moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
The Effect of Web-Based and Artificial Intelligence-Assisted Personalized Applications on Knowledge Levels Compliance With Treatment and Self-Management Among Diabetic Individuals
Trial ID: NCT06650098
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-01
Completion date: 2026-06-01
Sponsor: Uludag University
Conditions: Diabetes Mellitus; Artificial Intelligence (AI); Self-management
Interventions: artificial intelligence-supported mobile application; WEB based application
Locations: Istanbul Basaksehir Cam and Sakura City Hospital
Countries: Turkey (Türkiye)
Enrollment: 156
Age range: None - None
Sex: ALL

Summary:
Patients in the AI-supported mobile application group will be able to log in with a username and password that will be defined specifically for them. Patients will be informed about how the application is used during their first interview. They will enter their personal and disease characteristics (age, gender, height, weight, HbA1C, HDL, LDL) into the application at the entrance. Other sections of the application will include exercise, nutrition, medication tracking, complication tracking and diabetic foot care sections. The person will be asked to enter relevant information in these fields according to their own life and condition (for example; how many times do you use insulin per day, what are your medication times, how do you spend your day in terms of exercise, how many meals do you eat, what is your diet, do you urinate frequently, are you extremely thirsty, are you hungry often, do you have numbness in your hands and feet, etc.). After the patient enters the necessary information, they will also be asked to enter their daily blood sugar measurement values into the system. Thus, the individual\&amp;#39;s hypo/hyperglycemia risk, risk analysis, nutrition recommendations, medication reminder system, exercise reminder and incentive warnings will be communicated to the individual thanks to the AI-based mobile application. The aim of this application is to reduce the risk of complications and improve the individual\&amp;#39;s quality of life by providing personalized recommendations for all the needs of the individual, including alarms and reminders, and to support patients to continue their diabetes education and disease management more actively.

Eligibility criteria:
Inclusion Criteria:

* Having been diagnosed with diabetes for at least 1 year
* Being between the ages of 18-65
* Being open to verbal communication
* Being able to read and write and speak Turkish
* Having a smart android phone and being able to use mobile applications
* Being willing to participate in the study

Exclusion Criteria:

* Having a perception disorder and psychiatric disorder that prevents the patient from communicating,
* Having a condition that prevents them from using a smart phone (advanced retinopathy and neuropathy, internet problems)
* Being on intensive insulin treatment
* Having a condition that prevents them from continuing the application phase of the study
* Wanting to leave the study

Outcomes:
Diabetes Self-Management Scale (DSMS)
Prospective Evaluation of Cardiac Point-of-Care Ultrasound (POCUS) Performed by PEM Fellows and Artificial Intelligence Interpretation Compared With a Complete Echocardiography in Children With Preexisting Cardiac Disease
Trial ID: NCT07503158
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-30
Completion date: 2028-03-30
Sponsor: Nicklaus Children's Hospital f/k/a Miami Children's Hospital
Conditions: Cardiac Anomalies
Interventions: AI Pocus_Cardiac
Locations: Nicklaus Children's Hospital
Countries: United States
Enrollment: 200
Age range: 1 Month - 21 Years
Sex: ALL

Summary:
The goal of this study is to prospectively assess the diagnostic agreement of PEM fellow-performed cardiac POCUS and of AI-assisted interpretation using the Exo Iris probe, as compared to a complete echocardiography for detecting left ventricular (LV) systolic dysfunction and pericardial effusion in children with preexisting cardiac disease.

Eligibility criteria:
Inclusion Criteria:

* Children aged 0-21 years
* Preexisting cardiac disease
* Participants are either evaluated in the ED or in the cardiology setting.

Exclusion Criteria:

* Participant's clinical condition requires immediate life-saving interventions
* New diagnosis of cardiac condition
* Preexisting cardiac abnormality documented prior to the index encounter.

Outcomes:
presence of pericardial effusion and LV systolic dysfunction
Research on Endoscopic Precision Biopsy Guided by AI System
Trial ID: NCT05261932
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-11-26
Completion date: 2023-11-30
Sponsor: Beijing Tsinghua Chang Gung Hospital
Conditions: Colorectal Adenoma
Interventions: AI-assisted guided biopsy
Locations: Beijing Tsinghua Changgung Hospital
Countries: China
Enrollment: 40
Age range: 30 Years - 75 Years
Sex: ALL

Summary:
Colorectal adenoma is a common disease and frequently-occurring disease in gastroenterology. With the continuous progress of colonoscopy equipment and the gradual improvement of endoscopic accessories, especially the development of chromo-endoscopy and magnifying endoscopy. The observation of the surface structure and capillary morphology of colorectal adenomas can realize optical biopsy. Currently, most clinical endoscopic diagnosis of colorectal diseases is biopsy under colonoscopy, and further treatment options are determined based on the pathological results of the biopsy. The problem is that the pathological diagnosis of some preoperative biopsy is not completely consistent with the pathological diagnosis of postoperative large specimens. Previous studies have found that the pathological diagnosis accuracy rate of preoperative biopsy is only 66-75%, so there is a certain degree of subjectivity in relying solely on colonoscopy white light biopsy. Based on the previous work, the research team has initially established an intelligent recognition model for colorectal adenoma classification (low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia), and formed a colorectal adenoma of a certain size with annotated endoscopic image data set. Using the YOLO-V4 algorithm, under the Darknet framework, to train an artificial intelligence (AI) system which specifically for adenoma recognition and diagnosis, its accuracy rate has reached more than 90%. This study intends to increase the sample size based on the previous work, and further improve the accuracy of the classification and diagnosis of the AI system, so as to guide the endoscopist to perform targeted biopsy and improve the accuracy of preoperative biopsy.

Eligibility criteria:
Inclusion Criteria:

* Age between 30-75;
* Those who have no mental abnormality and can conduct questionnaire surveys;
* BBPS ≥ 6;
* Colorectal advanced adenoma, and admitted for complete resection with EMR and ESD;
* Provide the relevant information required by this study and sign the informed consent.

Exclusion Criteria:

* Those who cannot provide the relevant information required by this research;
* Patients with inflammatory bowel disease;
* Those with a history of liver cirrhosis, uncontrolled hypertension, history of myocardial infarction, cardiac insufficiency, renal insufficiency, respiratory failure, diabetic ketosis and electrolyte imbalance and other serious diseases;
* Those who cannot stop antiplatelet drugs or anticoagulant drugs;
* Those who have not completed full colonoscopy;
* Pregnant women.

Outcomes:
The accuracy of AI; The accuracy of expert with or without AI; The accuracy of non-expert with or without AI
Smartphone App Based Cognitive Behavioral Therapy Versus Online Group Cognitive Behavioral Therapy: Randomized, Non-inferiority Clinical Trial
Trial ID: NCT05450614
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-08-01
Completion date: 2023-12-31
Sponsor: Hospital de Clinicas de Porto Alegre
Conditions: Depressive Disorder, Major
Interventions: Thrive app; TCCG
Locations: Hospital de Clínicas de Porto Alegre
Countries: Brazil
Enrollment: 200
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The present research project aims to compare the efficacy and safety of an intervention based on a smartphone application, which uses CBT techniques, to online group cognitive behavioral therapy (CBCT), in improving depressive symptoms.

The project also has supplemental analysis to predict who will respond to the CBT intervention using the application. For this analysis, machine learning algorithms, a set of techniques from the field of artificial intelligence, will be used to create a predictive calculator for response to interventions. The analysis protocol used for this analysis will be in accordance with that proposed in task Force of the International Society for Bipolar Disorders.

Eligibility criteria:
Inclusion Criteria:

* have depressive symptoms characterized by PHQ-9≥9 and a diagnosis of Major Depressive Episode through the Mini-International Neuropsychiatric Interview instrument -- own a smartphone
* agree with the treatment objectives and sign the consent form
* age between 18 and 65 years
* be living in Brazil and be fluent in Portuguese.

Exclusion Criteria:

* pregnancy
* initiation of any psychological treatment within the previous 3 months
* visual impairment that prevents use of the app
* current risk of suicide
* bipolar disorder, schizophrenia, schizoaffective disorder, intellectual disability, and alcohol or drug abuse in the last year.

Other comorbid medical conditions will also be included, unless they are degenerative (dementia or multiple sclerosis) in a way that could compromise interaction with the application. Participants may be taking psychotropic drugs, including medication for depression, if the medication regimen has not changed in the previous 3 months and if there has been no change over the course of study participation.

Outcomes:
reduction of Patient Health Questionnaire (PHQ-9) averages
Performance Evaluation of Artificial Intelligence Screening Model in Coronary Heart Disease Detection
Trial ID: NCT06658600
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-10
Completion date: 2025-05
Sponsor: Tsinghua University
Collaborators: Shanghai Jiao Tong University Affiliated Sixth People's Hospital; Shanghai Health and Medical Center
Conditions: Coronary Heart Disease
Interventions: Physician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart disease; Physician readers will be assisted with RF-CL table to calculate the probability of obstructive coronary heart disease
Locations: Tsinghua University; Shanghai Health and Medical Center; Shanghai Sixth People's Hospital
Countries: China; China; China
Enrollment: 900
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
To determine whether an integrated AI decision support can save time and improve accuracy of assessment of obstructive coronary heart disease (CHD), the investigators are conducting a randomized controlled study of AI guided measurements of obstructive CHD probability compared to clinical assessment in preliminary evaluations by physicians.

Eligibility criteria:
Inclusion criteria:

* Individuals with symptoms of coronary heart disease
* Age range: 18-75 years old
* Can accept and cooperate with the examination and potential follow-up work after being selected for clinical trials

Exclusion criteria:

* Severe hypertension (\>180/110mmHg)
* Complex arrhythmia (atrial fibrillation, atrial flutter, frequent premature beats)
* Severe lung disease and chest malformation or surgery patients
* Acute myocardial infarction occurring less than 3 months ago
* Individuals with severe liver and kidney dysfunction and electrolyte imbalance

Outcomes:
Diagnostic Accuracy of Participants with Obstructive Coronary Heart Disease
A Prospective, Single-arm, Single-center Observational Study to Evaluate the Clinical Effectiveness of an Intelligent Reporting System for the Upper Gastrointestinal Endoscopy
Trial ID: NCT05851716
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-08-21
Completion date: 2024-05-20
Sponsor: Renmin Hospital of Wuhan University
Conditions: Endoscopy, Gastrointestinal; Artificial Intelligence
Locations: Renmin Hospital of Wuhan University
Countries: China
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to evaluate the effectiveness of an AI-based reporting system for upper gastrointestinal endoscopy. The main question it aims to answer is: Whether the AI-based reporting system can improve the completeness of the reports, which are drafted by endoscopists with the AI assistance. Participants will undergo upper gastrointestinal endoscopy examination as routine. The junior endoscopists will draft the report with the assiatance of the AI system. And the senior and expert endoscopists will draft the report using the traditional reporting system without AI assistance.

Eligibility criteria:
Inclusion Criteria:

1. Aged 18 years
2. Aim to undergo screening, surveillance, and diagnosis
3. Undergo sedated EGD
4. Able to read, understand, and sign informed consent

Exclusion Criteria:

1. EGD contraindications
2. Not suitable for sedated endoscopy after anaesthesia evaluation
3. Biopsy contraindications
4. Active upper gastrointestinal bleeding or emergency oesophagogastroduodenoscopy (EGD)
5. Pregnancy
6. Upper gastrointestinal surgery or residual stomach
7. Not suitable for recruitment after investigator evaluation because of other high-risk conditions

Outcomes:
Completeness of reporting lesions
Evaluation of Artificial Intelligence for Adenoma Detection in Water Exchange Colonoscopy: the WEAID Randomized Controlled Trial (Water Exchange With Artificial Intelligence-assisted Detection)
Trial ID: NCT06173258
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-18
Completion date: 2025-01-05
Sponsor: Evergreen General Hospital, Taiwan
Collaborators: Digestive Endoscopy Unit, CTO Hospital, Iglesias, Italy; The Sepulveda Ambulatory Care Center, VAGLAHS, UCLA in the USA
Conditions: Colon Adenoma; Colonoscopy; Colon Polyp
Interventions: Water exchange alone; Water exchange plus Artificial Intelligence (AI)
Locations: CTO Hospital; Chi-Liang Cheng
Countries: Italy; Taiwan
Enrollment: 560
Age range: 45 Years - 75 Years
Sex: ALL

Summary:
The goal of this clinical trial is to compare the detected adenoma per colonoscopy (APC) in participants undergoing screening, surveillance, and positive fecal immunochemical test (FIT) or guaiac fecal occult blood test (gFOBT). There will be two arms in this study: WE water control and water plus artificial intelligence (AI). The main question it aims to answer is whether the addition of AI into water exchange (WE) colonoscopy increases APC than WE alone. The control method will use water instead of air inserted into the colon. The study method will use a commercially available AI system plus water during the procedure. Researchers will compare APCs to see if the addition of AI increases detection of adenomas during WE colonoscopy.

Eligibility criteria:
Inclusion Criteria:

* Male and female patients aged 45-75 years at average risk for colorectal cancer who plan to undergo colonoscopy for primary screening, postpolypectomy surveillance, and individuals with positive fecal immunochemical test or guaiac fecal occult blood test results.

Exclusion Criteria:

* Patients with a history of inflammatory bowel disease
* Patients with hereditary colorectal cancer syndrome or serrated polyposis syndrome
* Patients with a personal history of colorectal cancer
* Patients with a history of colorectal resection
* Patients with colonic stricture
* Patient with severe comorbid illnesses rendering polypectomy unsafe
* Patients with colonoscopy contraindications (e.g., acute diverticulitis or toxic megacolon)
* Therapeutic colonoscopy (e.g., hemostasis, removal of a large polyp)
* Emergent colonoscopy
* Pregnant women or those planning pregnancy
* Patients unwilling to participate in the study

Outcomes:
Adenoma per colonoscopy; Adenoma detection rate
Effect of Artificial Intelligence-Based Training on Cognitive Functions in Patients With Stroke
Trial ID: NCT07452276
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-01
Completion date: 2026-06-06
Sponsor: Omima Alaa Eldin Hussein
Conditions: Post-Stroke Cognitive Impairment (PSCI); Stroke; Artifical Intelligence
Interventions: cognifit Mobile Application for Cognitive Training; Conventional Cognitive Rehabilitation
Locations: Physical therapy- Cairo University
Countries: Egypt
Enrollment: 40
Age range: 45 Years - 60 Years
Sex: ALL

Summary:
This study would answer the following question:Does AI application-based training improve cognitive function in Patients with Stroke?

The aims of this study:

To investigate the efficacy of AI application-based training on cognitive function in stroke patients.

Eligibility criteria:
Inclusion Criteria:

* Patient's age will range from 45- 60 years.

  * Diagnosis of stroke in the chronic stage (≥3 months post-onset).
  * Presence of cognitive impairment confirmed by a Montreal Cognitive Assessment (MoCA) score ≤ 26.
  * Medically stable and able to follow instructions.
  * patients with memory deficits following stroke

Exclusion Criteria:

\- Patients will be excluded if they have:

* Severe visual, hearing, or speech impairments prevent participation.
* History of other neurological or psychiatric disorders.
* Unstable cardiovascular or systemic disease.
* Severe depression (Beck Depression Inventory \> 29).

Outcomes:
Change in Cognitive Function
An Ambispective Pre and Post Deployment Observational Cohort Study to Evaluate the Yield of Actionable Lung Nodules and Lung Cancer Through Chest X-Rays Using Artificial Intelligence
Trial ID: NCT06746324
Status: WITHDRAWN
Study type: OBSERVATIONAL
Start date: 2025-05-15
Completion date: 2026-06-15
Sponsor: University of Florida
Conditions: Lung Nodules; Lung Cancers; Early-Stage Lung Cancer; Artificial Intelligence in Radiology; Computer-Aided Detection
Enrollment: 0
Age range: 35 Years - None
Sex: ALL

Summary:
The study evaluates the impact of qXR-LN compared to standard radiologist-only interpretations before and after AI deployment. The goal is to compare how well lung nodules and cancers are detected in two time periods: before and after the implementation of the AI tool in routine clinical practice. The study aims to determine whether the AI system can help radiologists identify more actionable lung nodules and diagnose lung cancer earlier, ultimately improving patient outcomes.

No changes will be made to patients' standard care, and all treatment decisions will be based on the clinical judgment of physicians. The study includes patients over 35 years old who undergo chest X-rays for various medical reasons, excluding those with known lung cancer.

Eligibility criteria:
Inclusion Criteria:

* Age ≥35 years at the time of chest X-ray acquisition
* Chest X-ray must be obtained as part of routine care (e.g., ordered for respiratory complaints, screening, or other clinical indications)
* Chest X-ray performed using CR/DR/DX imaging modality
* Examination described as "Chest"
* View: PA or AP
* Patient positioned as Erect or Supine
* Image available in valid DICOM format with proper DICOM prefix values (including "DICM" in the header)

Exclusion Criteria:

* Patients aged \<35 years at the time of chest X-ray
* Patients with known lung cancer at the time of chest X-ray acquisition
* Lateral views or any imaging modality other than CR/DR/DX
* Imaging or anatomy not specified as Chest (e.g., different body parts or modalities)

Outcomes:
Difference in Nodule Detection Rate Between Pre- and Post-Deployment Cohorts
Künstliche Intelligenz (KI)-gestützte Früherkennung Der Diabetischen Retinopathie (FUNDUS-KI)
Trial ID: NCT07378891
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-12-01
Completion date: 2028-12-15
Sponsor: University Hospital Tuebingen
Conditions: Diabetes (DM); Prediabetes
Locations: University Hospital Tuebingen, Dep. Diabetology
Countries: Germany
Enrollment: 100
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Examination of individuals with prediabetes and established diabetes for early signs of diabetic retinopathy.

Eligibility criteria:
Inclusion Criteria:

* Voluntary adults
* Aged between 18 and 80 years
* Understanding of the study explanations and instructions
* Prediabetes (defined by elevated fasting glucose 100-125 mg/dL and/or 2-hour glucose in the oral glucose tolerance test of 140-199 mg/dL and/or HbA1c of 5.7-6.4%)
* manifest diabetes (all types of diabetes according to Ahlqvist)

Exclusion Criteria:

* participants unable to give consent
* Those who do not agree to be informed about incidentally detected pathological findings
* Uncontrolled arterial hypertension (Stage III) despite multiple antihypertensive medications
* Eye diseases that impair the interpretability of the fundus examination

Outcomes:
Assessment of retinal changes
Large Scale Clinical Data Registry (CDR) to Accurately Identify the Specific Tumor Phenotypes to Better Diagnose and Predict Patient Outcome in HCC
Trial ID: NCT04681274
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-08-31
Completion date: 2022-12-31
Sponsor: Assistance Publique - Hôpitaux de Paris
Conditions: Hepatocellular Carcinoma
Interventions: Image features extraction and clustering; Phenotype signature database building
Locations: Assistance Publique - Hôpitaux de Paris (AP-HP) Groupe Hospitalier La Pitié-Salpêtrière
Countries: France
Enrollment: 2429
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The purpose of this study is the development of a content-based image retrieval (CBIR) platform, where validation studies will be conducted for liver disease subtyping and hepatocellular carcinoma (HCC) phenotyping on images for use as diagnostic and prognostic markers of outcome in conjunction with large scale data registries and advanced predictive machine learning methodologies. The proposed objectives will deliver one or more fit-for-purpose non-invasive imaging-based methodologies to evaluate the presence, activity and type of HCC in clinical practice.

Eligibility criteria:
Inclusion Criteria:

Patients with visual liver disease who:

* Have a lesion visualized on CT scans / MRI with histological confirmation (surgical resection, biopsy, transplant).
* With a CT scan / MRI performed within 6 months prior to biopsy, surgical or transplant intervention.

Exclusion Criteria:

* Patients that had CT scans / MRI taken more than 6 months prior to surgical intervention

Outcomes:
Specific tumor phenotypes