moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases Depending on Tongue Images
Trial ID: NCT04811599
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-03-21
Completion date: 2022-06-01
Sponsor: Shandong University
Conditions: Gastrointestinal Disease
Locations: Qilu Hospital, Shandong University
Countries: China
Enrollment: 2000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The purpose of this study is to analysize the relationship between the characteristics of tongue image and the diagnosis of gastrointestinal diseases , then develop and validate a deep learning algorithm for the diagnosis of gastrointestinal diseases depending on tongue images, so as to improve the objectiveness and intelligence of tongue diagnosis. At the same time, gastrointestinal flora of common tongue images were analyzed in order to provide a microecological basis for understanding the relationship between tongue images and digestive tract diseases.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 - 80 years undergoing endoscopic examination;patients gave informed consent and signed informed consent.

Exclusion Criteria:

\-

Outcomes:
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm
Stockholm Mammography Risk Stratified Trial
Trial ID: NCT06270355
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: PHASE3
Start date: 2024-04-08
Completion date: 2032-04-08
Sponsor: Karolinska Institutet
Conditions: Breast Cancer
Interventions: Risk based screening
Enrollment: 70000
Age range: 40 Years - 74 Years
Sex: FEMALE

Summary:
SMART is a Phase III, randomised, non-blinded screening trial. Women who consent to participate in SMART are randomised to either individualised screening or screening according to the Swedish National Breast Cancer Screening Program. Women randomised to individualised screening get their 2-year risk of breast cancer assessed and women with the highest short-term risk are offered contrast enhanced mammography and a mammogram at 12 months.

Eligibility criteria:
Inclusion Criteria:

* Have read, understood and signed the informed consent
* Perform breast cancer screening at Södersjukhuset
* Females in the age range 40 - 74 years at baseline mammography
* Using a smartphone or tablet with BankID

Exclusion Criteria:

* Pregnant, breast feeding or planning to become pregnant.
* Previous breast cancer
* A medical condition that according to the responsible physician would negatively affect the health of the women.
* Cognitive impairment and / or language ability that would make it difficult for the participant to understand the implication of study and to perform a contrast mammography.

For women that will be offered contrast enhanced mammography the clinically accepted and applied exclusion criteria will be used. Women who cannot perform a contrast mammography will be offered an automated ultrasound.

Outcomes:
Incidence of interval cancers
TIMELY-PrädiktoR Prospective Study
Trial ID: NCT05461729
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-06-01
Completion date: 2029-08-31
Sponsor: Dr. Boris Schmitz
Collaborators: European Commission; University of Amsterdam; Technische Universität Dresden; MEDIZINISCHE UNIVERSITAT GRAZ; Stichting Katholieke Universiteit; Foundation for Research and Technology - Hellas
Conditions: Coronary Artery Disease
Locations: Klinik Königsfeld
Countries: Germany
Enrollment: 1200
Age range: 18 Years - None
Sex: ALL

Summary:
Brief Summary:

Cardiac rehabilitation (CR) is a multi-factorial intervention, designed to limit the physiological and psychological effects of cardiovascular disease such as coronary artery disease (CAD), manage symptoms, and reduce the risk of future cardiovascular events. CR is a structured program not only addressing CAD but also comorbidities including hypertension, dyslipidemia, diabetes and obesity as well as other risk factors. CR aims at long-term lifestyle changes to reduce modifiable risk factors, and it's success depends on a large number of interacting variables including biological, psychological and social factors. Recently, the importance of patient-centered approaches to secondary prevention and CR success has been underlined but intra-individual factors and their interactions are not well understood. The TIMELY prospective study aims to collect high-resolution data for data mining and artificial intelligence machine learning models to identify dependencies between factors and predict favorable outcomes of CR. Data collection will include data documented during controlled center-based CR as well as remote-measurement of physical activity data, (central) blood pressure and pulse wave analysis as well as long-term ECG data during a 6-months period after discharge. Follow-up assessments will be performed at least at 6 months and at 12 months after discharge. Events (MACCE) will be collected for up to 10 years. After 2023, the study will be run under the acronym PrädiktoR.

Main objectives of the study:

1. To examine uptake and adherence to a healthy lifestyle (i. e. adherence to CR guidelines in CAD) and the effects on long-term outcomes.
2. To identify potentially mediating mechanisms and predictive factors for long-term CR success in CAD, also to be used in machine learning models and for the generation of digital twins.
3. To investigate acceptance of different eHealth components as well as expectations and needs among CAD patents in CR.

Eligibility criteria:
Inclusion Criteria:

Coronary Artery Disease (CAD) patients after myocardial infarction (STEMI/NSTEMI) and/or angioplasty and/or Percutaneous Coronary Intervention (PCI) and/or coronary artery bypass graft surgery who participate in phase II Cardiac Rehabilitation (CR).

Exclusion Criteria:

* Incapability to give informed consent
* Conditions that prevent patients from participation in CR including unstable coronary or cerebrovascular conditions and acute infections

Outcomes:
Change in Cardiorespiratory Fitness (CRF); Change in risk of mortality; Number of patients with major adverse cardiac and cerebrovascular events (MACCE)
Prospective Clinical Data Collection for Developing Machine Learning-assisted Obstetric Ultrasound (MOBUS) Screening Algorithms
Trial ID: NCT06742229
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-20
Completion date: 2025-12-30
Sponsor: Caption Health, Inc.
Conditions: Pregnancy
Interventions: Research Ultrasound scan
Locations: University of Minnesota; Baylor College of Medicine
Countries: United States; United States
Enrollment: 700
Age range: 18 Years - None
Sex: FEMALE

Summary:
The goal of this clinical trial is to

* Collect ultrasound data from pregnant and non-pregnant individuals presenting to multiple study sites.
* Use the collected data and ultrasound images to train and validate Artificial intelligence algorithms developed by the Sponsor

Consented participants will be asked to take part in a research ultrasound scan

Eligibility criteria:
Inclusion Criteria:

Pregnant group (Group 1)

1. Participants aged 18 years or older at time of consent.
2. Participants with confirmed pregnancy (positive HCG urine test strip and/or a documented obstetric ultrasound scan.
3. Participants who are scheduled for or referred for a standard-of-care clinical obstetric ultrasound examination.
4. Participants who provide written consent.

Non-pregnant group (Group 2)

1. Participants aged 18 years or older at time of consent.
2. Participants who are not actively pregnant (confirmed using an HCG urine test strip).
3. Participants who provide written informed consent.

Exclusion Criteria:

\- 1. Participants for whom participating in this study would delay or compromise care in any way.

2\. Participants who are not able to understand or provide written consent.

Outcomes:
Research sweep Ultrasound Images (Vscan ) from the participants will be assessed for quality of images and comparing with patient chart history
Development of a 2D Video Gait Analysis Platform and Applying to Establish an Artificial Intelligent (AI) Model for the Adjustment of the Customized Shoes' Sole
Trial ID: NCT07003087
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2024-08-05
Completion date: 2026-08-30
Sponsor: China Medical University Hospital
Conditions: Gait Disorders
Interventions: Shoe's sole modification
Locations: China Medical University, Department of Physical Therapy
Countries: Taiwan
Enrollment: 50
Age range: 20 Years - 70 Years
Sex: ALL

Summary:
Artificial intelligence (AI) technology makes gait analysis based on two-dimensional images feasible. The first aim of this study is to develop an analysis platform that uses two-dimensional image analysis technology to determine the relative excursion of body segments and limbs in gait. The second aim is to apply this analysis platform to develop an artificial intelligence model for customized shoe adjustment to optimize gait. For the methodology, OpenPose will be used as the analysis tool to quantify the relative shifts between specific joint/node trajectories of the body, and the VICON three-dimensional motion analysis system will be used to verify the acquisition parameters. For the second aim, an existing 2D gait image database before customized shoe adjustment will be used to obtain the inputs to the customized shoe adjustment AI model. Through training the AI model, enabling to generate the outputs of the most appropriate adjustment parameters for shoe soles. After the AI model has well-trained, the walking kinematics and foot pressure before and after wearing ordinary sports shoes or customized shoes will be compared during running on a treadmill. This research is expected to establish an efficient customized shoe adjustment AI model, allowing users to improve walking efficiency.

Eligibility criteria:
Inclusion Criteria:

* Self-percieved discomforts in lower extremity
* Be able to walk independently at least for 30 minutes and run independently at least for 10 minutes

Exclusion Criteria:

* Obvious foot deformity, trauma in the lower extremity
* Motor or neurologic disorder that affect walking patterns

Outcomes:
kinemaitc asymmetry
An Accessible and Inclusive Artificial Intelligence Assisted Chronic Disease Self-Management Telehealth Platform: A Pilot Study
Trial ID: NCT05481593
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-01
Completion date: 2025-10-31
Sponsor: University of Alabama at Birmingham
Conditions: Disability Physical; Chronic Condition; Chronic Conditions, Multiple
Interventions: Scheduled Coaching + Gamified Rewards; Scheduled Coaching Calls + Independent Rewards; No Scheduled Coaching Calls + Gamified Rewards; No Scheduled Coaching Calls + Independent rewards
Locations: University of Alabama at Birmingham
Countries: United States
Enrollment: 212
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study to pilot test an accessible and inclusive artificial intelligence (AI)-assisted, individualized, family-focused lifestyle modification intervention (AI4CHRON) for health-related quality of life for adults with impaired mobility and chronic medical conditions.

Eligibility criteria:
Inclusion Criteria:

* 18 years or older
* Diagnosis of heart disease, chronic lung disease, or Type 2 diabetes
* Living with a permanent physical disability (i.e., spina bifida, multiple sclerosis, stroke,)
* Have the ability to converse in and read English
* Availability of a smartphone or computer

Exclusion Criteria:

* Current enrollment in any structured intervention
* Severe cognitive impairment
* Severe untreated depression in the past 6 months
* Major cardiac event in the past 12 months
* Uncontrolled blood pressure
* Resting tachycardia
* Renal failure
* Severe peripheral neuropathy
* Active treatment for cancer in the past 12 months
* Having both visual and hearing impairments (either of them is fine)

Outcomes:
Health-related quality-of-life
Artificial Intelligence to Predict Surgical Outcomes and Assess Pain Neuromodulation in Trigeminal Neuralgia Subjects
Trial ID: NCT05810428
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-06
Completion date: 2027-04-30
Sponsor: IRCCS San Raffaele
Conditions: Trigeminal Neuralgia; Trigeminal Nerve Diseases; Virtual Reality; Artificial Intelligence; Radiosurgery; Magnetic Resonance Imaging
Interventions: GKRS-VR training; Control Group
Locations: IRCCS San Raffaele
Countries: Italy
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
Trigeminal neuralgia (TN) is the most common cause of facial pain. Medical treatment is the first therapeutic choice whereas surgery, including Gamma Knife radiosurgery (GKRS), is indicated in case of pharmacological therapy failure. However, about 20% of subjects lack adequate pain relief after surgery. Virtual reality (VR) technology has been explored as a novel tool for reducing pain perception and might be the breakthrough in treatment-resistant cases. The investigators will conduct a prospective randomized comparative study to detect the effectiveness of GKRS aided by VR-training vs GKRS alone in TN patients. In addition, using MRI and artificial intelligence (AI), the investigators will identify pre-treatment abnormalities of central nervous system circuits associated with pain to predict response to treatment. The investigators expect that brain-based biomarkers, with clinical features, will provide key information in the personalization of treatment options and bring a huge impact in the management and understanding of pain in TN.

Eligibility criteria:
Inclusion Criteria:

* diagnosis of TN according to the International Classification of Headache Disorders, third edition (ICHD-3) criteria;
* age \>18 years;
* indication to undergo radiosurgical treatment;
* willingness and ability to comply with scheduled visits and other trial procedures.

Exclusion Criteria:

* Any person unable to lie still within the environment of the MRI scanner for the required period to perform the study and those where MRI scanning is contraindicated;
* metal implants, pacemaker, etc.;
* Pregnancy or breastfeeding;
* Any significant psychiatric disease;
* Use of illicit drugs;
* Brain pathology shown by brain MRI and/or neurophysiological examination;
* Any person unable to understand and follow the instructions of the investigators;
* Any other condition according to the Investigator would make the subject unsuitable for the study.

Outcomes:
Numeric Rating Scale for pain (NRS) changes; Barrow Neurological Institute (BNI) pain score changes
Real-World Experience of Artificial Intelligence-Assisted Colonoscopy in Colorectal Cancer Screening in a General Hospital: A Single-Center Cohort Phase IV Study
Trial ID: NCT06792292
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-01
Completion date: 2027-06-01
Sponsor: Chirec
Conditions: Artificial Intelligence; Colonic Adenoma; Colonic Neoplasms; Colonic Polyp; Colonoscopy
Interventions: conventional colonoscopy procedure; artificial intelligence-assisted colonoscopy procedure
Locations: Hopital Delta Chirec
Countries: Belgium
Enrollment: 765
Age range: 45 Years - 74 Years
Sex: ALL

Summary:
Cancer can develop in the colon, or large bowel. Examination of the colon with a tube fitted with a camera is called a colonoscopy.

Colonoscopy allows detection of small growths in the colon, called "polyps". Polyps can often be removed during colonoscopy. Some of these polyps are called adenomas and can become cancer after several years.

A good colonoscopy aims to find and take out as many of these polyps as possible.

A quality indication of colonoscopy is the "adenoma detection rate" (ADR). It should be high, meaning many polyps are detected and taken out.

New artificial intelligence devices to assist colonoscopy seem to increase the ADR, and maybe help prevent cancer even better than normal colonoscopy.

The goal of this clinical trial is to compare the ADR when using standard colonoscopy to the ADR with artificial intelligence (AI)-assisted colonoscopy.

Eligibility criteria:
Inclusion criteria:

* Patient (woman or man) candidate for a screening colonoscopy - Age: 45 to 74 years included
* Absence of inflammatory bowel disease
* Absence of significant digestive symptoms indicating colonoscopy (i.e. screening is the only indication for the examination)
* Patient able to understand the concept of the study and agreeing to participate

Exclusion criteria:

* Patient outside the inclusion age
* All exclusion criteria for a colonoscopy.
* The indication for colonoscopy is not simple screening; for example, assessment of anemia, rectal bleeding, weight loss or abdominal pain.
* Patient's refusal to participate, or patient's inability to understand the study concept
* Any patient with major psychological or psychiatric disorders.

Outcomes:
Adenoma Detection Rate in Conventional versus Artificial Intelligence-Assisted Colonoscopy
Accuracy of an Artificial Intelligence-assisted Diagnostic System for Caries Diagnosis: a Prospective Multicenter Clinical Study
Trial ID: NCT06428344
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-08-01
Completion date: 2027-06-01
Sponsor: Zhejiang Provincial People's Hospital
Conditions: Dental Caries; Artificial Intellegence; Diagnosis; Machine Learning
Enrollment: 220
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
This clinical trial was designed as a prospective, multicenter, multi-reader multi-case (MRMC), superiority, parallel-controlled study. Participants who met the trial criteria and signed the informed consent form were enrolled. The trial group involved diagnoses of caries on panoramic radiographs using an artificial intelligence-assisted diagnostic system, while the control group involved diagnoses made by dental practitioners specializing in operative dentistry and endodontics with five years of experience, who interpreted oral panoramic radiographs to determine the presence and severity of caries.

Eligibility criteria:
Inclusion Criteria:

\-

Inclusion Criteria:

1. Patients presenting with clinical manifestations of caries as their chief complaint;
2. Age ≥18 and ≤70 years, irrespective of gender;
3. Oral panoramic radiographs showing a complete dentition, specifically with the second molars and all premolars intact in each quadrant;
4. On the oral panoramic radiographs, the number of teeth with restorations or fillings does not exceed one in any quadrant;
5. Oral panoramic radiographs that are clear, easy to interpret, and free from significant artifacts;
6. Participants who voluntarily agree to partake, can comprehend the purpose of the study, and are capable of signing an informed consent form.

Exclusion Criteria:

1. Oral panoramic radiographs that are unclear, with overlapping, blurring, or artifacts present;
2. Insufficient number of teeth available for study;
3. Severe tooth wear or erosion leading to significant alteration in tooth morphology;
4. Presence of supernumerary teeth, microdontia, or missing teeth;
5. Conditions not suitable for oral radiography, such as pregnancy or undergoing radiation therapy for tumors;
6. Limited mouth opening that precludes clinical examination;
7. Neurological disorders, psychiatric illnesses, or psychological impairments;
8. Participation in another clinical trial within the last three months;
9. Any other condition deemed by the researchers as unsuitable for inclusion in the study.

Outcomes:
Sensitivity, specificity, and accuracy of caries diagnosis
Effects of Artificial Intelligence Assisted Follow-up Strategy Based on a New Remote Contactless Sleep Monitoring System on Secondary Prevention in Patients Received Coronary Artery Bypass Grafting Surgery
Trial ID: NCT04636996
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-01-01
Completion date: 2022-12-31
Sponsor: Chinese Academy of Medical Sciences, Fuwai Hospital
Conditions: Coronary Heart Disease; Artificial Intelligence; Sleep Apnea
Interventions: artificial intelligence assisted follow-up
Locations: Cardiovascular Institute and Fuwai Hospital, CAMS&PUMC
Countries: China
Enrollment: 200
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
The present study is trying to find out whether artificial intelligence assisted follow-up strategy will improve secondary prevention in CABG patients. In addition, we will test whether rural patients may have more benefits under the new follow-up strategy based on the artificial intelligence device compared with urban patients.

Eligibility criteria:
Inclusion Criteria:

1. Age between 18-70, male or female
2. Received isolated CABG procedure due to coronary artery disease
3. Having a smartphone and are willing to cooperate
4. Signing written informed consent

Exclusion Criteria:

1. Patients undergoing CABG + valve replacement surgery
2. Postoperative hospital stays more than 10 days (Due to limited postoperative follow-up time and unstable condition such as incision infection, angina, etc.)
3. Patients with lung, liver, and kidney failure (Due to organ dysfunction which needs more professional health care and not suitable for this trial)
4. Patients with Urinary system diseases (Getting up during sleep, which will interfere with sleep monitoring)
5. Patients mentally or legally disabled
6. Breastfeeding or pregnant women
7. End-stage disease, estimated survival time is less than 3 months
8. Participate in other perioperative intervention studies at the same time

Outcomes:
Heart rate
Evaluating the Clinical Impact of Artificial Intelligence on Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial
Trial ID: NCT07186803
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: PHASE3
Start date: 2025-09-17
Completion date: 2026-07-30
Sponsor: University Health Network, Toronto
Conditions: Laparoscopic Cholecystectomy
Interventions: Artificial Intelligence Guidance Models
Locations: Toronto General Hospital; Toronto Western Hospital
Countries: Canada; Canada
Enrollment: 70
Age range: 18 Years - None
Sex: ALL

Summary:
Today, the majority of gallbladder removals surgeries are done using minimally invasive techniques through small cuts to help patients recover faster. However, these procedures are technically more challenging because surgeons have a restricted view of the patient's anatomy, which can increase the risk of serious complications. Artificial intelligence (AI) tools have been developed to guide surgeons during surgery and help them make safer decisions that reduce the risk of injury to the patient. This study will use a randomized controlled trial to compare outcomes between surgeries with AI assistance and standard procedures without AI.

Primary Objective: To determine whether the AI improves surgeons' ability to achieve the Critical View of Safety, a key step for safe gallbladder removal, compared to standard procedures.

Secondary Objectives:

* Determine whether the AI helps the surgeon perform more safe dissections compared to the standard procedures.
* Collect surgeon feedback on the use of AI during the procedure

Eligibility criteria:
Inclusion Criteria:

* Surgeon participants: Attending surgeons or fellows that perform laparoscopic cholecystectomy at University Health Network.
* Patients participants: Adults 18 years of age and over, scheduled for laparoscopic cholecystectomy surgery.

Exclusion Criteria:

* Surgeon participants: Anyone who is not a surgeon or fellow at University Health Network or that does not perform laparoscopic cholecystectomies.
* Patient participants: Any patient who is not having a laparoscopic cholecystectomy surgery.

Outcomes:
Critical View of Safety Achievement Rate