moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Development of an Artificial Intelligence Algorithm to Detect Pathological Repolarization Disorders on the ECG and the Risk of Ventricular Arrhythmias
Trial ID: NCT05829993
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-11-28
Completion date: 2027-06-28
Sponsor: Assistance Publique - Hôpitaux de Paris
Collaborators: UMMISCO - Institute of Research for Development (IRD)
Conditions: Cardiac Disease; Ventricular Arrythmia
Locations: Centre d'Investigation Clinique Paris-Est/Hôpital Pitié-Salpêtrière
Countries: France
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
Torsades de Pointes (TdP) are potentially fatal ventricular arrhythmias that are promoted by prolonged ventricular repolarization (Long QT, LQT). The different forms of LQT result from inhibition of cardiac potassium currents (IKr and IKs) or activation of a late sodium current (INaL). These alterations may be either congenital (3 types: cLQT-1: IKs, cLQT-2: IKr, cLQT-3: INaL) or drug-induced (diLQT, via inhibition of IKr).

More than 100 medications have received marketing authorization despite a known risk of TdP, due to a favorable benefit-risk ratio (e.g., hydroxychloroquine).

QTc, which represents the duration of ventricular repolarization (in milliseconds) - defined as the time from the beginning of the QRS complex to the end of the T wave, corrected for heart rate - is prolonged in all forms of LQT.

Specific T-wave abnormalities, depending on the altered ion currents, have been described and can help differentiate the various types of congenital or drug-induced LQT.

However, screening for LQT and TdP risk, both at the individual and population levels, currently relies mainly on isolated QTc evaluation and genetic testing, which often takes considerable time to return.

Thus, limiting ECG analysis to QTc measurement alone offers low predictive value, as the ECG contains a wealth of additional information beyond a single interval.

The investigator recently demonstrated that artificial intelligence (AI)-based ECG analysis using deep-learning convolutional neural networks can detect more discriminative features of the ECG for predicting the type of LQT and the risk of TdP, going beyond QTc alone.

Using these techniques, the investigator developed a model with probabilistic modules capable of: predicting TdP risk, identifying LQT subtypes (scores ranging from 0 to 100%), and quantitatively measuring ECG parameters such as QTc, heart rate, PR, and QRS duration.

The objective of this project is to prospectively validate our model in real-world conditions across various departments within AP-HP, for:

Automatic measurement of QTc, and Identification and classification of LQT types and TdP risk.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18
* Patients or subjects taken care in recruiting centres for which an ECG is indicated
* No opposition to participation in the study

Exclusion Criteria:

* Medical contraindication for ECG
* Subjects with pacemaker-driven QRS

Outcomes:
Concordance of QTc measurement between the reference method and deep-learning model at 500 ms threshold
LUCE: LUng Cancer (r)Evolution
Trial ID: NCT07517393
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04
Completion date: 2036-03
Sponsor: Casa di Cura Dott. Pederzoli
Conditions: Thoracic Neoplasm; Pulmonary Nodules; Non-oncological Pulmonary Pathologies
Locations: Ospedale P. Pederzoli Casa di Cura Privata S.p.A.
Countries: Italy
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
This research study aims to retrospectively and prospectively analyze the clinical, anamnestic, functional, biological data of patients who have performed or will perform non-contrast chest photon count CT.

The use of contrast medium is evaluated by the physician. Main objective The study aims to understand how many people who undergo non-contrast photon-counting chest CT have: lung nodules, chest tumors, and other non-cancerous lung diseases.

Secondary objectives

* Analyze the data with the help of artificial intelligence to create a "digital twin" of the lung, that is, a virtual copy of the lung that allows you to study and simulate the behavior of the lung for research and analysis purposes, without directly intervening on the patient.
* Follow any suspicious lung changes over time to understand how they change and whether they are linked to clinical, biological, or laboratory parameters.

The enrollment phase will last 12 months and will begin with the approval of the study. At this stage, clinical data already present in the patient's medical record and data relating to the photon count CT examination will be collected. After the enrollment phase, the patient will be observed for a further total duration of 10 years: in particular, a follow-up visit will be arranged at two, five and ten years. More specifically, if there are no radiological elements that require scheduling a visit to the center, the patient will be contacted by telephone for a telematic update of the data. If, however, pulmonary alterations are identified during the study, the patient will be referred to the Reference Operating Unit, as per clinical practice, to continue with the most appropriate personalized diagnostic-therapeutic process. In these cases, the patient will be contacted by telephone and an appointment with the institution will be suggested.

Eligibility criteria:
Inclusion Criteria:

* Men or women aged ≥ 18 years who will perform a smdc photon count chest CT scan
* written informed consent.

Exclusion Criteria:

* Lack of information regarding anamnestic data
* any reason why the investigator deems the patient unenrollable in the practice.

Outcomes:
Describe the frequency of patients performing chest photon counting CT without mdc with pulmonary nodules; Describe the frequency of thoracic malignancies in subjects performing chest photon counting CT without mdc.; Describe the frequency of non-oncological pulmonary diseases of subjects performing chest photon counting CT without mdc.
Validation and Development of Digital Anthropometry Methodologies.
Trial ID: NCT07003516
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-11
Completion date: 2025-12
Sponsor: Dynamical Business and Science Society - DBSS International SAS
Collaborators: CES University; Universidad de Córdoba; ARTHROS Centro de Fisioterapia y Ejercicio
Conditions: Body Composition
Locations: Universidad CES; Fundación Universitaria del Área Andina; Fundación Universitaria del Área Andina
Countries: Colombia; Colombia; Colombia
Enrollment: 130
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
Anthropometric measurements are commonly employed to evaluate body composition, morphology, and health-related parameters across diverse populations. While a cost-effective and field-friendly method, the COVID-19 pandemic has spurred research on digital anthropometry worldwide. Machine learning, a fusion of artificial intelligence and data mining, holds promise for enhancing data collection and analysis in Kinanthropometry applications. Rather than replacing traditional methods, digital anthropometry presents a significant opportunity to enhance accuracy, validity, practicality, and the implementation of self-monitoring procedures under professional guidance. The CyberMetron Project by DBSS aims to perform additional research and increase scientific literacy among practitioners for public awareness of digital anthropometry.

Eligibility criteria:
Inclusion Criteria:

* People over 18 years of age (under 60 years of age)
* Persons born and living in the cities of the participating universities
* Signed informed consent to undergo evaluation of anthropometric measurements.

Exclusion Criteria:

* Body mass ≥ 160 kg
* Amputations
* Pregnant women.
* People with implants or prostheses.

Outcomes:
Anthropometric measurements; Body composition and morphology by digital anthropometry
A Prospective, Randomized, Single-blind, 2 × 2 Factorial Design Single Center Study Evaluating the Polyp Detection and Quality Monitoring Function of EndoAngel in Improving the Quality of Colonoscopy
Trial ID: NCT04453956
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-07-01
Completion date: 2020-10-30
Sponsor: Renmin Hospital of Wuhan University
Conditions: Adenoma
Interventions: Colonoscopy with the assistance of EndoAngel's polyp detection function.; Colonoscopy with the assistance of EndoAngel's quality monitoring function.; Colonoscopy without the assistance of EndoAngel's any function.
Locations: Renmin Hospital of Wuhan University
Countries: China
Enrollment: 1090
Age range: 18 Years - None
Sex: ALL

Summary:
An artificial intelligence-based system, named EndoAngel,has been constructed previously,which has polyp detection and quality monitoring functions. This study aims to evaluate the two different functions of EndoAngle in improving adenoma detection rate.

Eligibility criteria:
Inclusion Criteria:

1. Male or female aged 18 years or above;
2. Colonoscopy is needed to further characterize gastrointestinal diseases;
3. Ability to read, understand and sign informed consent forms;
4. The researchers believe that the subjects can understand the process of the clinical study and are willing and able to complete all the study procedures and follow-up visits to cooperate with the study procedures.

Exclusion Criteria:

1. Participated in other clinical trials, signed informed consent forms and followed up in other clinical trials；
2. Participate in a drug clinical trial and during the elution period of the trial or control drug；
3. Drug or alcohol abuse or mental disorder in the last 5 years;
4. Pregnant or lactating women;
5. Patients with multiple polyp syndrome;
6. Patients with a history of inflammatory bowel disease, colorectal cancer, or colorectal surgery;
7. Patients with known perforation or colonic obstruction;
8. Patients with a contraindication for biopsy;
9. The researchers did not consider the subjects suitable for colonoscopy;
10. The researchers determined that the subjects had high-risk diseases or other special conditions that were not appropriate for clinical trials.

Outcomes:
Adenoma detection rate
Effects of an AI-driven Personalized Exercise Feedback Program on Exercise Adherence and Health Outcomes in Patients with Traumatic Brain Injury
Trial ID: NCT06817564
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-01
Completion date: 2031-08-31
Sponsor: National Defense Medical Center, Taiwan
Conditions: Traumatic Brain Injury; Exercise; AI (Artificial Intelligence); Digital Health
Interventions: AI-PEF; Theory-based digital exercise; Active Control
Enrollment: 125
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to develop and evaluate an AI-driven Personalized Exercise Feedback Program (AI-PEF) to enhance exercise adherence and health outcomes in mTBI patients.

Methods: AI-PEF integrates the transtheoretical model and self-determination theory with machine learning algorithms to provide real-time, personalized feedback. A phased randomized controlled trial will be conducted: Phase I evaluates feasibility and acceptability through Delphi methods with expert consensus and patient feedback; Phase II validates preliminary outcomes with 30 participants in a 2-arm randomized trial; and Phase III assesses the program's impact on adherence, sleep quality, depressive symptoms, and quality of life with 90 participants in a 3-arm randomized trial.

Eligibility criteria:
Inclusion Criteria:

* Eligible participants are patients aged over 18 with mild TBI (GCS 13-15)
* who can walk independently,
* reside in the Greater Taipei area,
* and possess sufficient Chinese or Taiwanese language proficiency to understand the trial
* complete self-administered questionnaires.

Exclusion Criteria:

* Exclusion criteria include individuals with severe medical conditions (e.g., respiratory failure, epilepsy, psychiatric disorders), musculoskeletal or neurological impairments
* hindering physical activity in the 6-minute walk test,
* cognitive impairments (MMSE \< 24),
* frontal lobe injuries or penetrating injury causing significant psychological dysfunction.
* Patients regularly engaging in moderate-to-high-intensity aerobic exercise or participating in other studies will also be excluded to avoid bias.

Outcomes:
Adherence; Adherence; Adherence; Adherence; Adherence
Asia Pediatric Intensive Care Epidemiology and Outcomes Study
Trial ID: NCT06481644
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-08-01
Completion date: 2027-12-31
Sponsor: KK Women's and Children's Hospital
Collaborators: King Chulalongkorn Memorial Hospital; Tengku Ampuan Rahimah Hospital; King Abdullah Specialized Children's Hospital; Children's Hospital 2; University Malaysia Medical Centre; Saiful Anwar Hospital; Murni Teguh Memorial Hospital; Siriraj Hospital; Harapan Kita Women and Children Hospital; Vicente Sotto Memorial Medical Center; Hong Kong Children's Hospital; Ramathibodi Hospital; Aga Khan University; Aichi Children's Health and Medical Center, Japan; Hospital Sultanah Aminah Johor Bahru; Children's Hospital of Chongqing Medical University; Prof IGNG Ngoerah Hospital; Penang Hospital, Malaysia; Vietnam National Children's Hospital; Hospital Pakar Kanak-kanak UKM; National University Hospital, Singapore; Queen Elizabeth Hospital, Hong Kong; Shanghai Children's Medical Center
Conditions: Pediatrics; Intensive Care Unit; Critical Illness
Locations: K K Women's and Children's Hospital; King Chulalongkorn Memorial Hospital
Countries: Singapore; Thailand
Enrollment: 10000
Age range: None - None
Sex: ALL

Summary:
The overall objective of this study is to improve the standard of care of critically ill pediatric patients. The specific aims are to describe the clinical profile and outcomes of all admissions to Asian pediatric ICUs, determine the risk factors associated with poor outcomes, determine quality indicators for benchmarking ICU performance across sites and develop and train artificial intelligence algorithms to predict mortality, length of ICU stay and resource utilization.

Eligibility criteria:
Inclusion Criteria:

* All patients admitted to the pediatric ICUs of participant hospitals

Exclusion Criteria:

* No consent

Outcomes:
Mortality
AI-Powered Scoliosis Auto-Analysis System Multicenter Development and Validations
Trial ID: NCT05146193
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-05-01
Completion date: 2030-02-01
Sponsor: The University of Hong Kong
Collaborators: Peking Union Medical College; Peking University Third Hospital; Beijing Chao Yang Hospital; University of Malaya; Nara Medical University; Hamamatsu University; Ruijin Hospital; Huashan Hospital; Zhejiang University; Ji Shui Tan Hospital
Conditions: Spinal Deformity
Interventions: Nude back photo
Locations: Duchess of Kent Children's Hospital
Countries: Hong Kong
Enrollment: 2500
Age range: 10 Years - 80 Years
Sex: ALL

Summary:
The investigators aim to use artificial intelligence (AI) to help clinicians in diagnosing and assessing spinal deformities.

Eligibility criteria:
Inclusion Criteria:

* Idiopathic scoliosis, adult deformity (spondylolisthesis, idiopathic kyphosis, kyphoscoliosis, lordoscoliosis)

Exclusion Criteria:

* Refusal for imaging, postoperative patients

Outcomes:
Cobb angle
A Clinical Research in Using Artificial Intelligence (AI) to Design Dental Crown
Trial ID: NCT07562607
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-09-05
Completion date: 2027-01-31
Sponsor: The University of Hong Kong
Conditions: Tooth Defect
Interventions: Conventional CAD/CAM Dental Crowns; AI-Designed Dental Crowns
Locations: Faculty of Dentisry, the University of Hong Kong
Countries: Hong Kong
Enrollment: 40
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
This clinical research validates a fully automatic AI algorithm for dental crown design using GANs trained on University of Hong Kong 3D prosthesis data and AI-powered FEA for stress correction, overcoming CAD/CAM limitations like manual technician time and occlusal errors. In-vitro fatigue tests confirmed performance comparable to conventional crowns. Clinically, AI-designed crowns are compared to technician CAD/CAM controls using 10 FDI criteria (aesthetic/functional/biological), assessed via oral exams, and IOS (wear), to prove feasibility and optimize the algorithm.

Eligibility criteria:
Inclusion Criteria:

* Both male and female patients (aged 18-60 years old) attending the HKU Faculty of Dentistry teaching clinic in Prince Philip Dental Hospital (PPDH), who are in need of single crown restorative treatment in the posterior region.

Exclusion Criteria:

* female patients with pregnancy; patients with any systemic diseases (e.g., uncontrolled diabetes, uncontrolled hypertension, uncontrolled osteoporosis, etc); patients with history of local irradiation therapy; patients with untreated periodontal diseases or poor oral hygiene; patients with severe bruxism or clenching habits; patients with periapical lesions in the treated teeth.

Outcomes:
Fracture of material or tooth and loss of retention
Artificial Intelligence-Based Predictive Models for Estimating Treatment Response in Sciatica Patients Treated With Transforaminal Epidural Steroid Injection (TFESI)
Trial ID: NCT07080307
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07
Completion date: 2026-10
Sponsor: Marmara University
Conditions: Sciatica; Low Back Pain; Lumbar Spinal Stenosis; Radiculopathy; Lumbar Disc Herniation
Locations: Marmara Üniversitesi Tıp Fakültesi, İstanbul , Maltepe 34854 Recruiting
Countries: Turkey (Türkiye)
Enrollment: 1500
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective observational study aims to develop artificial intelligence (AI)-based predictive models to estimate treatment response in patients with sciatica undergoing transforaminal epidural steroid injection (TFESI). Approximately 1500 adult patients diagnosed with sciatica and scheduled for TFESI at Marmara University Training and Research Hospital Algology Clinic will be enrolled. Clinical, radiological, laboratory, and psychological data will be systematically collected before the procedure and at multiple follow-up points (1 hour, 3 weeks, 3 months, and 6 months post-treatment). Pain relief, measured by a 50% or greater reduction in the Numeric Rating Scale (NRS), will be the primary outcome. The study will use these comprehensive data sets to train and validate AI algorithms, aiming to create a decision-support system that can predict individual patient responses to TFESI and improve personalized treatment planning.

Eligibility criteria:
Inclusion Criteria:

* Age 18 years or older
* Presence of clinical and radiological radiculopathy findings consistent with L4, L5, or S1 nerve root involvement
* Symptoms present for at least 3 months
* Resistant to conservative treatment
* Scheduled for transforaminal epidural steroid injection (TFESI) by a pain medicine specialist
* Being willing to participate in the study and having signed a consent form

Exclusion Criteria:

* Malignancy
* Coagulation disorders
* Pregnancy
* History of allergic reaction to the injected substances
* History of major psychiatric disorders
* Presence of known coagulopathy
* Receiving systemic steroid treatment for any reason
* Having received an epidural steroid injection within the last 3 months

Outcomes:
Change in pain intensity measured by Numeric Rating Scale (NRS); Change in functional status measured by Oswestry Disability Index (ODI)
A Novel Approach to Antimicrobial Resistance: Machine Learning Predictions for Carbapenem-Resistant Klebsiella in ICUs
Trial ID: NCT05985057
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-12-01
Completion date: 2024-06-30
Sponsor: Kocaeli University
Conditions: Carbapenem Resistant Enterobacteriaceae Infection; Artificial Intelligence; Intensive Care Unit
Interventions: Artificial intelligence
Locations: Kocaeli University
Countries: Turkey (Türkiye)
Enrollment: 289
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of this study to predict carbapenem resistant Klebsiella spp. earlier in our patients monitored in our Intensive Care Unit in the future, using artificial intelligence.

Patients with bloodstream infection and pneumonia caused by Klebsiella spp. will be comparatively examined in two groups, as sensitive and resistant. Resistance will be attempted to be predicted with deep machine learning.

Eligibility criteria:
Inclusion Criteria:

Patients monitored in our third-level intensive care unit between June 2017 and June 2023 will be evaluated retrospectively. Patients with pneumonia and bloodstream infection developed with Klebsiella spp. will be included in the study.

Exclusion Criteria:

* Patients under the age of 18 have not been included in the study.
* Infections outside of the respiratory tract and bloodstream have not been included in the study.
* Patients with respiratory tract colonization and without active inflammation have also not been included.

Outcomes:
Risk of Carbapenem Resistant Klebsiella Infection
QT-Logs : a Clinical Study to Monitor Cardiac Safety, With Artificial Intelligence for QT Interval Analysis of ECG Data From Smartwatches, in Patients Receiving Hydroxychloroquine Treatment for COVID-19
Trial ID: NCT04371744
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-04-17
Completion date: 2020-06-05
Sponsor: Assistance Publique Hopitaux De Marseille
Conditions: COVID-19
Locations: Assistance Publique Hôpitaux de Marseille
Countries: France
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
This observational pilot prospective study will evaluate a new method for remote monitoring of corrected QT measurement using an artificial intelligence (AI)-based solution and ECG data collected via smartwatches (AI-QTc), in patients ambulatory treated with the HC-AZ combination, at the early stage of COVID-19 infection, at a tertiary hospital center. Daily ECGs will be performed via the smartwatches. AI-QTc will be compared to standard manual QTc reviewed by cardiologist. Correlation and agreement between measures will be assessed.

Eligibility criteria:
Inclusion Criteria:

* Patient over 18 years old
* SARS-COV-2 confirmed infection by positive nasopharyngual PCR
* Prescription of association hydroxychloroquine and azythromycine for COVI-19 therapy
* Patient's smartphone able to download the smartwatches application

Exclusion Criteria:

* Presence of a pacemaker
* Pregnant or breastfeeding females
* Refusal to participate
* Incapacity
* Adult under legal protection (trusteeship, guardianship)

Outcomes:
Corrected QT (QTc) interval measurement
Lesion Detection Assessment in the Liver: Standard vs Low Radiation Dose Using Varied Post-Processing Techniques
Trial ID: NCT03151564
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2017-05-09
Completion date: 2027-04-30
Sponsor: M.D. Anderson Cancer Center
Conditions: Diseases of Liver; Colon Carcinoma; Colorectal Carcinoma; Liver Metastases
Interventions: Computed Tomography Scan - 50% Dose Reduction; Computed Tomography Scan - 70% Dose Reduction; Deep Learning Image Reconstruction (DLIR)
Locations: University of Texas MD Anderson Cancer Center
Countries: United States
Enrollment: 146
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
To compare 2 different image creation/processing techniques during a standard CT scan in order to "see" problems in the liver and learn which method provides better image quality. The techniques use new artificial intelligence software to decrease image noise, which helps the radiologist to evaluate.

Eligibility criteria:
Inclusion Criteria:

1. Patient must be \>/= 18 years of age and \</=90 years of age
2. Men and non-pregnant women
3. Pathology proven diagnosis of colon or colorectal carcinoma
4. Liver metastases on most recent CT examination
5. Standard of care CT abdomen examination planned WITH IV contrast

Exclusion Criteria:

1. Patients cannot give informed consent
2. Patients cannot undergo CT examination

Outcomes:
Metastasis Detection Accuracy