardiovascular disease is the leading cause of death worldwide, and individuals with diabetes or other cardiometabolic conditions are at increased risk of adverse cardiovascular outcomes. Although advances in prevention and treatment have reduced cardiovascular events globally, cardiometabolic disease continues to represent a significant health burden, particularly in regions with high diabetes prevalence. In Qatar and other Gulf Cooperation Council countries, the prevalence of diabetes and obesity is increasing, contributing to a high proportion of participants presenting with acute coronary syndrome who have type 2 diabetes or prediabetes. This observational study will use electronic medical record data from patients hospitalized at the Heart Hospital with acute coronary syndrome and a concomitant diagnosis of diabetes or prediabetes. The study will assess trends in cardiovascular risk factors and cardiovascular events, including readmission and mortality. An artificial intelligence component will be used to develop and validate machine learning based risk prediction models to forecast adverse cardiovascular outcomes in participants with cardiometabolic disease. These models will integrate clinical, biochemical, imaging, and other non-invasive data routinely collected during participants care to identify predictors of cardiovascular events.

Deep Learning Framework for Continuous Depth of Anesthesia Forecasting

The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive. Deep learning architectures, specifically Long Short-Term Memory (LSTM) networks, excel at processing complex, time-series data to forecast future clinical states. While standard PK/PD models (such as the state of the art Eleveld model for Propofol and Remifentanil) estimate target-site drug concentrations (Ce), they do not account for real-time, patient-specific dynamic responses. This study aims to deploy an AI framework designed to predict future physiological states.