moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Guidance with Oura and AI for Reducing Arterial Stiffness - the GONDOR-AS Trial
Trial ID: NCT06644014
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-09-16
Completion date: 2025-07-31
Sponsor: Pauli Ohukainen
Collaborators: Kuopio Research Institute of Exercise Medicine
Conditions: Arterial Stiffness; Maximal Oxygen Uptake
Interventions: Exercise; Exercise; Smart Ring
Locations: Kuopio Institute of Exercise Medicine
Countries: Finland
Enrollment: 165
Age range: 30 Years - 65 Years
Sex: ALL

Summary:
The goal of this three-month clinical trial is to study if wearing an Oura Ring and following the exercise guidance provided by an AI-based Advisor in the Oura App works to reduce arterial stiffness. It will also study the difference between different exercise protocols in reducing arterial stiffness and improving aerobic fitness. The main questions the study aims to answer are:

* Can an AI-based Advisor provide useful exercise guidance that helps improve cardiovascular health and aerobic fitness?
* Does regular exercise lower arterial stiffness measured with a clinical reference device and Oura Ring's proprietary Cardiovascular Age -metric and its estimated arterial stiffness?
* Is there a difference between attending supervised high-intensity interval (HIIT) training sessions and following personalized exercise instructions for steady-state aerobic training?

Participants will:

* Follow Oura's AI Advisor's instructions at their own convenience, attend supervised training sessions, or just wear the Ring without changing their lifestyle
* Visit an exercise physiology lab in the beginning of the trial and after three months for measurements in arterial stiffness, cardiorespiratory fitness, and body composition

In addition, the study contains questionnaires investigating the participants' experiences related to lifestyle changes.

Eligibility criteria:
Inclusion Criteria:

* Self-reported moderate intensity physical activity less than 2 hours 30 minutes per week or vigorous intensity physical activity less than 1 hour 15 minutes per week.

Exclusion Criteria:

* Coronary artery disease (or other chronic condition preventing safe participation in an exercise intervention), diabetes and moderate/severe hypertension diagnosed by a physician. Inability to use the Oura Ring for some reason (e.g. work-related restrictions) is also considered as exclusion criteria; however, short interruptions (e.g. 1-2 hour work tasks without ring) are possible.

Outcomes:
Arterial stiffness, clinical reference; VO2max
Artificial Intelligence-assisted Common Bile Duct Stent Selection in Endoscopic Retrograde Cholangiopancreatography
Trial ID: NCT05321472
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-04-20
Completion date: 2023-04
Sponsor: Shandong University
Conditions: Stenosis of Bile Duct; Endoscopic Retrograde Cholangiopancreatography
Locations: Department of Gastroenterology,QiLu Hospital,Shandong University
Countries: China
Enrollment: 600
Age range: 18 Years - None
Sex: ALL

Summary:
Common bile duct stenosis is an important indication for endoscopic retrograde cholangiopancreatography(ERCP). Appropriate selection of bile duct stent size is not only conducive to successful stent implantation but also to improve the prognosis of patients. Currently, the selection of stent specifications is based on the operator's empirical estimation, which is not only not accurate but also increases the radiation exposure time, causing unnecessary harm to both the operator and the patient. Our objective is to develop an artificial intelligence algorithm to automatically select appropriate stent.

Eligibility criteria:
Inclusion Criteria:

* Patients older than 18 years old who underwent ERCP

Exclusion Criteria:

* failed cholangiopancreatography caused by failed intubation, gastric retention, duodenal disease and so on
* patients proved no stenosis in common bile duct
* poor cholangiograms due to the lack of contrast agent or insufficient filling of contrast agent (cholangiograms without the completed CBD or thumbnails)

Outcomes:
The accuracy of the calculated length of the stents by the artificial intelligence
Application of Artificial Intelligence Deep Learning Technology in Magnetic Resonance Lumbar Imaging
Trial ID: NCT06037057
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-10-08
Completion date: 2024-03-08
Sponsor: RenJi Hospital
Conditions: Deep Learning,Lumbar Magnetic Resonance Imaging
Interventions: no intervention name
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
To study the comparative analysis of artificial intelligence deep learning technology in the image quality of under-artificial intelligence (AI) reconstruction images and the original acquisition images of magnetic resonance lumbar spine

Eligibility criteria:
Inclusion Criteria:

* Patients of both sexes, aged ≥18 years, with no history of lumbar surgery, underwent lumbar magnetic resonance imaging examination.

Exclusion Criteria:

* With metal implants in the body, claustrophobic, unable to lie flat for 15 minutes, with a history of lumbar surgery.

Outcomes:
Application of Artificial Intelligence Deep Learning Technology in Magnetic Resonance Lumbar Imaging
Clinical vAliDation of ARTificial Intelligence in POlyp Detection
Trial ID: NCT04442607
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-10-13
Completion date: 2022-11-29
Sponsor: Universitaire Ziekenhuizen KU Leuven
Collaborators: Nuovo Regina Margherita Hospital, Rome, Italy; Krankenhaus Barmherzige Brüder, Regensburg, Germany; Centre Hospitalier Universitaire de Nantes, Nantes, France; Centrum Onkologii-Instytut im. Marii Skłodowskiej-Curie, Warschau, Poland; Spire Portsmouth Hospital, Portsmouth, United Kingdom; Amsterdam UMC, location VUmc; University Hospital, Ghent
Conditions: Polyp of Colon
Interventions: artificial intelligence image processing
Locations: University Hospitals Leuven
Countries: Belgium
Enrollment: 856
Age range: 40 Years - None
Sex: ALL

Summary:
This study is an open label, unblinded, non-randomized interventional study, comparing the investigational artificial intelligence tool with the current "gold standard": Data acquisition will be obtained during one scheduled colonoscopic procedure by a trained endoscopist. During insertion, no action will be taken, colonoscopy is performed following the standard of care. Once withdrawal is started, a second observer (not a trained endoscopist but person trained in polyp recognition) will start the bedside Artificial intelligence (AI) tool, connected to the endoscope's tower, for detection. This second observer is trained in assessing endoscopic images to define the AI tool's outcome. Due to the second observer watching the separate AI screen, the endoscopist is blinded of the AI outcome. When a detection is made by the AI system that is not recognized by the endoscopist, the endoscopist will be asked to relocate that same detection and to reassess the lesion and the possible need of therapeutic action. All detections are separately counted and categorized by the second observer. All polyp detections will be removed following standard of care for histological assessment. The entire colonoscopic procedure is recorded via a separate linked video-recorder.

Eligibility criteria:
Inclusion Criteria:

* Age ≥40 years
* Referral for screening, surveillance or diagnostic colonoscopy
* Able to give informed consent by the patient or by a legal representative

Exclusion criteria for study inclusion

* \<40 years old
* Referral for a therapeutic colonoscopy
* Known Lynch syndrome or Familial Adenomatous Polyposis syndrome
* Any contraindication for colonoscopy or biopsies of the colon
* Uncontrolled coagulopathy
* Confirmed diagnosis of inflammatory bowel disease prior to the scheduled colonoscopy
* Short bowel or ileostomy
* Pregnancy

Exclusion criteria for study analysis

* Colonic inflammation of \> 30cm during colonoscopy
* Incomplete colonoscopy for any reason
* Incomplete recording or technical failure of the artificial intelligence system

Outcomes:
Total polyp detection during single pass colonoscopy by the artificial intelligence tool in comparison to polyp detection by the endoscopist with endoscopic diagnosis as a gold standard
Crowdsourcing an Open COVID-19 Chest Radiograph Imaging Repository for Artificial Intelligence Research
Trial ID: NCT05384912
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-11-18
Completion date: 2025-02-06
Sponsor: University of Central Florida
Conditions: COVID-19
Locations: University of Central Florida
Countries: United States
Enrollment: 81
Age range: 18 Years - None
Sex: ALL

Summary:
The objectives of this project are to (1) assemble a crowdsourced, de-identified radiographic repository; and (2) train and validate existing COVID-NET deep learning diagnostic models.

Eligibility criteria:
Inclusion Criteria:

* This study will include anyone in the country who has been tested for COVID-19 with a chest radiograph image.

Exclusion Criteria:

* Patients who do not have a chest radiograph image used for COVID-19 testing

Outcomes:
Assemble a crowdsourced, de-identified radiographic repository
Integration of a Trained Language Model to Improve Glycemic Control Through Increased Physical Activity: a Fully Digital My Heart Counts Smartphone App Randomized Trial
Trial ID: NCT06596330
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07
Completion date: 2029-07
Sponsor: Stanford University
Conditions: Type2diabetes
Interventions: Validation of language model prompts in increasing short-term physical activity; Assessment of long-term changes to physical activity and glycemic control
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Type 2 diabetes (T2D) is one of the most common and fastest growing diseases, affecting 1 in 8 adults (nearly 800 million) worldwide by 2045. Sedentary behavior and increased adiposity are major risk factors for T2D. Cardiovascular disease is the leading cause of death in those with T2D, while diabetic microvascular disease, causing kidney disease, neuropathy, and retinopathy, contributes to T2D morbidity.

Physical activity is one of the most potent therapies in preventing/treating T2D and its complications. Mean daily steps is a proxy for physical activity, with even modest improvements in step count (i.e., +500 steps) associated with decreased T2D and mortality. However, adherence to regular physical activity remains low in T2D patients, with short-term decreases in daily step count associated with impaired glycemic control and T2D recurrence.

The investigators have developed an artificial intelligence (AI) language model (similar to ChatGPT), which can automatically generate coaching prompts to encourage physical activity by incorporating an individual's stage of change. The investigators will extend our research using the My Heart Counts (MHC) smartphone app to 1) validate the efficacy of the AI-generated prompts in patients with T2D and 2) perform a longer-term randomized crossover trial using the language model as a social accountability chatbot - encouraging participants to maintain their physical activity changes over months. The investigators hypothesize that my AI-assisted coaching prompts will significantly increase 1) mean daily step count by 500 steps in 1,000 adults recruited nationwide over a 7-day period, and 2) improve HbA1c and weight via long-term behavior change over a 24-week intervention period.

Eligibility criteria:
Inclusion Criteria:

* Individuals aged ≥18 years old, with a clinical diagnosis of T2D, able to read and understand English, and who are physically able to walk, will be included in our study

Exclusion Criteria:

* Criteria that fall outside of the inclusion criteria.

Outcomes:
Mean daily steps
Artificial Intelligence-based Models Enabling Robust and Precise Spine Malalignment Auto-analysis in China: a Multicentre, Retrospective Cohort Study
Trial ID: NCT06711757
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-01-05
Completion date: 2024-11-05
Sponsor: The University of Hong Kong
Collaborators: Peking Union Medical College Hospital; Queen Mary Hospital, Hong Kong; The Duchess of Kent Children's Hospital at Sandy Bay; Ruijin Hospital; Huashan Hospital; Beijing Jishuitan Hospital; Nanfang Hospital, Southern Medical University
Conditions: Adolescent Idiopathic Scoliosis
Locations: Digital Health Laboratory, Li Ka Shing Faculty of Medicine, The University of Hong Kong
Countries: Hong Kong
Enrollment: 3015
Age range: 10 Years - 18 Years
Sex: ALL

Summary:
This retrospective study aimed to enhance and validate a model for diagnosing adolescent idiopathic scoliosis (AIS) across multiple medical centers. The study included 2,763 participants from prestigious hospitals in mainland China and Hong Kong. X-rays were used to develop and validate the model, with data from different hospitals to ensure robustness. Participants aged 10-18 with confirmed AIS were enrolled, and data were deidentified for privacy. The model was optimized using training data and validated internally before being deployed for real-world application. A novel data augmentation technique was used to address data heterogeneity, and a standardized analysis platform, AlignProCARE, was employed for evaluation. X-rays were annotated with vertebra landmarks, and traditional and intensity-based data augmentation methods were applied for image processing. Coronal Cobb angle was used to evaluate spinal alignment, with severity classified as normal-mild, moderate, or severe. The model's performance was statistically assessed for accuracy in predicting Cobb angle and severity grading. Overall, the study aimed to provide a reliable diagnostic tool for AIS analysis in clinical practice, improving efficiency and standardization in diagnosis and treatment.

Eligibility criteria:
Inclusion Criteria:

* Participants aged between 10 and 18 years old,
* A pathological confirmation of the presence or absence of AIS

Exclusion Criteria:

* Patients with other types of scoliosis, such as congenital or neuromuscular scoliosis
* Patients with skin diseases, such as acne, psoriasis, skin pigmentation and rash that can affect imaging
* Individuals that cannot stand up
* Cases where standing imaging was not feasible or other conditions that could impair image acquisition.

Outcomes:
Cobb Angle prediction accuracy
Knowledge-guided Causal Diagnostic Network for the Detection of Muscle-invasive Bladder Cancer With Single T2-weighted Imaging
Trial ID: NCT06362330
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-07-01
Completion date: 2024-06-30
Sponsor: The First Affiliated Hospital with Nanjing Medical University
Conditions: Muscle-invasive Bladder Cancer; Artificial Intelligence
Interventions: magnetic resonance imaging
Locations: Yu-Dong Zhang
Countries: China
Enrollment: 1000
Age range: None - None
Sex: ALL

Summary:
Accurate preoperative detection of muscle-invasive bladder cancer remains a clinical challenge. The investigators aimed to develop and validate a knowledge-guided causal diagnostic network for the detection of muscle-invasive bladder cancer with multiparametric magnetic resonance imaging(MRI).

Eligibility criteria:
Inclusion Criteria:

* Urothelial carcinoma of the bladder confirmed by final histopathology ②Received a standard contrast-enhanced 3.0T mpMRI before surgery ③All tumors within patients included were resected and received pathologic examination separately

Exclusion Criteria:

①Absence of surgical interventions

②With inadequate image quality or with inadequate pathology for analysis

Outcomes:
Muscle-invasive bladder cancer; Non-muscle-invasive bladder cancer
Early Prediction of Bronchopulmonary Dysplasia Using Clinical Data From the First Three Postnatal Weeks in Preterm Infants: A Retrospective Study With Large Language Models
Trial ID: NCT07525167
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05-01
Completion date: 2026-12-31
Sponsor: Konya City Hospital
Conditions: Bronchopulmonary Dysplasia
Interventions: Artificial Intelligence-Based Risk Prediction
Locations: Konya City Hospital, İstiklal, Adana Çevre Yolu Cd. No:135/1
Countries: Turkey (Türkiye)
Enrollment: 108
Age range: 0 Days - 28 Days
Sex: ALL

Summary:
Early Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Clinical Data from the First Three Postnatal Weeks with Large Language Models: A Retrospective Study This retrospective, observational study aims to evaluate the early prediction of bronchopulmonary dysplasia (BPD) in preterm infants using clinical data from the first, second, and third postnatal weeks. The study includes infants born before 32 weeks of gestation or weighing less than 1,500 grams, followed at the Neonatal Intensive Care Unit of Konya City Hospital.

The study will compare the performance of different large language models (LLMs), including ChatGPT, Gemini, and Claude, in predicting BPD development. Clinical variables such as gestational age, birth weight, respiratory support, oxygen requirement, mechanical ventilation duration, and infection status will be used.

Primary outcome: Accuracy of BPD risk prediction by each AI model compared to actual clinical outcomes. Secondary outcomes: Sensitivity and specificity of predictions, weekly prediction performance, and comparative performance among AI models.

The results will provide insight into the potential clinical utility of AI-based approaches for early BPD risk assessment in preterm infants.

Eligibility criteria:
Inclusion Criteria:

* Preterm infants born before 32 weeks of gestation or with birth weight \<1,500 grams
* Admitted and followed in the Neonatal Intensive Care Unit (NICU) of Konya City Hospital
* Availability of complete clinical data in hospital records
* Documented bronchopulmonary dysplasia (BPD) outcome status

Exclusion Criteria:

* Presence of major congenital anomalies
* Incomplete or missing clinical data
* Death shortly after birth with insufficient follow-up data to determine BPD status

Outcomes:
Accuracy of bronchopulmonary dysplasia (BPD) risk prediction by artificial intelligence (AI) models in preterm infants.
Utilization of Artificial Intelligence in Supporting Physical Therapy Clinical Decision in Management of Myofascial Pain Syndrome Patients
Trial ID: NCT07342790
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-01
Completion date: 2027-04-01
Sponsor: Cairo University
Conditions: Myofacial Pain Syndrome; Clinical Decision Support
Interventions: dry needling; neuromuscular facilitation (PNF) stretching; strengthening exercises (isometric & dynamic); Transcutaneous electrical nerve stimulation; Ultrasound; Hot pack; Stretching exercises
Locations: Outpatient clinic, faculty of physical therapy, Cairo university
Countries: Egypt
Enrollment: 70
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The goal of this study is to investigate the effect of AI integration into clinical physical therapy clinical decision in improving cost effectiveness and clinical outcomes purposes of the study are:

1. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on management of pain in myofascial pain syndrome.
2. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on improving joint range of motion limitations in myofascial pain syndrome.
3. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on improving muscle strength in myofascial pain syndrome.
4. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on management of functional limitation in myofascial pain syndrome.
5. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on cost-effectiveness in physical therapy management of myofascial pain syndrome.

Eligibility criteria:
Inclusion Criteria:

* A- Demographic: Adult individuals 18-65 both sex

B- Pain Characteristics:

* Localized pain.
* Intensity: baseline pain score of 4 or higher on the VAS . C- Duration: chronic pain 3-6 months

D- Prescence of Myofascial Trigger Points (MTrPs):

E- Daily Functioning limitations: moderate or severe

Exclusion Criteria:

* • Severe cognitive impairment or illness.

  * Recent history of major surgery or trauma (within 3 months).
  * Other chronic conditions that could significantly interfere with the study.
  * Patients with fibromyalgia which may have the Key Diagnostic Criteria for Fibromyalgia Syndrome:

    1. Widespread Pain Index (WPI) (appendix (2): Measures the number of painful areas across the body. A score of 7 or more indicates a higher likelihood of FMS (Wang et al. ,2025).
    2. Symptom Severity Scale (SSS) (appendix3): Assesses the severity of symptoms such as fatigue, sleep disturbances, and cognitive difficulties. A score of 5 or more is indicative of FMS

Outcomes:
Pain Intensity changing
Validation of OphtAI Software Diagnostic Performance for Automated Screening of Diabetic Retinopathy, Diabetic Macular Edema, Glaucoma, ARM and ARMD: a Multicentre Study
Trial ID: NCT05752045
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-06-28
Completion date: 2024-03
Sponsor: Evolucare Technologies
Collaborators: Slb Pharma; Assistance Publique - Hôpitaux de Paris; BPIfrance
Conditions: Diabetic Retinopathy; Diabetic Macular Edema; Age-Related Macular Degeneration; Age-related Maculopathy; Glaucoma
Interventions: Eye Fundus Double Capture for Eye Diseases Screening with OphtAI Medical Device
Locations: Centre Aix Vision; CHU Brest; Centre Ophtalmologique Brétigny Essonne; CH SUD Francilien Corbeil Essonne; Centre d'examens de santé de la CPAM 93; Retinodiab Bourgogne; Clinique Honoré Cave; CHU Nantes; Retinodiab Franche-Comté; Diabète Occitanie; CHU Toulouse; OPHDIAT
Countries: France; France; France; France; France; France; France; France; France; France; France; France
Enrollment: 1389
Age range: 18 Years - None
Sex: ALL

Summary:
Evolucare OphtAI is a medical device offering automated, artificial intelligence powered, screening capabilities for Diabetic Retinopathy, Diabetic Macular Edema, Glaucoma, ARM and AMD, whose performances will by tested through the OphtAI-EVAL.

Eligibility criteria:
Doesn't not accept healthy volunteers as diabetic patients are needed, still they may be free of any eye diseases.

Inclusion Criteria:

The characteristics required for a subject to take part in the research are

* Male or female over 18,
* Type 1 or 2 diabetic,
* Presenting for screening for diabetic retinopathy,
* Beneficiary of a social security scheme,
* For whom written consent has been obtained for participation in the protocol.

Exclusion Criteria:

The following characteristics do not allow the subject to take part in the research:

* Patient with known DR, more severe than "minimal", including having been treated,
* Any other condition that, in the opinion of the health professionals, may interfere with their ability to complete the study or may present a significant risk,
* Presence of social, medical and/or psychological factors that may compromise the patient's adherence to the protocol,
* Simultaneously participating in another clinical research protocol or having recently participated in another research study for which the exclusion period would not be completed.

Patients who participate in this research will not be able to participate in another research at the same time. However, there is no exclusion period at the end of this research for participation in any other study.

Outcomes:
Referable Diabetic Retinopathy screening sensitivity/specificity