moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Development and Validation of an Explainable Artificial Intelligence Model for Early Gastric Cancer Diagnosis Using Multimodal Endoscopic Imaging
Trial ID: NCT07551466
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05-01
Completion date: 2027-02-01
Sponsor: The First Affiliated Hospital of Soochow University
Conditions: Early Gastric Cancer
Interventions: No intervention (observational study)
Locations: The First Affiliated Hospital of Soochow University
Countries: China
Enrollment: 100
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Early gastric cancer (EGC) is often difficult to detect accurately during endoscopic examination due to subtle morphological features and variability among endoscopists. Artificial intelligence (AI) has shown promise in improving diagnostic performance; however, most existing models lack interpretability and rely on single-modality imaging.

This study aims to develop and evaluate an explainable multimodal artificial intelligence model for the diagnosis of early gastric cancer using endoscopic imaging. The model integrates features derived from white-light imaging and image-enhanced endoscopy, along with quantitative image features and clinical data, to improve diagnostic accuracy and provide interpretable decision support.

The primary outcome is the diagnostic performance of the AI model for detecting early gastric cancer, evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.

The results of this study are expected to provide evidence for the clinical utility of explainable AI in endoscopic diagnosis and support the development of reliable human-AI collaborative diagnostic systems.

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years
* Suspicious gastric lesions identified on white-light imaging (WLI)
* Preoperative biopsy indicating precancerous lesions (dysplasia or intraepithelial neoplasia) or adenocarcinoma, with preoperative magnifying endoscopy with narrow-band imaging (ME-NBI) performed
* Patients meeting the absolute indications for endoscopic submucosal dissection (ESD) and who underwent ESD

Exclusion Criteria:

* Non-adenocarcinoma histological types (e.g., lymphoma)
* Patients who did not undergo ME-NBI examination or did not receive ESD
* Lesions invading the muscularis propria or deeper layers
* Missing or indeterminate postoperative histopathological results

Outcomes:
Diagnostic performance of the artificial intelligence model for detecting early gastric cancer
Establishing Malnutrition Diagnosis System by Using Artificial-intelligence Technology to Improve the Application of Clinical Pathway
Trial ID: NCT04776070
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-08-13
Completion date: 2022-05-21
Sponsor: Peking Union Medical College Hospital
Collaborators: Sichuan Academy of Medical Sciences; Peking Union Medical College
Conditions: Disease-related Malnutrition
Locations: Dongcheng district，Peking union medical college hospital
Countries: China
Enrollment: 500
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The prevalence of malnutrition is estimated at 30-50% of hospitalized patients in China. Disease-related malnutrition increases the risk of infection, mortality, length of hospitalization as well as the economic burden. National Nutrition Plan proposed to reduce malnutrition, but a clear, effective roadmap and protocol has not existed yet. Several factors impede to resolve the above challenges. They include :1) the low efficiency of current malnutrition diagnosis methods; 2) the lack of dynamic, standard method that can evaluate nutritional status in quantitative way. To this end, the investigators aim to establish an artificial-intelligence malnutrition diagnosis system to improve the application of malnutrition Clinical Pathway. Firstly, the investigators will establish a multidimensional malnutrition large data set, based on our previously built national hospital nutrition screening data set.

It will contain deep 3D facial images, semi-structured and structured electronic medical record. Then, the investigators will use ensemble learning algorithm to establish a fully automatic, artificial-intelligence malnutrition diagnosis model that includes both etiological and phenotypic diagnosis.

Eligibility criteria:
Inclusion Criteria:

* Adults (≥18 years old);
* Within 48 hours of admission;
* Inpatients at high risk of malnutrition, such as malignant tumors, chronic obstructive pulmonary disease, etc;
* Han nationality;
* Able to given informed consent.

Exclusion Criteria:

* Patients with artificial facial changes (such as plastic surgery , head and neck radiotherapy , head and neck trauma);
* Diseases with special facial changes (such as acromegaly);
* High dose glucocorticoid users;
* Patients with facial edema;
* Emergency admission with an expected length of stay of less than 3 days;
* Other conditions researchers thought could not be included

Outcomes:
malnutrition diagnosis
Detection of Cerebral Ischemia With Artificial Intelligence- Biomarkers as Indicators in Controlled Cerebral Ischemia and Reperfusion.
Trial ID: NCT03919370
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-10-01
Completion date: 2024-12-31
Sponsor: Linda Block
Collaborators: Göteborg University
Conditions: Ischemia, Cerebral; Ischemic Stroke; Artery Occlusion; Artery Carotid Stenosi; Anesthesia; Surgery
Interventions: No intervention
Locations: Sahlgrenska University hospital
Countries: Sweden
Enrollment: 60
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
In patients undergoing planned surgery for carotid tromendarterendectomy, a non-invasive device that registers heart rate variability is attached. Furthermore a non-invasive device that monitors cerebral oxygenation- near infrared spectroscopy as well as electroencephalography is also attached. At times when surgeons clamps the carotid artery, there will be a moment with controlled cerebral ischemia.

This will be registered by the devices. The information obtained will be used to teach artificial intelligence what patterns are related to cerebral ischemia. The same procedure will be performed in patients undergoing ocklusive cerebral trombectomy, so the artificial intelligence will learn to recognize cerebral reperfusion.Blood samples will be drawn before and after cerebral ischemia may occur and will be analyzed for neurobiomarkers and cardiac biomarkers. To teach the algorithm patterns from anaesthesia and surgery in patients without pre existing neuronal injury, the same method will be applied to patients undergoing mixed abdominal surgery. This group will provide a better knowledge of neuro biomarker patterns during anesthesia and surgery.

Eligibility criteria:
Inclusion Criteria:

* Patients planned for carotid surgery or trombectomy
* Giving informed consent to participate

Exclusion Criteria:

* Patients that do not consent Patients with arrythmia or pacemaker

Outcomes:
Heart Rate variability; Near infrared spectroscopy; Electroencephalography; Biomarkers indicating cerebral ischemia in blood; Biomarkers indicating cardiac ischemia in blood
Reliability of Artificial Intelligence (AI)-Augmented Point-of-care Cardiac Ultrasound (POCCUS) in the Hands of Internists
Trial ID: NCT05455541
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-08-21
Completion date: 2023-12
Sponsor: Sheba Medical Center
Conditions: Cardiovascular Diseases; Valvular Heart Disease
Interventions: AI augmented POCUS examination
Locations: Sheba Medical Center
Countries: Israel
Enrollment: 300
Age range: None - None
Sex: ALL

Summary:
The study aim is to test the diagnostic performance of internists interpreting echo images aided by the AISAP CARDIO V0.7 diagnostic support system. Ground truth will be established by an interpretation by cardiologists specialized in echo, of the same POCUS images (acquired by the internist \\ sonographer ).

Up to 1000 subjects; Study population will be distributed according to the following schema:

Group 1 -up to 800 patients hospitalized in the Internal Medicine division Group 2 - up to 200 patients hospitalized in the acute Geriatric division

Eligibility criteria:
Inclusion Criteria:

1. Clinically indicated POCUS cardiac assessment as determined by the treating physicians for an accepted clinical indication
2. Willing and able to provide consent for a short partial transthoracic POCCUS examinations
3. No urgent or other compelling need for a comprehensive echo exam by the echo lab.

Exclusion Criteria:

1. Unstable clinical condition that does not permit any reasonable delay necessary to obtain consent and perform the POCCUS
2. Severe thoracic deformation or thoracic wall infection or wound that does not permit an adequate echocardiographic examination
3. Very poor or non-diagnostic echocardiographic imaging quality on prior echocardiography examinations.
4. Patients after lung resection
5. Patients unable lie in bed and undergo a standard echo study
6. Participation in an interventional study

Outcomes:
Degree of agreement; Clinical significant findings detected by the internist utilizing AI-aided POCUS
Using Machine Learning Algorithms to Develop a Risk Prediction Model for Phthalate-ester-induced Diseases and Suggestions for Improved Nursing Assessment
Trial ID: NCT05892029
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-01-01
Completion date: 2022-12-31
Sponsor: National Taipei University of Nursing and Health Sciences
Conditions: Plasticizers
Locations: Cheng Hsin General Hospital; National Taipei university of nursing and health science
Countries: Taiwan; Taiwan
Enrollment: 1136
Age range: None - None
Sex: ALL

Summary:
This exploratory study collected basic demographic and laboratory data from the Taiwan Biobank using artificial intelligence algorithms and applied data mining to identify the correlations between phthalate esters \[di(2-ethylhexyl) phthalate, DEHP\], lifestyle, and disease.

Eligibility criteria:
inclusion:

1. The target subjects of this study are individuals aged 30 to 70 years old
2. who were tested for PAEs from January 1, 2016 to December 31, 2020.

exclusion:

1who have not been tested for plasticizers

Outcomes:
Risk assessment analysis of disease and PAEs; Artificial intelligence prediction model for diseases and PAEs
Research on Identifying Critical Surgical Anatomy in Cholecystectomy Videos Based on Deep Learning
Trial ID: NCT07158372
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-15
Completion date: 2028-08-15
Sponsor: Chinese Academy of Sciences
Collaborators: The First Affiliated Hospital of Zhengzhou University; Capital Medical University; Beijing Anzhen Hospital; Shanghai East Hospital of Tongji University; Peking University People's Hospital
Conditions: Cholecystectomy; Surgical Video Identification
Interventions: AI-assisted Intraoperative Anatomy Analysis
Locations: The First Affiliated Hospital of Zhengzhou University; Beijing Anzhen Hospital, Capital Medical University; Beijing Luhe Hospital, Capital Medical University; Peking university people's hospital; Shanghai East Hospital of Tongji University
Countries: China; China; China; China; China
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
Laparoscopic cholecystectomy is a common surgical procedure, but it carries the potential for bile duct injury and other surgical risks. To provide visual assistance to surgeons during surgery and mitigate these risks, this research project aims to develop a real-time object recognition algorithm based on deep learning technology. This algorithm will label key anatomical structures in laparoscopic cholecystectomy videos, providing surgeons with immediate information on dangerous and safe areas.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 or above who are diagnosed by a doctor as needing laparoscopic cholecystectomy

Exclusion Criteria:

* Patients who did not undergo surgery at the original hospital and those whose videos were blurry were excluded.

Outcomes:
Dice Similarity Coefficient; Mean Intersection over Union; Global Accuracy
Can Pre-operative Characteristics Predict Failure of Supraglottic Airway to Tracheal Tube? A Machine Learning Algorithm (ERICA)
Trial ID: NCT06617403
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-12-01
Completion date: 2024-12-31
Sponsor: University Hospital Ulm
Collaborators: Technical University of Munich
Conditions: Anesthesia, General; Postoperative Complications; Laryngeal Masks; Treatment Failure
Interventions: non
Locations: University Hospital Ulm; Technical University Munich
Countries: Germany; Germany
Enrollment: 44000
Age range: 18 Years - None
Sex: ALL

Summary:
Supraglottic airway devices (SGA) are a safe and well-established technique for airway management. Nowadays, up to 60% of general anaesthetics performed in European countries use SGA. In 0.2-4.7% SGA fail and require conversion to tracheal tubes.

The ERICA study will use artificial intelligence methods to develop a model that can predict the risk of an unplanned SGA conversion based on pre-operative characteristics available during the premedication visit.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (≥18 years) receiving general anaesthesia for non-cardiac surgery with a supraglottic airway device

Exclusion Criteria:

* None

Outcomes:
Risk of unplanned SGA conversion
Development and Validation of a Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases
Trial ID: NCT04222439
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-01-01
Completion date: 2020-02
Sponsor: Shandong University
Conditions: Gastrointestinal Disease
Interventions: AI for the Diagnosis of Gastrointestinal Diseases
Locations: Qilu Hospital, Shandong University
Countries: China
Enrollment: 100000
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of gastrointestinal diseases. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.

Eligibility criteria:
Inclusion Criteria:

* Participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals.

Exclusion Criteria:

\-

Outcomes:
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
Artificial Intelligence With DEep Learning on COROnary Microvascular Disease
Trial ID: NCT04598997
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-10-20
Completion date: 2024-11
Sponsor: University Hospital, Grenoble
Conditions: Coronary Microvascular Disease; Artificial Intelligence; Cardiovascular Diseases; Heart Failure
Locations: Chu Grenoble Alpes
Countries: France
Enrollment: 600
Age range: 18 Years - None
Sex: ALL

Summary:
Despite the progress made in the management of myocardial infarction (MI), the associated morbidity and mortality remains high. Numerous scientific data show that damage of the coronary microcirculation (CM) during a STEMI remains a problem because the techniques for measuring it are still imperfect. We have simple methods for estimating the damage to the MC during the initial coronary angiography, the best known being the calculation of the myocardial blush grade (MBG), but which is semi-quantitative and therefore not very precise, or more precise imaging techniques, such as cardiac MRI, which are performed 48 hours after the infarction and which make the development of early applicable therapeutics not very propitious. Finally, lately, the use of special coronary guides to measure a precise CM index remains non-optimal because it prolongs the procedure. However, the information is in the picture and this information could allow the development of therapeutic strategies adapted to the patient's CM. Indeed, the arrival of iodine in CM increases the density of the pixels of the image, this has been demonstrated by the implementation in 2009 of a software allowing the calculation of the MBG assisted by computer. But the performances of this software did not allow its wide diffusion. Today, the field of medical image analysis presents dazzling progress thanks to artificial intelligence (AI). Deep Learning, a sub-category of Machine Learning, is probably the most powerful form of AI for automated image analysis today. Made up of a network of artificial neurons, it allows, using a very large number of known examples, to extract the most relevant characteristics of the image to solve a given problem. Thus, it uses thousands of pieces of information, sometimes imperceptible to the naked eye. We hypothesize that a supervised Deep Learning algorithm trained with a set of relevant data, will be able to identify a patient with a pejorative prognosis, probably related to a microcirculatory impairment visible in the image.

Eligibility criteria:
Inclusion Criteria:

* Age over 18 years
* Patients who have undergone coronary angioplasty revascularization at CHUGA for STEMI from 2015 to 2018 for which images are usable.
* Patient affiliated with social security
* Non-opposition to participation

Exclusion Criteria:

* Coronary artery image not usable
* Patient under guardianship or deprived of liberty

Outcomes:
Death or re-hospitalization for heart Failure
Teledermoscopy and Artificial Intelligence: Effects of Implementation in Clinical Practice
Trial ID: NCT05033678
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-08-16
Completion date: 2029-08-31
Sponsor: Region Skane
Collaborators: Lund University; Karolinska University Hospital
Conditions: Skin Cancer
Interventions: Diagnostic algorithms
Locations: Department of dermatology, Skane University Hospital
Countries: Sweden
Enrollment: 8000
Age range: 15 Years - None
Sex: ALL

Summary:
The study has 2 parts. Part 1 will investigate the effects of introducing teledermoscopy in clinical practice, more specifically the change in referral patterns, the risk of undetected skin cancers and the effect on diagnostic accuracy in general practitioners.

Part 2 will investigate how to introduce artificial intelligence (AI) within teledermocsopy. In this study the investigators will measure the diagnostic accuracy of teledermoscopic assessors that had access to the results of artificial intelligence algorithm compared to those who did not.

Data will be collected through teledermoscopic referrals, patient records, national registries and questionnairs.

Eligibility criteria:
Inclusion Criteria:

* Has a skin lesion assessed by a physician during a visit
* The physician decides to create a teledermoscopy referral

Exclusion Criteria:

* inability or unwillingness to participate in the study
* the patient is younger than 15 years old
* Images of such bad quality they cannot be assessed

Outcomes:
Effect on referral patterns; Effect on diagnostic accuracy due to availability of artificial intelligence; Risk of undetected skin cancer; Artificial intelligence timing and effect on diagnostic accuracy and willingness to rethink the preliminary diagnosis
Rajaie Cardiomyopathy and Myocarditis Registry
Trial ID: NCT04304118
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-11-11
Completion date: 2025-11-11
Sponsor: Rajaie Cardiovascular Medical and Research Center
Conditions: Clinical Characteristics; Natural History; Long-term Outcomes; Current Therapeutic Approaches
Locations: Behshid Ghadrdoost
Countries: Iran
Enrollment: 6000
Age range: 1 Year - None
Sex: ALL

Summary:
The prevalence of patients with cardiomyopathies who referred to Rajaei Cardiovascular medical and research center is remarkable, and also the mission of this center is to achieve center of excellence in the field of cardiomyopathy. Rajaie Cardiomyopathy and myocarditis Registry study is an observational registry of consecutive patients with four cardiomyopathy subtypes: hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and restrictive cardiomyopathy (RCM) as well as myocarditis designed to determine clinical characteristics, natural history, current therapeutic approaches, response to treatment and long-term outcomes of patients with cardiomyopathy and myocarditis and to address limitations in extant evidence to improve prognostication in cardiomyopathies and myocarditis. Prediction of mortality and response to different treatments in these patients using artificial intelligence is another aim of this registry

Eligibility criteria:
Inclusion Criteria:

1. age greater than one year
2. documented cardiomyopathy/ myocarditis fulfilling standard diagnostic criteria
3. able to give informed consent or in the case of child consent from a parent

Exclusion Criteria:

Patients who have not consent to participate in the study.

Outcomes:
Admissions to hospital; Development of chronic heart failure; Prevalence of Stroke/Transient ischemic attack; Incidence of sudden death; Incidence of heart trasplantation
Eye-tracking-based Artificial Intelligence Detects Abnormalities of the Oculomotor System in Type 1 Diabetes
Trial ID: NCT04608890
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-12-01
Completion date: 2021-12-31
Sponsor: University of Milan
Collaborators: ASST Fatebenefratelli Sacco
Conditions: Diabetic Neuropathies
Interventions: Eye-tracking video test
Locations: Sacco University Hospital
Countries: Italy
Enrollment: 40
Age range: 16 Years - 60 Years
Sex: ALL

Summary:
Abnormalities of the oculomotor system may represent an early sign of diabetic neuropathy and are currently poorly studied. The investigators designed an eye-tracking-based test to evaluate the oculomotor function in patients with type 1 diabetes.

The investigators used the SRLab -Tobii TX300 Eye tracker®, an eye-tracking device, coupled with a software that we developed to test abnormalities of the oculomotor system. The software consists in a series of screens divided in 5 classes of parameters (Resistance, Wideness, Pursuit, Velocity and Optokinetic Nystagmus \[OKN\]) to evaluate both smooth and saccadic movement in different directions. 40 healthy volunteers and 40 patients with long-standing type 1 diabetes will be enrolled to analyze the alterations in the oculomotor system and function.

Eligibility criteria:
Patients:

Inclusion Criteria:

* Type 1 diabetes
* willing to give consent

Exclusion Criteria:

* Diabetic retinopathy and other ocular diseases
* other endocrine disease
* malignancy

Healthy volunteers:

Inclusion Criteria:

* willing to give consent

Exclusion Criteria:

* Other ocular diseases
* Any endocrine disease
* Malignancy

Outcomes:
Percentage of patients with a change in eye movement test in each class Resistance, Velocity, Pursuit, Wideness and OKN measured by the eye-tracking based test