moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Impact of Dietary Intake on Continuous Glucose Patterns Considering Cardiorespiratory Fitness Levels.
Trial ID: NCT05845827
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-06-20
Completion date: 2023-12-31
Sponsor: Dartmouth-Hitchcock Medical Center
Conditions: Glucose Metabolism Disorders; Dietary Habits; Pediatric ALL; Adult ALL
Locations: Dartmouth-Hitchcock Medical Center
Countries: United States
Enrollment: 30
Age range: 14 Years - 22 Years
Sex: ALL

Summary:
Cardiorespiratory fitness (CRF), also known as "exercise capacity", is the capacity of respiratory and circulatory systems to supply oxygen to skeletal muscle during exercise for the generation of energy. Determinants of CRF include lung capacity, capillary density, cardiac output, hemoglobin concentration, and mitochondrial function. The research group studies how CRF is related to fuel utilization, yielding a mechanistic understanding of the association between lower CRF and worsening metabolic health via mitochondrial function. The objective of this study is to measure fuel utilization in response to habitual diet for one week in adolescents and young adults, ages 14-22 years (n=30). Fuel utilization will be estimated by glucose measures using a continuous glucose monitor (CGM). Habitual dietary intake will be collected via a mobile phone application (BiteAI, Inc) that uses artificial intelligence to extract nutrient information from food photographs. Participants will undergo two standard of care (SOC) meal tolerance tests at home - a glucose tolerance test and a Ensure® mixed meal tolerance test. CRF will be estimated by measuring maximum oxygen consumption (VO2 max) during a graded treadmill test. The hypothesis is that a higher VO2 max will be associated with increased fuel utilization, measured by lower glucose response to the SOC meal tolerance tests. The proposed study is described in the following aims:

SPECIFIC AIM 1. Test for feasibility of completion of self-report dietary food records, completion of two at home meal tolerance tests that are standard of care for metabolic health screening, and completion of a seven-day continuous glucose monitor.

SPECIFIC AIM 2. Identify nutrients and foods that are associated with an elevated glucose response.

SPECIFIC AIM 3. Assess the influence of VO2 max on the glucose response to the SOC glucose tolerance test and Ensure® mixed meal tolerance test.

Eligibility criteria:
Inclusion Criteria:

* Adolescents and young adults, ages 14-22 years

Exclusion Criteria:

* Previous diagnosis of type 1 or 2 diabetes
* Previous diagnosis of hyperlipidemia or other metabolic disease
* Use of medications known to affect glucose metabolism (metformin, oral steroids, sulfonylureas, insulin).
* Allergies to milk.
* Inability to participant in the maximal exercise test on the treadmill.
* Individuals not having android or IOS phones
* Individuals who cannot speak and/or write in English.

Outcomes:
Number of dietary records completed by each study participant; Number of days wearing the continuous glucose monitor; Completion of the at-home glucose tolerance test
Intelligent Digital Tools for Screening of Brain Connectivity and Dementia Risk Estimation in People Affected by Mild Cognitive Impairment
Trial ID: NCT05159661
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-03-01
Completion date: 2026-02-27
Sponsor: Oslo University Hospital
Collaborators: Aalto University; Accelopment AG; Alzheimer Europe; BrainSymph AS; Det Norske Veritas; Helsinki University Central Hospital; IRCCS San Raffaele Roma; Lurtis Rules S.L.; Neuroconnect Srl; Oslo Metropolitan University; Radboud University Medical Center; Tallinna Ülikool; Universidad Complutense de Madrid; Catholic University of the Sacred Heart
Conditions: Cognitive Dysfunction; Dementia; Mild Cognitive Impairment
Locations: Helsinki University Hospital; Università Cattolica del Sacro Cuore Campus di Roma; Scientific Institute for Research, Hospitalization and Healthcare; Oslo University Hospital; Universidad Complutense de Madrid
Countries: Finland; Italy; Italy; Norway; Spain
Enrollment: 1000
Age range: 60 Years - 75 Years
Sex: ALL

Summary:
Every three seconds someone in the world develops dementia. There are over 50 million people worldwide living with dementia and by 2030 this figure is expected to reach 82 million. Besides time-consuming patient investigations with low discriminative power for dementia risk, current treatment options focus on late symptom management. By screening brain connectivity and dementia risk estimation in people affected by mild cognitive impairment, the European Union (EU) funded AI-Mind project will open the door to extending the 'dementia-free' period by offering proper diagnosis and early intervention. AI-Mind will develop two artificial intelligence-based digital tools that will identify dysfunctional brain networks and assess dementia risk. Personalised patient reports will be generated, potentially opening new windows for intervention possibilities.

Eligibility criteria:
Inclusion Criteria:

* Male and female aged between 60 and 75 years
* MCI diagnosis with a MMSE \>= 25
* or MCI diagnosis with MoCa \>= 17

Exclusion Criteria:

* Confirmed dementia
* History of cerebrovascular disease (i.e. stroke episodes)
* Alcohol Use Disorder Identification Test (AUDIT) score positive
* Severe medical disorders associated with cognitive impairment (organ insufficiencies, chronic infections, endocrinological disorders)
* Severe head trauma with structural brain lesion and/or previous brain surgery;
* Severe mental disorders; Schizophrenia, known Major depression or bipolar disorder
* Neuroimaging evidence of other potential causes of cognitive decline (e.g. subdural haematoma, malignancy)
* History of malignancy \< 5 years;
* Recent use of psychotropic drugs including AChEI and Memantine (\< 3 months);
* Participation in trials with experimental drugs.

Outcomes:
AI-Mind Connector with estimated specificity and sensitivity value > 0.9; AI-Mind Predictor with estimated specificity and sensitivity value > 0.9.
Artificial Intelligence-Based Multimodal Data Integration for Tumor Microenvironment Analysis and Response Prediction in Hepatocellular Carcinoma Patients Undergoing TACE Combined With Immunotherapy and Targeted Therapy
Trial ID: NCT07584317
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05-18
Completion date: 2028-12-31
Sponsor: Gao-jun Teng
Conditions: Hepatocellular Carcinoma
Interventions: Artificial Intelligence
Enrollment: 1170
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to prospectively validate a retrospective cohort-derived AI-based multimodal model and explore tumor heterogeneity and the immune microenvironment to guide TACE combined with immunotherapy and targeted therapy in HCC.

Eligibility criteria:
1. Retrospective Study Cohort 1.1 Inclusion Criteria Age ≥18 years; Patients with hepatocellular carcinoma confirmed by histopathology or clinical diagnosis; At least one intrahepatic lesion that is repeatedly measurable according to RECIST v1.1.

   1.2 Exclusion Criteria Known sarcomatoid hepatocellular carcinoma or fibrolamellar hepatocellular carcinoma; Presence of other active malignancies within the past 5 years or concurrent active malignancies other than hepatocellular carcinoma; Missing preoperative imaging examinations, including CT or MRI, or poor image quality; Missing key baseline clinical data; Loss to follow-up after treatment.
2. Prospective Study Cohort 2.1 Inclusion Criteria Age ≥18 years; Patients with hepatocellular carcinoma confirmed by histopathology or clinical diagnosis; Scheduled to receive first-line TACE combined with immunotherapy and targeted therapy; At least one intrahepatic lesion that is repeatedly measurable according to RECIST v1.1; Expected survival of more than 3 months. 2.2 Exclusion Criteria Known sarcomatoid hepatocellular carcinoma or fibrolamellar hepatocellular carcinoma; Presence of other active malignancies within the past 5 years or concurrent active malignancies other than hepatocellular carcinoma; Other factors that, in the investigator's judgment, make the patient unsuitable for participation in this study; Severe allergy to iodinated contrast agents that preclude imaging examinations or TACE treatment.

Outcomes:
Prediction Performance of the AI Model
Validation of the Fracture Classification Accuracy (Ground Truth) of Anteroposterior X-ray of the Proximal Femur According to the Arbeitsgemeinschaft für Osteosynthesefragen/Orthopedic Trauma Association Classification Done by a Single Center: A Pilot Validation Study
Trial ID: NCT06351943
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-05-01
Completion date: 2025-06-30
Sponsor: AO Innovation Translation Center
Collaborators: University of Turin, Italy
Conditions: Proximal Femoral Fracture
Interventions: Fracture classification annotations provided by the Turin group; Fracture classification annotations provided by the AO expert surgeon group
Locations: AO Foundation
Countries: Switzerland
Enrollment: 2932
Age range: None - None
Sex: ALL

Summary:
The AO@AI Turin project is a collaborative project with a Turin group and the AO (Arbeitsgemeinschaft für Osteosynthesefragen, or in English, Association for the Study of Internal Fixation) foundation. An Image database (DB) has been built to host AP pelvic radiographs ready for artificial intelligence (AI) development.

The goal of this project is to determine the agreement between the Turin annotation of fracture status and the annotation from an external group of AO expert surgeons for a random subset of the Turin images.

Eligibility criteria:
Inclusion criteria

* Not applicable.
* The study utilizes the anonymized images in the Image database (DB). No patients will be enrolled for purposes of this study.

Exclusion criteria

• Not applicable.

Outcomes:
Annotations of fracture status of the image; In case of fracture, Arbeitsgemeinschaft für Osteosynthesefragen (AO, in English, Association for the Study of Internal Fixation)/Orthopedic Trauma Association (OTA) classification: Type; In case of fracture, AO/OTA classification: Group; In case of fracture, AO/OTA classification: Subgroup; In case of fracture, AO/OTA classification: Qualifier for 31A1.1; In case of fracture, AO/OTA classification: Qualifier for 31B2
Reasoning Artificial Intelligence Collaborate With Radiologists in Neurological Disease Interpretation and Diagnosis on CT and MRI
Trial ID: NCT07167043
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-05-01
Completion date: 2030-12-01
Sponsor: Yaou Liu
Conditions: Central Nervous System Disease; MRI; CT; AI (Artificial Intelligence); Radiology
Interventions: AI-assisted diagnostic systems
Locations: Beijing Tiantan Hospital
Countries: China
Enrollment: 30000
Age range: None - None
Sex: ALL

Summary:
This clinic trial aims to validate the working performance of radiologists with or without artificial intelligence (AI) diagnostic tool at neurological diseases diagnosis on brain CT/MRI. Routine diagnosis workflow in real clinical scenario including imaging reading, feature interpretation, differential diagnosis, writing initial report and optimizing revised version. And the gold standards of diagnosis are the histopathology references for brain tumors and the discharge diagnosis integrating all the examination results for the other neurological diseases. The performance of AI-assisted tools on diagnosing should be examined in a clinical process with multiple aspects identical to human radiologists' work before being transformed and putted to use. This study hypothesizes that AI models, trained with over 100,000 patient scans, are non-inferior to radiologists in neurological disease diagnosis on brain CT and MRI. For the secondary end-points, we investigate the performance of AI-radiologist collaboration of reasoning-enhanced AI-assisted systems. We hypothesize that, by visualizing the process of imaging interpretation and diagnosis, reasoning-enhanced AI can not only improve working performance of radiologists but also boost their trust in AI tools.

Eligibility criteria:
Inclusion Criteria:

* For MRI: patients suspected of harboring brain tumors or detected with brain occupancy at initiating or other institution, who subsequently underwent brain MRI.
* For CT: patients with or without neurological symptoms, suspected of harboring ischemic, hemorrhagic, space-occupiing, degenerative brain disease, or traumatic brain injury, who subsequently underwent brain CT.

Exclusion Criteria:

* Patients who opted-out or did not give permission to reuse clinical data.
* Patients with a history of prior brain surgery.
* Patients whose brain CT or MRI exhibit severe artifacts (e.g. heavy warping due to air, metal artifacts, heavy motion artifacts), thereby impeding the usage of the data.

Outcomes:
AI tools vs Radiologists from Clinical silence trial; AI-assisted diagnostic tool collaborates with Radiologists in clinical workflow
Performance Evaluation of a Computer-aided Measuring System for Colorectal Polyp Size Measurement: a Prospective Study
Trial ID: NCT06715384
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-15
Completion date: 2025-04-30
Sponsor: Changhai Hospital
Conditions: Colorectal Polyp; Colorectal Adenoma
Interventions: Polyp size measurement using autonomous AI measurement or AI-assisted human measurement with the CAM system
Locations: Changhai Hospital, Naval Medical University
Countries: China
Enrollment: 168
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
Accurate polyp size measurements are essential for risk stratification, selection of polypectomy techniques, and surveillance interval assignments. Evidence indicated that the clinical implementation of artificial intelligence is an optimal tool to improve the measurement of polyps during colonoscopy. This study aimed to evaluate the performance of a computer-aided measuring (CAM) system (EndoDASS) and compare its accuracy with routine sizing methods during real-time colonoscopy.

Eligibility criteria:
Inclusion Criteria:

1. Adults aged 18-75, any gender; 76-85 years eligible case-by-case based on health status.
2. Colonoscopy screening, surveillance, or diagnostic participants.
3. Informed consent obtained.

Exclusion Criteria:

1. Anticoagulant use (e.g., aspirin, warfarin) within 7 days prior to colonoscopy or coagulopathy.
2. Inflammatory bowel disease.
3. Aronchick score \>3 at entry.
4. Incomplete Case Report Form (CRF) data.
5. Emergency colonoscopy.
6. Pregnancy or lactation.
7. Gastrointestinal obstruction.
8. Refusal to participate.

Outcomes:
Evaluation of the computer-aided measuring (CAM) system
Development and Evaluation of an Artificial Intelligence Model for Cervical Cancer Detection From Colposcopic Images
Trial ID: NCT06644248
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-11
Completion date: 2025-02-11
Sponsor: Bangladesh University of Engineering and Technology
Conditions: Uterine Cervical Neoplasms
Interventions: Colposcopy
Locations: Ibn Sina Medical College Hospital
Countries: Bangladesh
Enrollment: 500
Age range: 18 Years - None
Sex: FEMALE

Summary:
Cervical cancer is a significant health issue, particularly in low-income countries, where late diagnosis and limited access to screenings contribute to high mortality rates. This study aims to develop and evaluate an artificial intelligence (AI) model to analyze colposcopic images for detecting cervical cancer more accurately and efficiently. Colposcopy, a procedure used to examine the cervix for signs of cancer, relies heavily on doctors' expertise, leading to inconsistent results. The current gold standard, colposcopy-directed biopsy, is invasive and can cause complications. The hypothesis is that an AI model can outperform traditional methods in identifying cervical abnormalities, providing a reliable and scalable solution for early detection, especially in underserved areas. By automating the analysis process, the AI model aims to reduce reliance on trained personnel, making cervical cancer screening more accessible and improving early diagnosis and treatment outcomes. The study will create a diverse dataset of colposcopy images from various sources and develop the AI model. The model's performance will be validated in clinical settings, assessing its accuracy in classifying cancer stages and identifying transformation zones. The impact on early detection, patient outcomes, and model usability will be evaluated, as well as its generalizability across different healthcare environments. The goal is to enhance the accuracy and efficiency of cervical cancer screening, ultimately reducing mortality rates and improving patient care.

Eligibility criteria:
Inclusion Criteria:

* Female patients of age 18 years or older can be selectedas subjects.
* Individuals willing to participate in cervical cancerscreening.
* Availability for colposcopic examination.
* Women with no history of hysterectomy (total removalof the uterus).
* Women with no current or prior diagnosis of cervicalcancer.
* Availability of relevant medical records forconfirmation and comparison purposes.

Exclusion Criteria:

* Pregnant women, given the potential impact onscreening results and the need for specialconsiderations during pregnancy.
* Individuals with severe medical conditions orcircumstances that may make colposcopic examinationinappropriate or unsafe.
* Patients with conditions that could interfere with theaccuracy of the screening results, such as severevaginal bleeding.
* Follow-up screenings.

Outcomes:
Swede Score Evaluation
Diabetic Retinopathy Screenings in General Practice for People With Type 2 Diabetes
Trial ID: NCT05276778
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-02-28
Completion date: 2023-11-14
Sponsor: Aalborg University
Conditions: Diabetic Retinopathy; Type2 Diabetes
Interventions: Diabetic Retinopathy Screening
Locations: Malene Krogh
Countries: Denmark
Enrollment: 300
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
Diabetic Retinopathy is a microvascular eye complication of diabetes, which can go unnoticed until permanent vision damage has occurred - in worst-case blindness. Timely retinopathy screenings reduce the risk of sight loss since the disease can be treated if detected in time. For people with type 2 diabetes, retinopathy screenings are typically performed by specialist at private ophthalmologists' practices, and individualized screening intervals based on retinopathy level and diabetes regulation are recommended. Unfortunately, 26% of people with type 2 diabetes do not follow their screening interval, and the attendance is too low compared to the national standard of minimum 90% of patients with diabetes who ought to follow the screening program. Consequently, actions must be taken to improve screening attendance in Denmark.

The aim of this study is to investigate patients' acceptance of diabetic retinopathy screenings in general practice. Patients' acceptance is explored through a questionnaire developed for the study.

Eligibility criteria:
Inclusion Criteria:

* Diagnosed with type 2 diabetes
* Understands Danish
* Age between 18-70

Exclusion Criteria:

* Blindness
* Cannot partake in second screening

Outcomes:
Patients' acceptance of diabetic retinopathy screenings in general practice
Multicenter Study Comparing the Artificial Intelligence-Based Navicam Endoscopic Capsule With the Conventional Pillcam Endoscopic Capsule in Small Bowel Pathology
Trial ID: NCT06462352
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-27
Completion date: 2025-12-31
Sponsor: Hospital Clinic of Barcelona
Collaborators: Spanish Society of Digestive Endoscopy
Conditions: Small Intestine Disease
Interventions: Navicam SB capsule; Pillcam SB3
Locations: Hospital General Universitario Dr Balmis; Hospital Clínic de Barcelona; Hospital del Mar; Institut de Recerca Sant Pau (IR-Sant Pau); Hospital Universitario de Galdakao; Hospital General Universitario Virgen la Arrixaca; Hospital General Universitario de Elche; Hospital Clínico San Carlos; Hospital General Universitario Gregorio Marañón; Hospital General Universitario Morales Meseguer; Hospital Universitario Son Espases; Hospital Universitario Nuestra Señora de Candelaria; Hospital Universitario Virgen Macarena; Hospital de Terrassa; Hospital General Universitario de Valencia; Hospital Universitario i Politècnic La Fé
Countries: Spain; Spain; Spain; Spain; Spain; Spain; Spain; Spain; Spain; Spain; Spain; Spain; Spain; Spain; Spain; Spain
Enrollment: 147
Age range: 18 Years - None
Sex: ALL

Summary:
Since its introduction in 2001, small bowel capsule endoscopy has been pivotal in diagnosing small bowel pathology due to its minimally invasive nature and high diagnostic accuracy. However, the technology has limitations, including prolonged reading times and the need for specialized endoscopists. The Navicam endoscopic capsule, leveraging artificial intelligence (AI) with ProScan™ for automated reading, promises to address these limitations by reducing reading times and enhancing diagnostic efficiency.

This study aims to assess the diagnostic concordance and to compare the efficiency of the AI-based Navicam capsule with the conventional Pillcam SB3 in the exploration of the small bowel.

Eligibility criteria:
Inclusion Criteria:

* Outpatients over 18 years of age with any clinical indication to undergo a small bowel exploration using capsule endoscopy at the participating hospital.

Exclusion Criteria:

* Patients with known small bowel strictures detected by computed tomography (CT) or magnetic resonance imaging (MRI).
* Patients with a history of esophagogastric and small bowel surgery (excluding ileocecal resection).
* Patients with a clinical contraindication for small bowel capsule endoscopy.
* Hospitalized patients.
* Patients with pacemaker or Implantable Cardioverter Defibrillator (ICD).
* Patients scheduled for a MRI within 15 days after capsule endoscopy ingestion.
* Pregnant or actively breastfeeding patients.
* Patients with swallowing disorders requiring endoscopic placement of the capsule.
* Simultaneous participation in another clinical trial using any investigational drug or device.
* Concurrent life-threatening pathology or condition.
* Inability to sign the informed consent form.

Outcomes:
Concordance correlation coefficient in small bowel exploration (per-patient analysis)