moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Non-Invasive Identification of Endometrial Cancer/Endometrial Atypical Hyperplasia With an AI-Based Classifier Applied to Transvaginal Ultrasound in Patients With Post-Menopausal Bleeding
Trial ID: NCT06365905
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-03-27
Completion date: 2027-06-30
Sponsor: Mayo Clinic
Conditions: Endometrial Carcinoma
Interventions: Non-Interventional Study
Locations: Mayo Clinic in Arizona; Mayo Clinic in Florida; Mayo Clinic in Rochester
Countries: United States; United States; United States
Enrollment: 300
Age range: 55 Years - None
Sex: FEMALE

Summary:
This study evaluates if AI can be used with transvaginal ultrasound images for early detection of endometrial cancer or premalignant lesions.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 55 years
* At least one episode of postmenopausal bleeding (PMB)
* Endometrial biopsy available from at Mayo Clinic (MN, AZ, FL), Mayo Clinic Health System (MCHS), or an external institution
* Acceptance to participate in the study

Exclusion Criteria:

* Diagnostic tests performed more than 90 days apart
* Physical impediment/refusal to undergo transvaginal ultrasound (TVUS)

Outcomes:
Accuracy of artificial intelligence (AI) algorithm applied to transvaginal ultrasound (TVUS)
Effects of AI Based Physiotherapy on Mandibular Mobility Shoulder Function Whole Body Physical Function Quality of Life and Return to Work in Oral Cancer Patients: 3- and 9-Month Postintervention Follow-Up
Trial ID: NCT07237672
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-06
Completion date: 2027-12
Sponsor: National Taiwan University Hospital
Conditions: Oral Cancer
Interventions: AI-based physiotherapy follow-up assessments; conventional physiotherapy follow-up assessments
Locations: School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan
Countries: Taiwan
Enrollment: 65
Age range: 20 Years - 70 Years
Sex: ALL

Summary:
This follow-up clinical study aims to evaluate the sustained effects of two rehabilitation programs (AI-based physiotherapy vs. conventional physiotherapy) for oral cancer patients. Participants who completed the original intervention trial (ClinicalTrials.gov ID: 202411044RINC) will be assessed at 3 and 9 months post-intervention. Outcomes include mandibular mobility, shoulder joint range of motion, functional performance, quality of life, pain, and return-to-work status.

Eligibility criteria:
Inclusion Criteria:

* Participants who completed the intervention phase of the previous trial (202411044RINC)
* Age 20-65 years
* Able to attend follow-up assessments at 3 and 9 months

Exclusion Criteria:

* Withdrawal from the original trial
* Cancer recurrence or new cancer-related treatment interfering with functional assessment
* Unable to communicate or comply with follow-up visits

Outcomes:
Disabilities of the Arm, Shoulder, and Hand outcome questionnaire; EORTC QLQ-C30 questionnaire
Evaluating ChatGPT-4 as a Decision-Making Support Tool for Surgical Trainees
Trial ID: NCT06921447
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-04-10
Completion date: 2025-04-30
Sponsor: Ospedali Riuniti Trieste
Conditions: Artificial Intelligence (AI); Training; Surgery
Interventions: Clinical Cases
Locations: University of Trieste
Countries: Italy
Enrollment: 35
Age range: None - None
Sex: ALL

Summary:
This study aims to assess whether ChatGPT-4 can support surgical trainees in clinical decision-making. By comparing the performance of ChatGPT-4 with junior residents, senior residents, and attending surgeons on standardized clinical scenarios, the study seeks to understand the potential role of large language models in surgical education. The ultimate goal is to evaluate whether ChatGPT-4 can be safely integrated as a supplementary educational tool to aid junior residents in developing critical thinking and surgical judgment.

Eligibility criteria:
Inclusion Criteria:

* Actively enrolled or employed in the general surgery residency or department at the participating institution
* Willingness to participate and complete all clinical case scenarios
* Consent to participate in the study

Exclusion Criteria:

* Incomplete responses
* Use of external assistance (e.g., internet search, AI tools) when answering scenarios, as self-reported in instructions

Outcomes:
Proportion of correct responses
Randomized, Controlled Trial of a Computer Aided Detection Device in Standard Colonoscopy
Trial ID: NCT04754347
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-01-22
Completion date: 2021-10-31
Sponsor: Iterative Scopes, Inc
Collaborators: University of Minnesota
Conditions: Colorectal Polyp
Interventions: Computer Aided Detection device
Locations: Massachusetts General Hospital; Boston Medical Center; Mount Auburn Hospital; MNGI Digestive Health; Concord Endoscopy Center
Countries: United States; United States; United States; United States; United States
Enrollment: 1472
Age range: 40 Years - None
Sex: ALL

Summary:
The aim of this study is to evaluate the clinical benefit and safety of using a computer aided detection device, Skout, for real-time polyp detection in colonoscopy procedures with the indication of screening or surveillance. One of the reasons for the development of interval colorectal cancers is polyps missed during colonoscopy. Our hypothesis is that with the aid of Skout, the adenomas detected per colonoscopy will increase, and it is plausible to believe that this increase in detection could reduce the incidence of interval cancers.

Eligibility criteria:
Inclusion Criteria

Participants will be eligible for this study if they are:

* Undergoing colonoscopy with screening, surveillance, or diagnostic indications.
* Undergoing a procedure by a participating endoscopist.
* Have given informed consent.

Exclusion Criteria

Participants will not be eligible for this study if they:

* Have a history of inflammatory bowel disease.
* Have a history of familial adenomatous polyposis.
* Are under the age of 40.
* Have had a colonoscopy within the previous three (3) years.
* Undergoing diagnostic colonoscopy with high-risk indications including iron deficiency anemia, abnormal CT imaging, unexplained weight loss, Lynch Syndrome, blood in stool or FIT positive test.
* Entered with poor bowel preparation (inadequate for procedure as assessed by the Investigator).
* Use anti-platelet agents or anticoagulants that preclude the removal of polyps during the procedure.

Outcomes:
Adenomas per colonoscopy; Positive predictive value
Multimodality Imaging (Cardiovascular Magnetic Resonance Imaging, Echocardiography, and Nuclear Medicine Imaging) in the Screening, Diagnosis and Risk Stratification of Heart Failure With Preserved Ejection Fraction- a Multicenter Study.
Trial ID: NCT04602338
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-11-01
Completion date: 2028-06
Sponsor: Chinese Academy of Medical Sciences, Fuwai Hospital
Collaborators: Peking Union Medical College Hospital; Beijing Anzhen Hospital; China-Japan Friendship Hospital; Guangdong Provincial People's Hospital
Conditions: Heart Failure, Diastolic; Multicenter Study
Locations: Fuwai Hospital
Countries: China
Enrollment: 1000
Age range: None - None
Sex: ALL

Summary:
The incidence of Heart failure with preserved ejection fraction (HFpEF) in Heart failure patients increases rapidly. However, the current clinical awareness is insufficient, and the cardiac structural and functional injury are not well understood. It is difficult to recognize the subclinical changes of the cardiac in the early stage with conventional imaging techniques, and it is common to ignore the existence of the clinical alterations. This study aimed to investigate the cardiac features, early diagnosis and risk factors of HFpEF patients, based on the multi-modality (Magnetic resonance imaging- nuclear medicine imaging- echocardiography) imaging and multicenter study, combined with large data and artificial intelligence. This study will provide deep insights into the HFpEF in multicenter population.

Eligibility criteria:
Inclusion Criteria:

* left ventricular ejection fraction (LVEF)≥50%；
* N-terminal pro-b type natriuretic peptide (NT-proBNP)\>220pg/ml or b type natriuretic peptide (BNP) \>80 pg/ml;
* symptoms and syndromes of heart failure;
* At least one criteria of cardiac structure (left ventricular hypertrophy, or left atrial enlargement) and function abnormalities (based on tissue doppler, color doppler).

Exclusion Criteria:

* Special types of cardiomyopathy, including hypertrophic cardiomyopathy, restricted cardiomyopathy, etc.
* Infarction, myocardial fibrosis caused by ischemic cardiomyopathy and acute coronary syndrome ;
* Severe arrhythmia;
* Severe primary cardiac valvular disease;
* Restrictive pericardial disease;
* Refuse to participate in the study.

Outcomes:
All-cause Death; Cardiovascular Death; Hospitalization Due to Heart Failure
Pivotal Trial of an Automated AI-based System for Early Diagnosis and Prediction of Late Age Related Macular Degeneration in Primary Care Settings.
Trial ID: NCT04863391
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-07-19
Completion date: 2022-12-31
Sponsor: The New York Eye & Ear Infirmary
Collaborators: iHealthScreen Inc
Conditions: Age Related Macular Degeneration
Interventions: Referrable versus Non Referral AMD diagnostic test
Locations: New York Eye and Ear Infirmary of Mount Sinai
Countries: United States
Enrollment: 1000
Age range: 50 Years - None
Sex: ALL

Summary:
This study seeks to evaluate a system for the automated early detection of Age-Related Macular Degeneration (AMD). AMD is a condition in which there is breakdown of the macula of the eye, the part of the retina that is responsible for sharp, central vision. We will take pictures of subjects' eyes using an automated camera. These photographs will be securely transmitted and and then analyzed by a computer program which has been developed in other studies. The outcome of the computer program analysis will be compared with human analysis of these same pictures. If the computer analysis is has good enough accuracy, then this computer system could be used for wide-scale screening for AMD.

Eligibility criteria:
Inclusion Criteria:

1. Subjects will be recruited if willing and able to comply with clinic visit and study-related procedures, and provide signed informed consent
2. Gender of Subjects: Both males and females will be invited to participate.
3. Age of Subjects: Patients will be over 50 years and older

Exclusion Criteria:

1. Unable to provide informed consent.
2. Other retinal degenerations and retinal vascular diseases such as diabetic retinopathy or macular edema, prior retinal surgery.

Outcomes:
Sensitivity of identification of referable and non-referable AMD for early diagnosis of AMD; Specificity of identification of referable and non-referable AMD for early diagnosis of AMD using the iPredict's AI-based AMD screening software utilizing color fundus imaging.
Management Protocol for Comparison of Loco-regional Analgesic Techniques in Patients Undergoing Video Assisted Thoracoscopic Surgery (VATS)
Trial ID: NCT07102173
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-01
Completion date: 2026-04
Sponsor: ASST Sette Laghi
Conditions: Loco-regional Anesthesia
Interventions: Intercostal nerve block; Ultrasound-guided ESPB
Enrollment: 72
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to compare two loco-regional analgesic techniques:

1. Intercostal nerve block: Performed by the surgeon intraoperatively before final lung re-expansion, involving the administration of 20 mL of 0.5% levobupivacaine using a 27G needle into the subpleural space of the thoracotomy access site and adjacent intercostal spaces.
2. Ultrasound-guided ESPB: Performed by an anesthesiologist under surgical asepsis with the patient in a seated position. After identifying the target transverse process, a linear ultrasound probe is placed sagittally approximately 2 cm from the midline. The needle (22G, 50 mm) is inserted in-plane in a cranio-caudal direction until contacting the transverse process. After confirming proper injection by observing anesthetic spread between the erector spinae muscle and transverse process, a total of 30 mL of 0.375% ropivacaine is administered.

This protocol is intended for patients undergoing video-assisted thoracoscopic surgery (VATS) for pneumonectomies, lobectomies, wedge resections, and bullectomies.

Primary Endpoint:QoR Score at 24 hours (validated questionnaires. Secondary Endpoints: Intraoperative NOL, QoR at 48-72 hours, opioid consumption at 12-24-48 hours, NLR at 24 hours, CPSP incidence at 3 months.

Eligibility criteria:
Inclusion Criteria:

* Adult patients
* Signed informed consent
* Elective unilateral surgery

Exclusion Criteria:

* Minor patients
* BMI \> 40
* Unstable neurological disorders
* Known allergy to acetaminophen and NSAIDs
* Chronic renal failure (stage \> 4, whith GFR between 29 and 15 mL/min)
* Patient refusal
* Chronic use of NSAIDs and opioids in the 3 months prior to surgery
* Contraindications to peripheral block (infection al the insertion site, allergy to local anesthetics, severe coagulation disorders)

Outcomes:
QoR Score at Day 1
Early Prediction of Cerebral Palsy by MRI in Infants With White Matter Injury: a Multicenter Study
Trial ID: NCT06575283
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-01
Completion date: 2025-12-31
Sponsor: First Affiliated Hospital Xi'an Jiaotong University
Collaborators: The First Affiliated Hospital of Henan University of Traditional Chinese Medicine; Shenzhen Children's Hospital; Zunyi Medical College; Wuxi Women's & Children's Hospital; Shanxi Provincial Maternity and Children's Hospital; Chengdu Medical College; First Affiliated Hospital of Xinjiang Medical University; Baoji Central Hospital; Xian Children's Hospital; Guangzhou Women and Children's Medical Center; Third Affiliated Hospital of Zhengzhou University; Henan Provincial People's Hospital
Conditions: Cerebral Palsy; Periventricular White Matter Abnormalities
Interventions: No intervention will be performed in this cohort study
Locations: The First Affiliated Hospital of Xi'an Jiaotong University
Countries: China
Enrollment: 1000
Age range: 6 Months - 2 Years
Sex: ALL

Summary:
The goal of this study is to determin the MRI features associated with cerebral palsy and to develop prediction models of pediatric disorders by combining MRI with artificial intelligence.

The main questions it aims to answer are:

* How to achieve features on conventional MRI associated with cerebral palsy?
* How to predict the risk of cerebral palsy in infants aged 6 to 2 years based on conventional MRI and deep learning? Researchers will compare characteristics of periventricular white matter injury with cerebral palsy to those without cerebral palsy.

Participants will be asked to provide MRI data, clinical diagnoses information, and follow-up outcomes.

Eligibility criteria:
Inclusion Criteria:

1. Infants and children at high risk of periventricular white matter injury (PVWMI) (gestational age \<35 weeks, birth weight \<2.6 kg, forceps-assisted delivery/fetal head attraction, Apgar score \<7, hypoglycaemia, sepsis, electrolyte disturbances, premature rupture of membranes);
2. Those who underwent MRI at 6 months of age-2 years, including at least T1WI and T2WI sequences;
3. Upon follow-up, the patient's clinical diagnosis: cerebral palsy, other diagnoses that did not develop into cerebral palsy, or inability to confirm the diagnosis).

Exclusion Criteria:

1. Incomplete MRI images or unreadable images due to motion artefacts;
2. Incomplete neurobehavioural assessment data (including: gross motor function).

Outcomes:
Accuracy of the model predicting cerebral palsy
Histological Segmentation of the Superficial Femoral Artery From Microscan to CT Using Artificial Intelligence: a Feasibility Study (CTPred)
Trial ID: NCT06260488
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-03-15
Completion date: 2025-08-15
Sponsor: University Hospital, Strasbourg, France
Conditions: Peripheral Artery Disease; Femoropopliteal Stenosis
Interventions: Endovascular surgery
Locations: Hôpitaux Universitaire de Strasbourg
Countries: France
Enrollment: 20
Age range: 18 Years - None
Sex: ALL

Summary:
The femoropopliteal artery segment (FPAS) is one of the longest arteries in the human body, undergoing torsion, compression, flexion and extension due to lower limb movements. Endovascular surgery is considered to be the treatment of choice for the peripheral arterial disease, the results of which depend on the physiological forces on the arterial wall, the anatomy of the vessels and the characteristics of the lesions being treated. The atheromatous disease includes, in a simple way, 3 categories of plaques: calcified, fibrous, and lipidic. The study of these plaques and their differentiation in imaging and histology in the FPAS has already been the subject of research. To treat them, there are angioplasty balloons and stents with different designs and components, with different mechanical properties and different impregnated molecules.

There is no non-invasive method (imaging) to accurately differentiate lesions along the FPAS. The analysis is performed from the preoperative CT scan, but there are high-resolution scanners that allow a quasi-histological analysis of the tissue.

This microscanner can be used ex vivo. In the framework of a project, the learning algorithm was be créated (Convolutional Neural Networks) to automatically segment microscanner slices: after taking FPAS from amputated limbs, we correlated ex-vivo microscanner images of the arteries with their histology. The correlation was then performed manually between the microscanner images, and the histological sections obtained. the algorithm well be trained on these slices and validated its performance. The validation of the CT and microscanner concordance was the subject of scientific publications.

Eligibility criteria:
Inclusion criteria:

* Male or female of legal age
* Subject with a planned transfemoral amputation in the vascular surgery department of the Hôpitaux Universitaires de Strasbourg as standard care
* Subject with a CT as part of standard care
* Subject who has given his/her non-opposition to participate in the study

Exclusion criteria:

\- Impossible to give the subject informed information (subject in emergency situation, difficulties in understanding)

Outcomes:
Assessing the feasibility of histological segmentation of the superficial femoral artery on preoperative microscanner using artificial intelligence
Artificial Intelligence Analysis of Fluorescence Image to Intraoperatively Detect Metastatic Sentinel Lymph Node in Patients With Breast Cancer.
Trial ID: NCT05623280
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-11-01
Completion date: 2024-12-01
Sponsor: Xiang'an Hospital of Xiamen University
Conditions: Breast Cancer; Sentinel Lymph Node
Interventions: Indocyanine green
Locations: Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine
Countries: China
Enrollment: 40
Age range: 18 Years - 70 Years
Sex: FEMALE

Summary:
The purpose of this study is to analysis the fluorescence image of the breast sentinel lymph node (SLN) using Indocyanine green (ICG). Moreover, to investigate whether an artificial intelligence protocol was suitable for identifying metastatic status of SLN during the surgery, and evaluate the diagnosis consistency of the AI technique and pathological examinations for lymph node with and without metastasis.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18-70 years female.
* The preoperative core needle biopsy or open surgical excision biopsy diagnosis as breast cancer.
* No clinical examination of suspicious axillary lymph node-positive.
* Preoperative clinical or radiologic evidence without distant metastases (M0).
* The patient has good compliance with the planned protocol during the study and signed informed consent.

Exclusion Criteria:

* Pregnancy, breastfeeding.
* Allergy to ICG.
* Former operation or radiotherapy in the axilla or breast or thoracic wall in the same side of breast cancer.
* Psychiatric or cognitive impairment.

Outcomes:
Diagnosis of lymph node metastasis
Artificial Intelligence-Assisted Internet-Based Mindfulness Intervention for Emotional Distress: Protocol for a Randomized Controlled Trial on Long-Term Outcomes and Cost-Effectiveness
Trial ID: NCT07542561
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-15
Completion date: 2029-05-15
Sponsor: Xinghua Liu
Conditions: Subclinical Depression; Subclinical Anxious Adults; Subclinical Emotional Distress
Interventions: Artificial Intelligence-Assisted Internet-Based Mindfulness Intervention
Locations: Peking University
Countries: China
Enrollment: 550
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The goal of this clinical trial is to learn whether an artificial intelligence-assisted internet-based mindfulness intervention for emotional distress (iMIED) can prevent or reduce anxiety and depressive symptoms in adults with subclinical emotional distress. The main questions it aims to answer are:

Can iMIED reduce anxiety symptoms, as measured by the Generalized Anxiety Disorder-7 (GAD-7)? Can iMIED reduce depressive symptoms, as measured by the Patient Health Questionnaire-9 (PHQ-9)?

Researchers will compare participants receiving iMIED plus access to usual mental health resources with participants receiving usual mental health resources alone to see whether iMIED leads to greater improvement in emotional distress over time.

Participants will:

complete online screening and baseline assessments; be randomly assigned to either the iMIED intervention group or the control group; if assigned to the intervention group, complete a 49-day AI-assisted online mindfulness self-help program using a WeChat mini-program and AI support tool; complete follow-up online questionnaires during the intervention and at 3, 6, 12, 18, and 24 months after the intervention; report information on mental health symptoms, quality of life, healthcare use, sleep, stress, resilience, and life satisfaction.

Eligibility criteria:
Inclusion Criteria:

Aged 18 to 65 years PHQ-9 or GAD-7 score ≥5 Able to use a smartphone or other internet-enabled electronic device and possessing sufficient digital literacy to participate in the online intervention and complete follow-up assessments Willing to participate and able to provide informed consent

Exclusion Criteria:

Current psychotic disorder or bipolar disorder Current organic mental disorder, pervasive developmental disorder, severe cognitive impairment, or substance use disorder Current suicide risk, defined as a score of ≥2 on Item 9 of the PHQ-9 Previous participation in a structured 8-week mindfulness course or the 49-day self-guided course developed by the Peking University Mindfulness Laboratory

Outcomes:
Generalized Anxiety Disorder-7 (GAD-7); Patient Health Questionnaire-9 (PHQ-9)