Clinical Research Directory

Artificial Intelligence for Automated Clinical Data Exploration From Electronic Medical Records (CardioMining-AI)

The purpose of this study is to highlight the usefulness of artificial intelligence and machine learning to develop computer algorithms that will achieve with great reliability, speed and accuracy the automatic extraction and processing of large volumes of raw and unstructured clinical data from electronic medical files.

Intelligent Classification of Intraoperative Hemodynamic Phenotypes Based on CADFlow Doppler Monitoring

The goal of this observational study is to learn about intraoperative hemodynamic phenotypes in adult patients undergoing elective surgery under general anesthesia, using a new wearable ultrasound patch (CADFlow) that continuously monitors carotid and jugular blood flow. The main question it aims to answer is: Can a machine learning model built solely on non-invasive carotid and jugular Doppler data accurately identify and classify a patient's hemodynamic phenotype in real time during surgery? Participants undergoing routine elective surgery who are monitored with the CADFlow device as part of standard care will be recorded continuously from after tracheal intubation until the end of surgery. The collected signals will be used to discover distinct hemodynamic phenotypes and to train a model that classifies each patient's phenotype in real time.

Remote Monitoring of Asthma in Children and Young People

The objective of this study is to determine whether healthcare data and remotely collected patient data can accurately predict asthma attacks in children and young people aged 5-17 years. The main outcome is: when using this new system, is there a reduction in asthma attacks compared with a historic average. The whole population of children and young people with asthma will have routine healthcare data monitored, with a subset of people with high risk asthma asked to participate in a more detail study involving remotely monitored data.

Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation

Purpose: The primary objective of this study is to assess the diagnostic accuracy of an AI-enabled ultrasound tool for estimating fetal weight Participants: 1,000 pregnant individuals Procedures (methods): This prospective diagnostic accuracy study will enroll 1,000 pregnant individuals within one week of anticipated delivery. At a single visit, each participant will undergo two ultrasound assessments: (1) standardized sweeps for AI analysis (performed by both specialist and nonspecialist users), (2) specialist-performed fetal biometry.

Cardiovascular Complications in Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation.

Allogeneic hematopoietic stem cell transplantation (HSCT) represents a major therapeutic strategy for malignant hematologic diseases, with the number of procedures steadily increasing in France each year. Conditioning and maintenance regimens carry a risk of both short- and long-term cardiotoxicity, leading to serious cardiovascular events including acute coronary syndrome (ACS), cardiac dysfunction, arrhythmias, pulmonary hypertension, and pericardial effusion. The pathophysiology of cardiotoxicity in HSCT patients remains poorly understood. It is therefore crucial to investigate underlying mechanisms and identify predictive factors of cardiotoxicity in order to provide appropriate cardiological follow-up and management. Current European Society of Cardiology guidelines recommend routine monitoring of HSCT patients with echocardiography and cardiac biomarkers (NT-proBNP, troponin), although these recommendations are based on small-scale studies. The cardiodepressor factor DPP3 has shown promising results in cardio-oncology, with a causal role in anthracycline-induced cardiac dysfunction. Its role in HSCT-related cardiotoxicity requires further evaluation. This multicenter study of HSCT recipients will be a valuable resource, enabling a better understanding of the pathophysiology of cardiotoxicity and prognosis. It will highlight imaging (echocardiography, calcium score, supra-aortic Doppler), electrocardiographic, and biological markers (including DPP3) associated with prognosis.

Timely Ordering of Pharmacogenetic Testing

The goal of this trial is to learn if a machine learning (ML) model can help optimize drug therapy in the pediatric population. The main question\[s\] it aims to answer are whether a machine learning model predicting receipt of a targeted medication within the next three months: * Increases the offering of pharmacogenetic testing prior to receipt of a targeted medication * Increases the number of patients with pharmacogenetic results prior to receipt of a targeted medication * Increases the number of patients who have alteration in medication choice or dose based on pharmacogenetic results This trial only focuses on the prediction and provision of participants with a high-risk of receiving a medication with a pharmacogenetic indication in the next three months.

PACT Involvement in Cardiology Patients

The goal of this trial is to determine the effectiveness of a machine-learning (ML) model predicting a serious cardiac event within the next three months, when compared pre- versus post-deployment, in pediatric cardiac inpatients. The main questions it aims to answer are whether deployment of the ML model: 1. Increases PACT consultation within the next three months among admissions without PACT involvement in the previous 100 days 2. Increases PACT consultation or visit within the next three months among those who experience a serious cardiac event during this period 3. Decreases time to PACT consultation or visit among those seen by PACT during this period 4. Decreases the incidence of death in the intensive care unit (ICU) 5. Increases documentation of goals of care High-risk cardiology patients will be identified by an ML model each morning. If the patient has been seen by the PACT team within the past year, the update will go to the PACT team members. If the patient hasn't been seen by the PACT team, the email will be sent to the cardiology physician in charge of the patient. This physician will decide whether a PACT consultation is necessary based on their clinical judgment. If so, a referral will be made using the usual process. Outcomes of the identified patients will be compared pre- and post-deployment.

Federated Learning for Point-of-Care Cardiac Ultrasound

This prospective, multicenter study will evaluate a federated machine-learning system designed to analyze focused cardiac point-of-care ultrasound examinations. Federated learning allows participating clinical sites to contribute to model development while keeping raw ultrasound images and directly identifiable patient information within each site's controlled computing environment. Encrypted model updates, rather than patient images, will be transmitted for secure aggregation. The prospective validation cohort will include approximately 3,000 adults undergoing clinically indicated focused cardiac ultrasound. Model performance will be compared with an expert interpretation of a comprehensive transthoracic echocardiogram performed within 24 hours. The primary objective is to determine how accurately the model identifies reduced left ventricular systolic function, defined as a left ventricular ejection fraction below 40%. During the initial validation period, the investigational software will operate in silent mode. Its results will not be displayed to treating clinicians and will not be used to diagnose participants, select treatment, or replace standard clinical interpretation. The study will also evaluate image-quality classification, cardiac-view recognition, performance across clinical sites and ultrasound systems, model calibration, processing time, cybersecurity, privacy resilience, and performance across demographic and clinical subgroups. Long-term monitoring will assess whether model performance changes as clinical populations, ultrasound equipment, acquisition practices, and software environments evolve during the 2026-2037 study period.

Machine Vision Based MDS-UPDRS III Machine Rating

The Movement Disorders Society (MDS) Unified Parkinson's Disease Rating Scale (UPDRS) Part III (MDS-UPDRS III) is the primary assessment method for motor symptoms in Parkinson's disease patients. Currently, movement disorder specialists conduct semi-quantitative scoring, which entails limitations such as subjectivity, weak sensitivity, and a limited number of professional physicians. This study, based on machine vision, establishes gold standard labels according to expert scoring. By using machine learning, we develop a machine rating model and compare the model's performance with gold standard rating and general clinical rating to investigate the accuracy of machine vision-based MDS-UPDRS III machine rating.

Machine Learning Prediction of Mortality After Prone Positioning in ARDS

Acute respiratory distress syndrome (ARDS) is a life-threatening condition with high mortality. Prone position ventilation (PPV) is an evidence-based therapy that improves oxygenation and survival in patients with moderate to severe ARDS; however, outcomes remain heterogeneous. Early identification of patients at high risk of mortality after PPV may improve clinical decision-making and individualized management. This retrospective observational study developed and validated a machine learning model to predict intensive care unit (ICU) mortality in patients with ARDS receiving prone position ventilation. Clinical, laboratory, and treatment variables obtained from ICU electronic medical records were used to construct prediction models using multiple machine learning algorithms. The performance of these models was evaluated and compared to identify the optimal model for mortality prediction.

Comparison of NEWS2 and Machine Learning for Early Sepsis Warning

This prospective observational study aims to objectively measure the lead-time (the time from the first KDS alert to sepsis diagnosis) of the NEWS2-based clinical decision support system (KDS) and compare its early warning performance with a machine learning model trained on 2000 patients and externally validated. The study seeks to answer the following main questions: How early does the NEWS2-based KDS provide an alert before sepsis diagnosis? Does a machine learning model, developed using logistic regression and externally validated in a prospective cohort, offer superior specificity and comparable sensitivity to KDS? Participants who are already receiving routine clinical care at Kocaeli City Hospital will have their vital signs and laboratory data monitored as part of standard practice. NEWS2 scores will be calculated automatically and the time of the first alert (T0) will be recorded. Sepsis diagnosis will be confirmed by an increase in SOFA score ≥ 2 (T1), evaluated by two independent and blinded physicians. Lead-time will be calculated as the difference between T1 (hours×60) and T0 (minutes). The machine learning model will be tested prospectively on this cohort, and its performance will be compared with KDS using sensitivity, specificity, F1 score, ROC-AUC, and accuracy.

Machine Learning for Predicting Spinal Anesthesia Duration

Spinal anesthesia provides significant advantages over general anesthesia in knee arthroplasty, including reduced blood loss, faster recovery, and fewer complications. However, predicting its duration is critical for patient safety and effective postoperative management. This study evaluates the usability of machine learning (ML) algorithms to predict the termination time of spinal anesthesia and the patient's readiness for mobilization. Using demographic, surgical, and anesthetic variables, ML models were trained to estimate anesthesia duration. Accurate predictions may improve intraoperative planning, optimize postoperative care, and enhance patient outcomes. Integrating ML-based predictive systems into anesthesia practice can contribute to safer, more efficient, and personalized perioperative management.

ML-Based ABPA Recurrence Prediction and Clinical Utility

This multicenter bidirectional cohort study aims to develop and externally validate a machine learning model for predicting the risk of acute exacerbation within 1 year in patients with allergic bronchopulmonary aspergillosis (ABPA) during the stable phase, and further to evaluate the model's practical value in risk stratification and clinical decision-making. All patients diagnosed with ABPA according to the ISHAM 2024 criteria will be assigned to either the acute exacerbation group or the non-exacerbation group based on whether they experience an acute exacerbation within 1 year. Enrolled participants will be randomly divided into a training set and an internal validation set. During the feature selection phase, univariate analysis, collinearity diagnostics, feature importance ranking derived from nine machine learning algorithms, and expert consensus are comprehensively applied, ultimately leading to the development of 12 independent machine learning models. Model performance is assessed using the receiver operating characteristic (ROC) curve and its area under the curve (AUC), sensitivity, specificity, F1-score, calibration curve, and decision curve analysis. In addition, external validation further enhances the credibility of the model. To improve clinical interpretability, the SHAP method is employed to quantify the contribution of each feature, and an interactive nomogram is constructed to facilitate clinical application. All participants will be followed up for 12 months, during which regular clinical and laboratory evaluations will be performed.

Predicting Vaccine Hesitancy Using Machine Learning

In recent years, emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Virtual Reality (VR) have rapidly become integrated into daily life. The widespread use of these applications has led to the accumulation of vast amounts of data, giving rise to what is commonly referred to as "Big Data." Due to the sheer volume, manual processing and analysis of these large datasets are not feasible. Therefore, software tools and libraries-such as Python and R libraries-have been developed to perform these analyses efficiently and to generate predictions for the future by leveraging historical data through Machine Learning (ML) algorithms. The primary goal of machine learning algorithms is to discover patterns within existing data and use these patterns to make accurate predictions on new data. The use of machine learning in the field of healthcare has gained significant momentum in recent years. However, a review of the literature reveals that research specifically addressing childhood vaccine hesitancy remains limited. This study aims to identify the factors contributing to vaccine hesitancy among parents of children aged 0-48 months and to develop a predictive model using machine learning techniques based on these factors. Such a model could help anticipate the likelihood of vaccine refusal among parents and thereby support the development of targeted public health strategies for at-risk populations.

Estimation and Prediction of Individual Benefit From Postoperative Adjuvant Transcatheter Arterial Chemoembolization For Patients With Hepatocellular Carcinoma

Adjuvant transarterial chemoembolization (TACE) is widely adopted in China for resectable hepatocellular carcinoma (HCC), yet its efficacy remains inconsistent. We aimed to identify factors influencing individual patient benefit using causal machine learning. To this end, we retrospectively collected HCC patients with high risk factors for tumor recurrence from four centers of China, divided into the discovery cohort and the validation cohort . The primary endpoint was disease-free survival (DFS). The primary endpoint was overall survival (OS).Individual treatment effects (ITEs) were estimated within a causal machine learning framework. An ITE \< 0 was considered recommendation for adjuvant TACE , while ITE ≥ 0 indicated active surveillance. The model would be validated in the validation cohort. The contribution of each variable to ITE was assessed using the Shapley Additive Explanations (SHAP). An online calculator would be developed for future use by public.

A Machine Learning Approach for Predicting tDCS Treatment Outcomes of Adolescents With Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by disturbances in communication, poor social skills, and aberrant behaviors. Particularly detrimental are the presence of restricted and repetitive stereotyped behaviors and uncontrollable temper outbursts over trivial changes in the environment, which often cause emotional stress for the children, their families, schools and neighborhood communities. Fundamental to these cognitive and behavioral problems is the disordered cortical connectivity and resultant executive dysfunction that underpin the use of effective strategies to integrate information across contexts. Brain connectivity problems affect the rate at which information travels across the brain. Slow processing speed relates to a reduced capacity of executive function to recall and formulate thoughts and actions automatically, with the result that autistic children with poor processing speed have great difficulty learning or perceiving relationships across multiple experiences. In consequence, these children compensate for the impaired ability to integrate information from the environment by memorizing visual details or individual rules from each situation. This explains why children with autism tend to follow routines in precise detail and show great distress over seemingly trivial changes in the environment. To date, there is no known cure for ASD, and the disorder remains a highly disabling condition. Recently, a non-invasive brain stimulation technique, transcranial direct current Stimulation (tDCS) has shown great promise as a potentially effective and costeffective tool for reducing core symptoms such as anxiety, aggression, impulsivity, and inattention in patients with autism. This technique has been shown to modify behavior by inducing changes in cortical excitability and enhancing connectivity between the targeted brain areas. However, not all ASD patients respond to this intervention the same way and predicting the behavioral impact of tDCS in patients with ASD remains a clinical challenge. This proposed study thus aims to address these challenges by determining whether resting-state EEG and clinical data at baseline can be used to differentiate responders from non-responders to tDCS treatment. Findings from the study will provide new guidance for designing intervention programs for individuals with ASD.

Morphology in Oral Rare Syndromes & Artificial Intelligence for Clinical Diagnosis

MOSAIC aims to determine whether oro-dental morphological anomalies, particularly palatal morphology, associated with rare bone and cartilage diseases can be precisely characterized using 3D digital models analysed through geometric morphometrics. The study will also evaluate whether these morphological signatures can train an artificial intelligence (AI) algorithm to classify syndromes. A prospective monocentric case-control cohort will be constituted, including 3D intra-oral scans and associated clinical data. The final goal is to improve diagnostic accuracy and reduce diagnostic delay in rare bone disorders.

Prospective Evaluation of a Locked Risk-Stratified Surveillance Strategy for Extrahepatic Metastasis in Hepatocellular Carcinoma

This is a multicenter prospective observational cohort study in adults with hepatocellular carcinoma (HCC) without baseline extrahepatic metastasis who are receiving routine clinical management and follow-up at participating centers. The study evaluates the real-world implementation and clinical utility of a locked machine learning-guided risk stratification strategy for predicting lung and bone metastasis and supporting risk-stratified surveillance. The locked strategy uses prespecified 12-month risk thresholds and linked care pathways and is implemented without retraining or threshold revision. No study-mandated treatment or surveillance assignment is performed; clinical care remains at physician discretion according to local practice. The study assesses model transportability and calibration, implementation outcomes (including completion of prespecified actions, timeliness of action, and treatment activation), clinically actionable detection outcomes, patient-important outcomes, and longer-term survival. Comparative analyses use centre- and calendar-epoch-aligned usual-care episodes as the primary observational comparator.

Multi-Omics Inflammatory Phenotype for ABPA Recurrence Risk Prediction

To develop and externally validate a machine learning model for predicting the 1-year risk of relapse in patients with stable ABPA, and to further evaluate its value in risk stratification and clinical decision-making.

Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery

The goal of this observational study is to develop and pre-validate a machine learning algorithm to predict early recovery of mobility in patients undergoing hip or knee joint replacement surgery. The primary research question is: Can a machine learning model accurately classify patients with faster versus slower recovery of autonomous mobility in the first days after joint replacement surgery? Patients who have undergone elective hip or knee arthroplasty and received post-operative physiotherapy will have their clinical and perioperative data collected retrospectively (2020-2023) and prospectively (March 2026-December 2027). The algorithm will be trained on retrospective data and tested prospectively to evaluate its predictive performance for early mobilization and length of hospital stay.

Digital diagnoSis of Cardiac sOUNd in peDiatric Patients [DI-SOUND Study]

Neonatal screening procedures for potentially life-threatening congenital cardiovascular diseases (i.e., duct-dependent systemic or pulmonary circulation), currently implemented at the national level, rely primarily on cardiovascular physical examination performed by a neonatologist. More recently, this approach has been complemented by the assessment of hemoglobin oxygen saturation at both the upper and lower extremities (pre- and post-ductal saturation) in order to improve diagnostic sensitivity, although this practice has not yet been uniformly adopted nationwide. Converging evidence indicates that these screening strategies are affected by significant limitations in both sensitivity (failure to identify affected individuals) and specificity (false-positive findings in healthy subjects). These limitations are associated with substantial overall costs for the healthcare system. Failure to correctly identify affected neonates may result in increased morbidity and mortality, whereas overdiagnosis leads to unnecessary second-level diagnostic investigations and imposes a considerable psychological burden on families, who remain understandably anxious until diagnostic confirmation is achieved. The aim of the present research project (proof-of-concept study) is to develop a digital classifier capable to categorize heart sounds with commercially available digital stethoscopes into a binary classification system distinguishing physiological from pathological sounds. The derivation phase will be followed by a prospective validation phase, in which the classifier will be applied to assess its diagnostic performance. This phase will also evaluate the economic impact of the digital screening approach compared with standard practice. During the derivation phase, neonates with known cardiovascular status, as determined by prior echocardiographic assessment (including both healthy subjects and those with congenital heart disease), will be enrolled. Heart sounds will be recorded in a quiet environment under standard clinical conditions, without sedation. Digital recordings will be stored in WAV format and analyzed to develop a binary classification algorithm capable of distinguishing healthy from pathological cases. Following development, the classifier will be prospectively applied to a validation cohort of neonates undergoing conventional cardiovascular screening (clinical examination and pre- and post-ductal pulse oximetry), followed by classification using the digital tool under investigation. All participants will subsequently undergo confirmatory echocardiography. Diagnostic performance metrics, including sensitivity, specificity, positive and negative predictive values, and likelihood ratios, will be calculated for both the digital and conventional screening modalities. Furthermore, the number of missed pathological cases and the number of unnecessary second-level investigations resulting from false-positive findings will be used to define the economic benefit profile of the proposed screening strategy. Monte Carlo simulation techniques will be employed to extrapolate these findings at the national level, using ISTAT data on birth rates and disease prevalence. It is anticipated that the development of a digital classifier for the binary classification of neonatal heart sounds will be feasible. Moreover, it is expected that this tool will demonstrate superior diagnostic performance compared with current neonatal screening strategies, with beneficial implications not only for the accurate identification of affected and healthy neonates but also for reducing overall healthcare costs associated with missed diagnoses and inappropriate overdiagnosis.

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.

Construction of a Clinico-Imaging Collaborative Diagnostic Model for Dermatomyositis Combined With Interstitial Lung Disease Based on PET/CT Imaging Features and Clinical Parameters

The investigators investigated the associations between the imaging parameters of ⁶⁸Ga-FAPI and ¹⁸F-FDG dual-tracer PET/CT and concomitant interstitial lung disease (ILD) in patients with dermatomyositis (DM), developed a novel diagnostic model to predict DM complicated with ILD, and conducted external validation of this model. Meanwhile, the investigators compared the predictive performance of the imaging-only model with that of the classic clinical model and the clinical-radiological collaborative model.

Predictive Value of Gastrointestinal Blood Flow for Enteral Nutrition Intolerance in Critically Ill Patients

This study aims to explore the correlation between gastrointestinal blood flow and the incidence of enteral nutrition intolerance (ENI) and its symptoms in critically ill patients, construct and compare predictive models including blood flow parameters, and evaluate their incremental predictive value.