moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Study on the Staging and Prognosis Model of Bladder Cancer Based on Artificial Intelligence and Multimodal Omics Features
Trial ID: NCT06565923
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-03-18
Completion date: 2025-07-18
Sponsor: The First Affiliated Hospital with Nanjing Medical University
Collaborators: Suzhou Municipal Hospital; Yixing People's Hospital; Union Hospital, Tongji Medical College, Huazhong University of Science and Technology; Huai an First People Hospital; Jiangsu Province Hospital of Traditional Chinese Medicine; The First Affiliated Hospital of Zhengzhou University; The second affiliated hospital of Xuzhou medical university
Conditions: Bladder Cancer
Locations: The First Affiliated Hospital with Nanjing Medical University
Countries: China
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
Firstly, we retrospectively gathered the patient information who compliant with the criteria from 2012 to 2023, encompassing basic information, clinical information, along with MRI images, blood/urine samples, and tissue samples, for conducting relevant analyses of radiomics. Subsequently, based on artificial intelligence technology, deep learning and machine learning models were established on the basis of MRI radiomics and pathological histomics. Ultimately, the following research aims were accomplished: 1. Primary research objective: To explore the role of artificial intelligence and multimodal omics features in the staging and prognosis monitoring of bladder cancer. 2. Secondary objective: To explore the correlations among radiomics, case histomics, and test omics.

Eligibility criteria:
Inclusion Criteria:

* 1\. Patients with bladder cancer in preoperative examination; 2. Gender is not limited; 3. Age≥ 18 years old; 4. Be able to provide MRI images, pathological data and laboratory examination data before the operation; 5. Agree to provide basic personal clinical information and pathological and imaging data for scientific research use, and sign the informed consent form; 6. Agree to provide monitoring results during follow-up recurrence monitoring;

Exclusion Criteria:

* 1\. Incomplete clinicopathological data; 2. Combined with upper tract urothelial carcinoma or previously diagnosed upper tract urothelial carcinoma; 3. Is participating in the rest of the clinical studies; Unable to cooperate with the relevant examinations of this project, and do not agree to sign the informed consent form.

Outcomes:
Overall survival (OS); Progression-free survival (PFS); Recurrence-Free Survival (RFS)
Impact of Supplementary Access to a Fine-Tuned Medical Large Language Model (MetaGP-Edu) on Learning Outcomes in Undergraduate Medicine Education
Trial ID: NCT06945159
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-06-01
Completion date: 2024-12-01
Sponsor: Kang Zhang
Conditions: Medical Student
Locations: Peking Union Medical College; Peking University Health Science Center
Countries: China; China
Enrollment: 1632
Age range: None - None
Sex: ALL

Summary:
This multi-center retrospective cohort study investigates the real-world impact of integrating MetaGP-Edu, a proprietary AI tool fine-tuned for medical education, into the undergraduate Internal Medicine curriculum. Utilizing historical academic records from several major medical institutions in China across multiple academic years, the study compares the performance of student cohorts who learned via traditional methods only with subsequent cohorts who had supplementary access to MetaGP-Edu. The primary outcome measure is overall academic performance in the Internal Medicine course, assessed through final course scores. The analysis aims to determine if access to the AI tool as a supplementary resource is associated with differences in learning outcomes, while statistically accounting for baseline student characteristics and other potential confounders between the compared cohorts.

Eligibility criteria:
Inclusion Criteria:

Inclusion criteria required students to have completed the entire Internal Medicine course and possess a recorded final numeric score for the course within the selected timeframe

Exclusion Criteria:

Students with incomplete academic records for the course, those who transferred between institutions mid-course, or individuals identified as having repeated the course were excluded

Outcomes:
academic performance
A Multicentric Validation Study on the Accuracy of Artificial Intelligence Assisted System in Clinical Application of Digestive Endoscopy
Trial ID: NCT04232462
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-01-01
Completion date: 2025-12-31
Sponsor: Renmin Hospital of Wuhan University
Conditions: Gastrointestinal Disease; Endoscopy; Artificial Intelligence
Interventions: Endoscopists refer to AI for diagnosis
Locations: Renmin Hospital of Wuhan University
Countries: China
Enrollment: 10000
Age range: 18 Years - None
Sex: ALL

Summary:
This is an artificial intelligence-based optical artificial intelligence assisted system that can assist endoscopists in improving the quality of endoscopy.

Eligibility criteria:
Inclusion Criteria:

1. male or female aged 18 or above;
2. endoscopy and related examinations should be performed to further clarify the characteristics of digestive tract diseases;
3. be able to read, understand and sign the informed consent;
4. the researcher believes that the subject can understand the process of the clinical study, is willing and able to complete all the study procedures and follow-up visits, and cooperate with the study procedures;

Exclusion Criteria:

1. have participated in other clinical trials, signed the informed consent and have been in the follow-up period of other clinical trials;
2. drug or alcohol abuse or psychological disorder in the last 5 years;
3. pregnant or nursing women;
4. subjects with previous history of gastrointestinal surgery;
5. the researcher considers that the subject is not suitable for endoscopy and related examination;
6. high-risk diseases or other special conditions that the investigator considers inappropriate for the subject to participate in the clinical trial.

Outcomes:
Accuracy; Sensitivity; Specificity; Positive Predictive Value (PPV); Negative Predictive Value (NPV); Receiver Operating Characteristic (ROC) Curve; Area Under the Curve (AUC)
Effects of Different Cardiorespiratory Training Program on Endurance Performance
Trial ID: NCT04357691
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-05-25
Completion date: 2022-12-31
Sponsor: Wang Yongtai Raymond
Collaborators: The N.1 Institute for Health (N.1)
Conditions: Healthy
Interventions: Conventional training; AI-led training
Locations: National University of Singapore
Countries: Singapore
Enrollment: 20
Age range: 21 Years - 35 Years
Sex: MALE

Summary:
Conventional training methods are typically administered in a fixed progressive manner, which can lead to sub-optimal responses and injuries. Artificial intelligence (i.e. CURATE.AI) can be harnessed to personalise physical training strategies. Using a single participant training profile, a parabolic/quadratic response to the intervention can be generated to identify the training intensity needed to optimise training outcomes. Previous studies showed CURATE.AI could dynamically modulate drug dosing in oncology. Extending the utility of results to human performance, this study will adapt CURATE.AI with the goal of optimising endurance performance through individualised training regimes.

Up to 20 participants will be recruited and randomised into two groups to undergo a calibration phase, which involves performing 3 sessions of exercise sessions per week over 2 weeks per intensity (low, moderate and high) in a crossover study design. Exercise sessions will be interspersed with a 2.4 km time trial, a VO2peak test and 2 weeks of wash out period. The utility phase will divide participants into two groups to undergo 3 exercise sessions per week, totalling to 12 exercise sessions. Either an AI-led training or a conventional training programme will be performed to compare the differences in training outcomes. Blood plasma will be obtained at selected time points in both phases to evaluate the effects of training on blood lipid profiles.

Findings from this study can potentially optimise efficacy and efficiency of endurance performance through personalised training with AI.

Eligibility criteria:
Inclusion Criteria:

* Do not smoke or use tobacco products (including shisha)
* BMI \< 30
* Medically fit
* 2.4 km run time of 10 to 14 minutes in the past 12 months

Exclusion Criteria:

•Individuals with a history of existing musculoskeletal injury or respiratory diseases

Outcomes:
Changes in 2.4 km completion time following first cycle in phase 1; Changes in 2.4 km completion time following second cycle in phase 1; Changes in 2.4 km completion time following third cycle in phase 1; Changes in 2.4 km completion time following phase 2
Validation of an Ex Vivo Calf Brain Force Detection System for Neurosurgical Simulation Training: A Case Series Study
Trial ID: NCT07583082
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05
Completion date: 2026-09
Sponsor: McGill University
Conditions: Surgical Education
Interventions: Observational
Locations: Neuro Imaging and Surgical Technologies Lab, Montreal Neurological Institute and Hospital, McGill University
Countries: Canada
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
Neurosurgery is a high-stakes surgical specialty where errors can result in significant patient mortality and morbidity. The amount of force applied on the brain simultaneously by the multiple different instruments during complex neurosurgical procedures is a critical safety metric that, to the investigators' knowledge, has not been previously measured in a realistic operative environment.

The investigators have therefore developed a simulation platform integrating an ex vivo calf brain and a 3D-printed skull model attached to a force sensor capable of capturing real-time forces applied to the brain. A cross-sectional case series study will be conducted to evaluate the validity of the system. Medical students, neurosurgical residents, neurosurgical fellows, and staff neurosurgeons from four Quebec institutions will be recruited to perform three simulated subpial resections each using our ex vivo calf brain simulation platform. The forces applied by the microscissors, bipolar forceps, and ultrasonic aspirator onto the brain will be captured along with kinematic data. This study aims to establish the face, content, construct, and convergent validity of this ex vivo calf brain force detection system.

Eligibility criteria:
Inclusion Criteria:

* Medical students, neurosurgical residents, neurosurgical fellows, and staff neurosurgeons from one of four Quebec institutions who do not fit the exclusion criteria.

Exclusion Criteria:

* For medical students, participation in a previous trial where they received training on the NeuroVR surgical simulator or the ex vivo calf brain simulation model.

Outcomes:
Forces applied to the brain during each simulated subpial resection procedure
Development and Evaluation of a Smart Technology-Assisted System for Shoulder Joint Physical Therapy Assessment
Trial ID: NCT07089082
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-01
Completion date: 2026-07-31
Sponsor: National Cheng-Kung University Hospital
Conditions: Shoulder
Locations: National Cheng Kung University
Countries: Taiwan
Enrollment: 40
Age range: 20 Years - 65 Years
Sex: ALL

Summary:
Shoulder pain is one of the most prevalent musculoskeletal conditions. Evidence-based medicine has identified physical therapy as the most effective intervention for managing shoulder disorders. To ensure accurate diagnosis and effective treatment planning, a comprehensive evaluation that integrates various clinical findings is essential. Without timely and accurate diagnosis and intervention, shoulder pain may recur and fail to improve, limiting therapeutic outcomes.

With technological advancements, the application of mobile devices and artificial intelligence (AI) in clinical settings has become increasingly widespread. Motion capture technologies integrated into mobile platforms offer emerging solutions for clinical challenges. If clinicians are equipped with an intelligent system for shoulder assessment and intervention-one that includes image-based quantitative assessment tools, evidence-based clinical guidelines and data repositories, and home-based rehabilitation support-it may enhance diagnostic precision, increase clinical efficiency, and improve patient adherence to home exercise programs.

The aim of this study is to develop a smart technology-assisted assessment system for orthopedic physical therapy of the shoulder joint and to validate its reliability and validity. This system will provide clinicians with objective, data-driven evaluation results. In future development, it will also offer support in treatment goal setting, intervention planning, and home-based exercise guidance. The proposed intelligent system is expected to serve as an evidence-based clinical aid, enhancing both the precision and efficiency of physical therapy interventions.

Eligibility criteria:
Inclusion Criteria for group 1:

* BMI (body mass index) between 18.5-24.9
* self-perceived good physical condition
* no history of shoulder orthopedic disease or nerve damage

Inclusion Criteria for group 2:

* Non-acute shoulder orthopedic diseases diagnosed by orthopedic physicians or assessed by physical therapists
* may include but are not limited to the following diseases: frozen shoulder, shoulder compression syndrome, shoulder rotator injury, shoulder instability, etc.

Exclusion Criteria:

* Any neurological disease that may cause pain
* a history of related surgery in the past six months
* acute inflammation of the shoulder joint
* open wounds in the shoulder joint area
* Principle Investor's teaching students, laboratory assistants

Outcomes:
Range of Motion
A Prospective, Longitudinal Cohort Study of the Incidence of Dementia After the Onset of Delirium in Patients With Mild Cognitive Impairment: MDD Cohort (Mild Cognitive Impairment Delirium Dementia)
Trial ID: NCT05113446
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-07-01
Completion date: 2025-12-31
Sponsor: Keimyung University Dongsan Medical Center
Conditions: Delirium; Mild Cognitive Impairment; Dementia
Interventions: Delirium assessment using S-CAM
Locations: Yangsan Seoul long-term care (LTC) facilities; Gunsan Chamsarang long-term care (LTC) facilities
Countries: South Korea; South Korea
Enrollment: 163
Age range: 65 Years - None
Sex: ALL

Summary:
* Identify the degree of delirium in subjects with mild cognitive impairment and find the risk factors of delirium. Mortality, hospital stay, and medical expenses are analyzed as clinical consequences related to delirium incidence.
* Dementia conversion rate and conversion period of subjects with mild cognitive impairment with delirium and it identifies the effect of delirium on dementia conversion.
* Develop an AI(Artificial intelligence) algorithm for predicting dementia transition in subjects with mild cognitive impairment based on the research results of the 1st, 2nd, and 3rd years.

Eligibility criteria:
Inclusion Criteria:

* Patients 65 years of age or older who are admitted to a LTC
* Patients diagnosed with mild cognitive impairment by doctors
* Patients with a score of 18 or more and 23 or less on the Mini-Mental State Examination

Exclusion Criteria:

* Patients unable to measure the delirium assessment tool, The Short Confusion Assessment Methods (S-CAM) (Inouye, 2014) due to severe vision and hearing problems
* Patients with serious psychiatric or neurological diagnosis,Patients who expire or were transferred on the day of admission -
* Patients receiving emergency treatment at the time of the delirium assessment or unable to measure or intervene with S-CAM due to hospital circumstances

Outcomes:
Delirium incidence in participants with mild cognitive impairment; Dementia conversion rate and transition period in participants with mild cognitive impairment
Investigation of Impact of AI on Prostate Cancer Workflow
Trial ID: NCT07084779
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-09
Completion date: 2026-11-30
Sponsor: Case Comprehensive Cancer Center
Collaborators: Siemens Medical Solutions
Conditions: Prostate Cancer; Prostatic Neoplasm; Cancer of the Prostate; Neoplasms, Prostate
Interventions: AI
Locations: Cleveland Clinic Foundation
Countries: United States
Enrollment: 150
Age range: 55 Years - 80 Years
Sex: ALL

Summary:
This study will enroll participants who are undergoing an MRI before a prostate biopsy due to suspected prostate cancer. The purpose of this study is to see if the use of Artificial Intelligence (AI) helps detect lesions on an MRI better than a radiologist not using AI. The AI Rad Companion (AIRC) Prostate MRI application is a software that uses measurements of the prostate and will be utilized in this study to help detect potential cancerous lesions. The AI software will assign the lesions a PI-RADS score, which is a way to measure the chance of the lesion being cancer.

There are two parts to this study. The first part involves comparing the interpretation of prostate MRI images by a radiologist alone, a radiologist aided by AI, and AI alone. A systematic biopsy will be completed per standard of care. The radiologist may opt to include up to 2 additional AI-identified targets to biopsy in addition to those biopsied for standard of care.

The second part of the study involves utilizing the MRI images from the first part of the study in addition to retrospective prostate MRI images. These de-identified images, along with Prostate Image Quality (PI-QUAL) scores, clinical data, and biopsy results will be sent to Siemens in order to aid in the development of methods to identify good or bad image quality in prostate MRI images.

Eligibility criteria:
Inclusion Criteria:

* Plan of care is to undergo a biopsy of the prostate after a pre-biopsy MRI
* Age 55-80
* Prostate-specific antigen (PSA) between 3-10 ng/mL
* No prior diagnosis or treatment of prostate cancer

Exclusion Criteria:

* Pre-biopsy MRI is of low quality
* PI-QUAL score of 1 using PI-QUAL version 2

Outcomes:
Readers' (radiologists') mean quadrant-level area under the receiver operating characteristic curve (AUC) in predicting the presence or absence of clinically significant prostate cancer (csPCa)
Development and Validation of a Deep Learning Algorithm for Real-time Recognition of Colonic Segments.
Trial ID: NCT04087824
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-09-15
Completion date: 2019-12-15
Sponsor: Shandong University
Conditions: Colonic Diseases
Interventions: AI assisted recognition of colonic segments
Enrollment: 60
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
The purpose of this study is to develop and validate a deep learning algorithm to realize automatic recognition of colonic segments under conventional colonoscopy. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18-70 years undergoing conventional colonoscopy

Exclusion Criteria:

* Known or suspected bowel obstruction, stricture or perforation
* Compromised swallowing reflex or mental status
* Severe chronic renal failure(creatinine clearance \< 30 ml/min)
* Severe congestive heart failure (New York Heart Association class III or IV)
* Uncontrolled hypertension (systolic blood pressure \> 170 mm Hg, diastolic blood pressure \> 100 mm Hg)
* Dehydration
* Disturbance of electrolytes
* Pregnancy or lactation
* Hemodynamically unstable
* Unable to give informed consent

Outcomes:
The accuracy of each colonic segment real-time recognition with deep learning algorithm.
Establishment of Hong Kong Diabesity Register and a 3-year Randomized Clinical Trial for the Investigation of the Effectiveness of a Multicomponent Integrated Care Program in Chinese With Diabesity
Trial ID: NCT07039279
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-22
Completion date: 2030-12-31
Sponsor: Chinese University of Hong Kong
Conditions: Diabesity
Interventions: Multicomponent Integrated Care Program; TourHeart + platform
Locations: Department of Medicine and Therapeutics, The Chinese University of Hong Kong (CUHK), Ward 3M, Diabetes and Endocrine Research Centre, 3/F Day Treatment Block and Children Wards (Old Block), Prince of Wales Hospital
Countries: Hong Kong
Enrollment: 200
Age range: 18 Years - 50 Years
Sex: ALL

Summary:
People with diabetes and obesity (diabesity) are difficult to treat with many unmet needs requiring personalized treatment regimens, intensive counselling and emotional support. Here, we propose to conduct a pragmatic trial including 2 parts: (1) establishment of a register for Hong Kong Chinese with diabesity and through this register, identify participants consecutively to enter (2) a 3-year randomized clinical trial to assess the effects of multicomponent integrated care program supported by a team of endocrinologist, dietitian, psychologist and research assistant with elements including medical therapy, cognitive behavioural therapy, e-care, artificial intelligence chatbot, biofeedback, peer support to improve the physical and mental health in people with diabesity, with weight reduction as primary outcome. Other outcomes include cardiometabolic risk factors, diabetes distress, lifestyles and sleep hygiene.

Eligibility criteria:
Inclusion Criteria:

1. Type 2 diabetes;
2. Obesity reaching action level as defined as body mass index (BMI) ³27.5 kg/m2 and/or waist circumference (WC) ³80cm in women and ³90cm in men;
3. Age between 18 and 50 years;
4. Able to conversate with smartphone technology and web-based program.
5. Able to read Chinese and communicate using Chinese.

Exclusion Criteria:

1. Type 1 diabetes;
2. Active malignant disease including those with history of malignant disease less than 5 years of disease-free duration;
3. Life expectancy less than 12 months;
4. Any medical illness or condition as judged by the investigators as ineligible to participate the study.

Outcomes:
5% body weight reduction from baseline at year 1