moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Dual Tandem Study Comparing the Adenoma Detection and Miss-rate of SC to That of Artificial Intelligence (CAD-EYE) Aided Colonoscopy and to That of Artificial Intelligence (CAD-EYE) and G-EYE® Aided Colonoscopy.
Trial ID: NCT05318495
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-03-01
Completion date: 2022-12
Sponsor: Dr. Horst Schmidt Klinik GmbH
Conditions: Adenoma; Colo-rectal Cancer
Interventions: Standard Colonoscopy; CAD-EYE; G-EYE
Locations: Helios Dr. Horst Schmidt Kliniken
Countries: Germany
Enrollment: 372
Age range: None - None
Sex: ALL

Summary:
Single-center, four-arm dual-tandem, randomized, open-label study involving the following devices:

1. The Standard Colonoscope is a high-definition colonoscope employing advanced optical filtering and enhancement techniques - blue light imaging (BLI) and linked color imaging (LCI).
2. CAD-EYE is an artificial intelligence software, used in real-time during the colonoscopy procedure for aiding the identification and characterization of polyps and adenomas.
3. The G-EYE® balloon is a reusable (reprocessable) balloon permanently installed on the distal tip of a standard colonoscope, and is also intended to assist in flattening colonic folds and control the colonoscope's field of view and tip positioning.

Eligibility criteria:
Inclusion Criteria:

1. Screening and surveillance population for Adenoma and CRC.
2. The patient must understand and sign a written informed consent for the procedure.

Exclusion Criteria:

1. Subjects with inflammatory bowel disease;
2. Subjects with a personal history of hereditary polyposis syndrome;
3. Subjects with suspected chronic stricture potentially precluding complete colonoscopy;
4. Subjects with diverticulitis or toxic megacolon;
5. Subjects with prior colonic surgery (exclusion appendectomy)
6. Subjects with a history of radiation therapy to abdomen or pelvis;
7. Pregnant or lactating female subjects;
8. Subjects who are currently enrolled in another clinical investigation.
9. Subjects with current oral or parenteral use of anticoagulants, not considered eligible by the investigator.
10. Subjects with recent (within the last 3 mounts) coronary ischemia or CVA (stroke)
11. Any patient condition deemed too risky for the study by the investigator

Outcomes:
Additional adenoma detection yield
Data Collection for the Development of Artificial Intelligence (AI) Software to Support Ultrasound-Guided Regional Anesthesia Procedures
Trial ID: NCT06920277
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-10-29
Completion date: 2024-10-31
Sponsor: Smart Alfa Teknoloji San. ve Tic. A.S.
Conditions: Ultrasound Scan
Interventions: Ultrasound scans
Locations: Conference Room at Doubletree by Hilton Austin
Countries: United States
Enrollment: 46
Age range: 18 Years - None
Sex: ALL

Summary:
This study is designed to collect ultrasound images from healthy volunteers in the U.S.

Eligibility criteria:
Inclusion Criteria:

* Provision of signed and dated informed consent form
* At least 18 years of age and older
* Stated willingness to comply with all study procedures and availability for the duration of the study
* In good general health

Exclusion Criteria:

* Pregnancy
* Inability to lie to flat
* Any complaint or anatomical deformity on the regions to be scanned

Outcomes:
Number of ultrasound scan recordings relevant to peripheral nerve blocks collected
DeepDoc - Clinical Validation of an AI-based Decision Support System in the Early Diagnostic Evaluation of Neurological Diseases: a Prospective Multi-center Study
Trial ID: NCT04055766
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-09-01
Completion date: 2021-03-01
Sponsor: Xijing Hospital
Conditions: Stroke; Headache; Vertigo; Meningitis/Encephalitis
Interventions: DeepDoc AI-based Decision Support System
Enrollment: 4000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
DeepDoc is an AI-based decision support system for the early etiology diagnosis of neurological diseases using clinical data points from patients admitted to hospital within 24 hours.This study aims to evaluate whether the diagnosis of the DeepDoc AI-based decision support system is better than the doctor's initial diagnosis by a multi-center, superiority diagnostic study.

Eligibility criteria:
Inclusion Criteria:

1. Patients with clinical symptoms or signs suspected of stroke，meningitis/encephalitis, vertigo and headache.
2. Neurological examination results and imaging changes within 24 hours of admission.

Exclusion Criteria:

Patients discharged, transferred to the hospital, or died within 24 hours after admission.

Outcomes:
Accuracy of diagnosis by DeepDoc AI-based Decision Support System
Be Yourself" Educational Intervention With Web Page and Chatbot to Promote Self-determined Motivation and Prevent Teenage Pregnancy: Pilot Trial.
Trial ID: NCT06824922
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-06
Completion date: 2025-06-06
Sponsor: Universidad Autonoma de Nuevo Leon
Conditions: Prevenir el Embarazo Adolescente
Interventions: "Be yourself", educational intervention with chatbot to promote self-determined motivation and prevent adolescent pregnancy: pilot trial protocol; Conventional Sex Education
Locations: Autonomous University of Nuevo Leon
Countries: Mexico
Enrollment: 150
Age range: 11 Years - 15 Years
Sex: ALL

Summary:
Introduction: Adolescent pregnancy and early initiation of sexual activity is a serious global public health problem, every year, around sixteen million adolescents between 15 and 19 years of age give birth, representing 11% of all births globally. Objective: To design a protocol of a pilot Randomized Controlled Trial to evaluate the preliminary efficacy of the "Be Yourself" intervention to increase self-determined motivation to prevent pregnancy in adolescents in secondary education. Methods: The protocol was developed based on the Protocol Items: ecommendations for Interventional Trials 2013 (SPIRIT2013) Statement, in the City of Monterrey, Nuevo León, Mexico in 2023. Results: The protocol outlined the design of the intervention, structured the content of the sessions using a logic model of intervention, and developed manuals for program facilitators and guides for participants. In addition, a website and a chatbot with Artificial Intelligence were developed, and the strategy for the application of the evaluation instruments was established. Conclusion: The educational intervention "Be yourself" is projected as a promising program to promote self-determined motivation and prevent adolescent pregnancy.

Eligibility criteria:
Inclusion Criteria:

* Adolescents between 11 and 15 years of age enrolled in public high schools in Nuevo León will be included.

public high schools in Nuevo León.

* Be male or female, regardless of whether they have initiated their sexual life. sexually active.
* Have access to a computer room with internet to use the web page and the interventions chatbot.

the interventions chatbot.

* Live at the same address with their parents or legal guardian.

Exclusion Criteria:

* Have previously participated in a similar sexuality intervention.
* Have cognitive or developmental difficulties that impede their understanding of the intervention and use of the technology platform, including people with
* Autism Spectrum Disorder (ASD) at levels that affect functional communication, Attention Deficit Hyperactivity Disorder (ADHD) with severe attention difficulties, or those who cannot read and write.

Outcomes:
Increased rates of Self-Determined Motivation to prevent teenage pregnancy; Increase in the Level of Knowledge about Pregnancy Prevention; Improving Communication on Sexual Issues; Change in Life Goals; Satisfaction of Basic Psychological Needs: autonomy, relationships and competence.
Impact of the Artificial Intelligence (Machine Learning) in a Telemonitoring Programme of COPD Patients With Multiple Hospitalizations (telEPOC)
Trial ID: NCT04978922
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-01-01
Completion date: 2022-06-01
Sponsor: Dr. Cristobal Esteban
Collaborators: Osakidetza
Conditions: COPD
Interventions: Machine Learning: ML (Artificial Intelligence System)
Locations: Hospital Galdakao Usansolo
Countries: Spain
Enrollment: 345
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
Given the current situation concerning healthcare, population demographics and economy, it seems required to look for new approaches in the health system. The use of new technologies must be the main factor for this change.

GENERAL OBJECTIVE:

To determine the impact that the application of an artificial intelligence system (Machine Learning) could have on an active telemonitoring programme of readmitted COPD patients.

Particular objectives: to determine the changes in:

* The use of healthcare resources.
* Patients´ quality of life.
* Costs.
* Load of work.
* Daily clinical practice.
* Inflammation markers

METHODS:

Based on the telEPOC programme and Machine Learning developement in this project, non-randomized intervention study, with two branches: intervention (Galdakao hospital) and control (Cruces and Basurto hospital).

Sample size of at least 115 patients per hospital (115 in the intervention branch and 230 in the control branch). A 2-year follow-up.

Uni and multivariate statistics will be applied.

Eligibility criteria:
Inclusion Criteria:

* Having a COPD (COPD was confirmed if the post-bronchodilator forced expiratory volume in one second (FEV1) divided by the forced vital capacity (FVC) was less than 0.7 (FEV1/FVC\<70%)
* Having been admitted at least twice in the previous year or three times in the two previous years for a COPD exacerbation (eCOPD).

Exclusion Criteria:

* Another significant respiratory disease.
* An active neoplasm.
* A terminal clinical situation.
* Inability to carry out any of the measurements of the project.
* Unwillingness to take part in the study.

Outcomes:
Number of resources after the implementation of ML (Machine Learning) added to a telemedicine system in readmitted COPD patients (telEPOC).; Change in quality of life in patients after the implementation of ML (in patients that generate alarms); Changes in quality of life in patients after the implementation of ML (in patients that generate alarms); Cost of the implementation of ML in relation to the standard telemonitoring programmes; Workload of nurses; Changes in clinical diary practice after including ML; Change in clinical diary practice after including ML
Development of an Artificial Intelligence Solution to Evaluate Clinical Activity of Thyroid Associated Orbitopathy Using Periocular Selfie Images Taken by Smartphones in Patients With Thyroid Dysfunction
Trial ID: NCT05731154
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-12-22
Completion date: 2022-07-25
Sponsor: Seoul National University Bundang Hospital
Collaborators: THYROSCOPE INC.
Conditions: Thyroid Eye Disease
Interventions: taking periorbital selfie images using smartphones
Locations: Seoul National University Bundang Hospital
Countries: South Korea
Enrollment: 100
Age range: 19 Years - None
Sex: ALL

Summary:
This study was conducted to develop an AI solution to evaluate clinical activity score (CAS) of thyroid associated orbitopathy (TAO) using periocular selfie images taken by smartphones in patients with thyroid dysfunction.

An AI solution to evaluate CAS in TAO was developed in previous retrospective study using periorbital digital images taken by DSLR camera in the studio. This AI solution will be adjusted to show its best performance with periocular selfie images taken by smartphones.

Eligibility criteria:
Inclusion Criteria:

* Patients with thyroid eye disease

Exclusion Criteria:

* Patients who have prior facial anomaly or surgery
* Patients who cannot use smartphone camera app

Outcomes:
Accuracy for clinical activity score (CAS) of thyroid eye disease (TED); Diagnostic accuracy for active thyroid eye disease
Impact of Artificial Intelligence-based Patient Reinforcement on Quality of Colonoscopy
Trial ID: NCT05041283
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-05-21
Completion date: 2022-02-01
Sponsor: University of Ulm
Conditions: Bowel Preparation
Interventions: Chatbot; Phone call
Locations: Specialist Practice Profes. Dikopoulos/Ludwig; University of Ulm, Interdisciplinary Endoscopy; Specialist Practice
Countries: Germany; Germany; Germany
Enrollment: 258
Age range: 18 Years - None
Sex: ALL

Summary:
In order to improve bowel preparation for colonoscopy and consequently enhance detection rate of malignant and premalignant findings, a prospective, randomized and controlled three-arm study was developed. Patients who undergo ambulatory colonoscopy are randomly assigned into a control group with standard preparation, a phone call supported preparation group or a group supported by an artificial intelligence based chatbot. Primary endpoint is defined as quality of bowel preparation (Boston Bowel Preparation Score), secondary endpoints are patients satisfaction, comprehensiveness of bowel preparation, sedation dose, rate of coecal intubation and the rate of adenoma and polyp detection, anxiety referred to colonoscopy and patients satisfaction with preparation support.

Eligibility criteria:
Inclusion Criteria:

* informed consent
* smartphone with access to WhatsApp
* indication for ambulatory colonoscopy
* ASA I or II

Exclusion Criteria:

* no informed consent given or possible
* no access to a smartphone with WhatsApp
* \<18 years old
* Pregnancy/Lactation
* Allergy to Moviprep©
* ASA (American Society of Anesthesiologists) state \>II
* extended abdominal surgery in past history
* no indication for ambulatory colonoscopy

Outcomes:
Quality of bowel preparation
Gait Training in Above Knee Amputation Patients Using Artificial Intelligence
Trial ID: NCT07265154
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-01
Completion date: 2026-03-01
Sponsor: South Valley University
Collaborators: Badr University; Benha University; Al Salam University
Conditions: Above Knee Amputation; Amputation
Interventions: Artificial Intelligence
Locations: Faculty of Physical Therapy,
Countries: Egypt
Enrollment: 40
Age range: 16 Years - 50 Years
Sex: ALL

Summary:
Effect of Using Artificial Intelligence on Gait Training in Above Knee Amputation Patients

Eligibility criteria:
Inclusion Criteria:

* All patients have above-knee amputations.
* All patients have no other neurological diseases affecting gait.
* All patients have no other neurological diseases affecting muscle balance.
* All patients have no other complications affecting joint ROM.

Exclusion Criteria:

* Patients have any complications affecting joint ROM.
* Patients have any neurological condition affecting muscle balance.
* Patients have any neurological condition affecting gait.

Outcomes:
Medio-lateral Stability Index (MLSI); Anterior-posterior Stability Index (APSI); Overall Stability Index (OSI)
Development and Prospective Evaluation of a Machine Learning Model to Predict Postoperative Respiratory Failure
Trial ID: NCT04527094
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-05-26
Completion date: 2022-06-25
Sponsor: Seoul National University Hospital
Conditions: Noncardiac Surgery
Interventions: Prediction of postoperative respiratory failure using a machine learning
Locations: Hyun-Kyu Yoon
Countries: South Korea
Enrollment: 22250
Age range: 18 Years - None
Sex: ALL

Summary:
The main objective of this study is to develop a machine learning model that predicts postoperative respiratory failure within 7 postoperative day using a real-world, local preoperative and intraoperative electronic health records, not administrative codes.

Eligibility criteria:
Inclusion Criteria:

* Adults patients undergoing general anesthesia for noncardiac surgery

Exclusion Criteria:

* Age under 18 years
* Surgery duration \< 1 hr
* Cardiac surgery
* Surgery performed only regional or local anesthesia, peripheral nerve block, or monitored anesthesia care
* Organ transplantation
* Patient with preoperative tracheal intubation
* Patients who had tracheostoma prior to surgery
* Patients scheduled for tracheostomy
* Surgery performed outside the operating room
* Length of hospital stay \< 24 h

If the patients had multiple surgeries during the same hospital stays, we included the first surgical cases in the dataset.

Outcomes:
the incidence of postoperative respiratory failure after general anesthesia
Performance of Artificial Intelligence in Colonoscopy for Right Colon Polyp Detection: a Randomized Controlled Trial
Trial ID: NCT06216405
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-01-20
Completion date: 2024-01-03
Sponsor: Groupe Hospitalier Diaconesses Croix Saint-Simon
Collaborators: Cosmo Artificial Intelligence-AI Ltd
Conditions: Colon Cancer; Colon Polyp; Colon Lesion; Colon Adenocarcinoma
Interventions: Colonoscopy
Locations: Groupe Hospitalier Diaconesses Croix Saint-Simon
Countries: France
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of the study is to assess whether the use of artificial intelligence improves polyp detection in a segment of the colon (the right colon).

To achieve this objective, patients will be divided into two groups: one will undergo a standard colonoscopy, the other a colonoscopy with the artificial intelligence software connected to the machine.

This software does not modify the colonoscopy technique in any way, and does not require the administration of any product to the patient.

The study will compare the detection rate of right colon polyps between the group of patients who underwent standard colonoscopy and those who underwent colonoscopy with artificial intelligence. If this number does not differ between the two groups, the investigators can conclude that there is no point in using artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* Age\>18 years
* Signed informed consent
* Affiliated to or beneficiary of a social security scheme

Exclusion Criteria:

* Patients with history of inflammatory bowel diseases
* Patients with history of familial polyposis syndrome
* Patients with history of right colonic resection.
* Pregnant or breastfeeding women
* Patient benefiting from a legal protection measure : tutorship, curatorship, patient deprived of freedom
* Adults who are unable to give their consent
* People under psychiatric care who are not able to understand the aim if this research

Outcomes:
Comparing the percentage of patients with at least one right colon polyp detected according to the randomization group, and adjusted for Boston Bowel preparation scale, for the operator, for the endoscope and for stratification factors.
An Artificial Intelligence Assessment System for Risk Grading of Sarcopenia Based on Ultrasonographic Multidimensional Data
Trial ID: NCT06199856
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-10-01
Completion date: 2028-12
Sponsor: West China Hospital
Conditions: Sarcopenia; Ultrasound
Interventions: ultrasound scan
Locations: Xinyi Tang
Countries: China
Enrollment: 1500
Age range: None - 100 Years
Sex: ALL

Summary:
1. To develop an artificial intelligence assisted diagnostic model for sarcopenia based on ultrasound images;
2. To develop artificial intelligence classification and regression models for auxiliary diagnosis of sarcopenia, patient strength estimation, and other functions based on ultrasound image data.

Eligibility criteria:
Inclusion Criteria:

* \> 50 years of age
* Patients with suspected sarcopenia, for example, who needed assistance with walking, rising from a chair, or climbing stairs; recently had a history of falls walking; recent unintentional weight loss

Exclusion Criteria:

* Amputated arm or leg
* Severe oedema (oedema higher than knee level)
* Implantable pacemaker
* Impaired consciousness, poor general health, or other reasons that would prevent the individual from completing the study

Outcomes:
Death; Diagnosed sarcopenia
A Blinded, Self-control Trial to Evaluate an Artificial Intelligence Based CAD System for Diabetic Retinography
Trial ID: NCT03973762
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-05-31
Completion date: 2020-08-15
Sponsor: Peking Union Medical College Hospital
Collaborators: Peking University People's Hospital; Beijing Tongren Hospital; Chinese PLA General Hospital
Conditions: Diabetic Retinopathy
Interventions: DR Grading with CAD; DR Grading by expert panel
Locations: Peking Union Medical College Hospital
Countries: China
Enrollment: 1081
Age range: None - None
Sex: ALL

Summary:
To evaluate the safety and performance of an innovative artificial intelligence based Computer-Aided Diagnosis(CAD) system for diabetic retinography, Retinal images of patients with diabetes mellitus or diabetic retinopathy(DR) were collected retrospectively. All images were graded by a retinal specialists expert panel and the CAD device using the International Clinical Diabetic Retinopathy severity scale criteria. Investigator responsible for DR grading by CAD system is blinded to the DR grading results from the expert panel. Finally, DR grading results of the CAD system and experts were compared using sensitivity and specificity.

Eligibility criteria:
Inclusion Criteria:

* Clinical history of diabetes mellitus or diabetic retinopathy;
* Fully Gradable Images;
* around 45° field which covers optic disc and macula;
* complete patient identification information;

Exclusion Criteria:

* incomplete patient identification information

Outcomes:
Se and Sp under investigation target 3
Development of an AI Assessment System for Pediatric Respiratory Distress : A Prospective Study
Trial ID: NCT07370623
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-28
Completion date: 2027-12
Sponsor: Samsung Medical Center
Conditions: Pediatric Respiratory Distress
Locations: CHA Bundang Medical Center, CHA University, 9, Yatap-ro, Bundang-gu; Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu; Samsung Medical Center, 81, Irwon-ro, Gangnam-gu
Countries: South Korea; South Korea; South Korea
Enrollment: 2200
Age range: 0 Years - 12 Years
Sex: ALL

Summary:
This is a multicenter, prospective observational study designed to collect clinical data for the development of a vision-language model-based artificial intelligence system for automated assessment of pediatric respiratory patterns.

The study enrolls pediatric patients aged 0 to 12 years who present to the pediatric emergency departments of participating institutions. Clinical and visual respiratory data are collected along with baseline clinical characteristics, including sex, age, body weight, height, presenting symptoms recorded at emergency department arrival, initial vital signs (body temperature, pulse rate, respiratory rate, blood pressure, and oxygen saturation), severity at presentation assessed by the Korean Triage and Acuity Scale (KTAS), emergency department management and outcomes such as hospital admission or discharge, and other relevant clinical information.

These data are used for cohort characterization and for the development and evaluation of an AI-based system that aims to automatically analyze pediatric respiratory patterns and support objective respiratory assessment in pediatric emergency care.

Eligibility criteria:
Inclusion Criteria:

* Pediatric patients aged 0 to 12 years who present to participating pediatric emergency departments.
* Patients for whom clinical data, including basic demographic characteristics (e.g., age, body weight), severity at presentation (e.g., KTAS level), vital signs, emergency department management and outcomes, as well as visual respiratory data, are available during emergency care.

Exclusion Criteria:

* Patients outside the specified age range.
* Patients with insufficient or poor-quality clinical or visual respiratory data.
* Patients whose data cannot be used due to withdrawal of consent or regulatory restrictions.

Outcomes:
Accuracy of automated respiratory pattern assessment