moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Intelligent and Educational System for Gestational Diabetes Management
Trial ID: NCT01850199
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2014-01
Completion date: 2015-09
Sponsor: Corporacion Parc Tauli
Collaborators: CIBER-BBN: Networking Research Center for Bioengineering.; Technical University of Madrid
Conditions: Gestational Diabetes
Interventions: Smart telemedicine remote monitoring for gestational diabetes; Usual management
Locations: Parc Tauli Sabadell University Hospital
Countries: Spain
Enrollment: 120
Age range: 18 Years - 46 Years
Sex: FEMALE

Summary:
Gestational diabetes, diabetes diagnosed during pregnancy, affects 8.8% of pregnancies in Spain that means more than 40,000 women per year. This prevalence is based on the National Diabetes Data Group criteria, previous to the 4th workshop on Gestational Diabetes (1998), but, if the new diagnosis criteria proposed by the International Associations of Diabetes and Pregnancy Study Groups, based on the most important study never made before on this topic, prevalence would increase to the double. When a women is diagnosed, the risk of complications for her and the child increases and, therefore, she has to start an specific diet and frequent visits to the diabetes center in order to check that glucose values do not exceed 95 mg/dl before or 140 mg/dl 1-hour after meals. In other case, she should start insulin treatment. Our project is aimed to develop intelligent tools based on neuro-diffuse techniques and integrated in a telemedicine system that allows control of gestational diabetes automatically, guaranteeing glucose control objectives consecution and avoiding face-to-face visits to the health care center. Furthermore, educational and motivation tools for a healthy behaviour will be included. At the end of the study efficacy and security about insulin management will be compare with the recommendations proposed by the diabetes team and data about direct and indirect costs will be calculated. The investigators anticipate that the smart telemedicine system can allow us to detect high blood glucose values earlier than in-person scheduled visits.

Eligibility criteria:
Inclusion Criteria:

* Pregnancy; Gestational diabetes diagnosed according the National Diabetes data Group Criteria.

Exclusion Criteria:

* Pregestational diabetes (diagnosed or suspected); Illiteracy; no computer connected to internet availability; unwillingness to participate in the study

Outcomes:
Median blood glucose (Interquartile range)
AI-Augmented Skin Cancer Diagnosis in Teledermatoscopy: A Prospective Randomized Study
Trial ID: NCT06080711
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-02-15
Completion date: 2024-10-30
Sponsor: Karolinska University Hospital
Collaborators: Karolinska Institutet; Medical University of Vienna; Stockholm School of Economics
Conditions: Skin Cancer; Melanoma
Interventions: AI assistance
Locations: Karolinska University Hospital
Countries: Sweden
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
In this study an artificial intelligence (AI) tool for skin cancer diagnosis is implemented in a teleldermatoscopy platform. The aim is to study the effects on clinician diagnostic accuracy, management decisions, and confidence. Furthermore, this prospective randomized study investigates the role of human factors in determining clinician reliance on AI tools and the consequent accuracy in a real-world setting.

Eligibility criteria:
Inclusion Criteria:

* Licensed physician
* Working at a dermatology clinic
* Sufficient knowledge in Swedish
* Written consent to participate

Exclusion Criteria:

* No experience of using dermatoscopy
* Does not wish to participate
* Incomplete answers
* Physicians that are involved in the patients' clinical care relating to the teledermoscopical consult

Outcomes:
Diagnostic accuracy; Accuracy of management decisions; Tendency to change initial diagnosis or management decision; Self-reported confidence in diagnosis and management decisions
Effectiveness of an AI-Assisted Camera-Based Progressive Muscle Relaxation Application in Office Workers: A Randomized Controlled Trial
Trial ID: NCT07571967
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-08
Completion date: 2026-06-01
Sponsor: Istanbul Medipol University Hospital
Conditions: Musculoskeletal Pain; Neck Pain; Stress, Physiological; Work Related Musculoskeletal Disorders
Interventions: AI-Assisted Camera-Based Progressive Muscle Relaxation Program
Locations: Istanbul Medipol University, Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation
Countries: Turkey (Türkiye)
Enrollment: 60
Age range: 20 Years - 40 Years
Sex: ALL

Summary:
This randomized controlled trial evaluates the effectiveness of an AI-assisted, camera-based progressive muscle relaxation application in office workers aged 20 to 40 years. Participants are randomly allocated to an intervention group or a control group. The intervention group completes a 4-week progressive muscle relaxation program using the PhysioTR Academic application, which provides camera-based real-time movement analysis and exercise guidance. The control group continues their usual daily routine and completes baseline and post-intervention assessments only. Outcomes include pain/tension intensity, musculoskeletal symptoms, perceived stress, neck disability, work productivity, functionality, and AI-based adherence metrics.

Eligibility criteria:
Inclusion Criteria:

* Aged between 20 and 40 years
* Actively working in a desk-based or office-based job
* Having access to a camera-enabled device such as a smartphone or computer
* Voluntary participation with informed consent
* Able to read and write Turkish
* Having a valid e-mail address

Exclusion Criteria:

* Previous surgery involving the neck or spine
* Cervical vertebral fracture or instability
* History of neurological disease such as multiple sclerosis or Parkinson's disease
* Vestibular system disorder
* Systemic inflammatory disease such as rheumatoid arthritis or ankylosing spondylitis
* History of malignancy
* Pregnancy
* Having received physiotherapy for the neck or back region within the last 6 months

Outcomes:
Change in Pain/Tension Intensity Assessed by Visual Analog Scale
Prediction Model Based on Deep Learning of CP-EBUS Multimodal Image in the Diagnosis of Benign and Malignant Lymph Nodes
Trial ID: NCT04328792
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2018-07-01
Completion date: 2020-12-31
Sponsor: Shanghai Chest Hospital
Conditions: Lymph Node Disease
Locations: Shanghai Chest Hospital
Countries: China
Enrollment: 1300
Age range: 18 Years - None
Sex: ALL

Summary:
Endobronchial ultrasound (EBUS) multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. In this study, EBUS multimodal image database of 1000 inthoracic benign and malignant lymph nodes (LNs) will be constructed to train deep learning neural networks, which can automatically select representative images and diagnose LNs. Investigators will establish an artificial intelligence prediction model based on deep learning of intrathoracic LNs, and verify the model in other 300 LNs.

Eligibility criteria:
Inclusion Criteria:

1. Chest CT shows enlarged intrathoracic LNs (short diameter \> 1 cm) or PET / CT shows patients with increased FDG uptake (SUV ≧ 2.0) in intrathoracic LNs;
2. Operating physician considered EBUS-TBNA should be performed on LNs for diagnosis or preoperative staging of lung cancer;
3. Patients agree to undergo EBUS-TBNA, sign informed consent, and have no contraindications.

Exclusion Criteria:

\- Patients having other situations that are not suitable for EBUS-TBNA.

Outcomes:
Diagnostic efficacy of EBUS multimodal artificial intelligence prediction model based on videos
Artificial Intelligent Guided Treatment of Atrial Fibrillation: A Randomized Controlled Pilot Study
Trial ID: NCT07069998
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-15
Completion date: 2028-05-26
Sponsor: East and North Hertfordshire NHS Trust
Conditions: Atrial Fibrillation (AF)
Interventions: virtual assistant; Standard AF care
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
Two-centre, prospective, randomized, open-label, controlled pilot study to examine whether integrating an artificial intelligence virtual assistant (VA), instructed with the latest international guidelines on optimal patient management into the outpatient management of patients diagnosed with atrial fibrillation (AF) in the last 6 months, is feasible, acceptable and effective in reducing the need for regular medical assessment and the healthcare burden, reduce variability, and meets with participant satisfaction, without compromising participant safety and overall care quality.

Eligibility criteria:
Inclusion Criteria:

1. Age ≥ 18 years
2. Documented diagnosis of AF within the last 6 months
3. Patient is at least mildly symptomatic. Presence of at least one of the symptoms like palpitation, dizziness, breathlessness, pre-syncope, and syncope with at least mild intensity
4. Ability to understand and comprehend the study rationale, design, and process.

Exclusion Criteria:

1. Severe comorbidities (such as physical impairment, cognitive impairment, and hearing loss) that could affect protocol adherence, in the opinion of the investigator
2. Chronic Kidney Disease (CKD) stage 4-5 or on dialysis
3. Medical illness with a life expectancy of less than 1 year
4. Alternative potentially reversible causes of AF (such as surgery, sepsis, or thyroid dysfunction)
5. Dementia or other cognitive impairment
6. Currently involved in another AF interventional clinical trial
7. Being pregnant

Outcomes:
How acceptable is the Virtual Assistant (VA) system as assessed by Sekhon's Theoretical Framework of Acceptability (TFA) questionnaire.; Patient-Reported Adherence to the Virtual Assistant (VA) System: 4-Item Feasibility Questionnaire
ARCHERY - Artificial Intelligence Based Radiotherapy Treatment Planning for Cervical, Head and Neck and Prostate Cancer
Trial ID: NCT05653063
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-12-07
Completion date: 2026-09-01
Sponsor: University College, London
Collaborators: National Institutes of Health (NIH); Rising Tide Foundation; Medical Research Council; Tata Memorial Hospital; Tata Memorial Centre; University of Stellenbosch; King Hussein Cancer Center; University of Malaya; University Ghent; M.D. Anderson Cancer Center; Mount Vernon Cancer Centre at Mount Vernon Hospital; University of Cape Town; National Cancer Institute (NCI)
Conditions: Cancer of Prostate; Cancer Head Neck; Cancer Cervix
Interventions: A web-based artificial intelligence (AI) auto-planning tool
Locations: Tata Medical Centre; Tata Memorial Hospital; King Hussein Cancer Center; University of Malaya Medical Center; Groote Schuur Hospital; Tygerberg Hospital
Countries: India; India; Jordan; Malaysia; South Africa; South Africa
Enrollment: 990
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of this study is to look at whether an Artificial Intelligence (AI) based computer program can automate two components of the radiotherapy treatment pathway to a sufficient quality standard to enable its routine clinical use. The two components include the delineation (outlining) of anatomical areas that are at risk of tumour spread and at risk of radiation damage, and the definition of the position, size and shape of the radiation beams.

The AI-based computer programs have been developed to perform tasks that would normally require direct human involvement by oncologists and medical physicists. Proposed advantages include improved treatment accuracy, as well as a reduction in the time (from weeks to minutes) and human resources needed to deliver radiotherapy, which this study will test.

Eligibility criteria:
Inclusion Criteria:

1. Consecutive patients with histologically confirmed head and neck cancers of the oropharynx, larynx, hypopharynx and nasopharynx (Stage I-III) that have given consent for radical radiotherapy.
2. Consecutive histologically confirmed primary cervical cancer patients (Stage IB-IIIB including pelvic node positive) that have given consent for radical radiotherapy.
3. Consecutive histologically confirmed primary prostate cancer patients (T1-4N0M0) that have given consent for radical radiotherapy.
4. Mental capacity to understand and consent to participate in the study.
5. Patients aged ≥18years.

Exclusion Criteria:

1. Patients requiring radiotherapy after curative surgery or surgery that is intended to remove as much of the tumour as possible.
2. Patients receiving palliative radiotherapy
3. Patients aged \< 18years.

Outcomes:
Proportion of radiotherapy treatment plans that have contours and dosimetry that meet pre-defined criteria for clinical acceptability
Risk Evaluation by Coronary Imaging and Artificial Intelligence-Based Functional Analyzing Techniques: Plasma Proteomic Profiles of Atheroma Classified by Intracoronary Imaging.
Trial ID: NCT06793787
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-07-01
Completion date: 2027-12-31
Sponsor: Ruijin Hospital
Conditions: Coronary Artery Disease; Coronary Atheroscleroses; Acute Coronary Syndromes (ACS); Chronic Coronary Syndrome
Locations: Ruijin Hospital, Shanghai Jiaotong University School of Medicine
Countries: China
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
This study is a single-center, prospective cohort study. The study is designed to identify novel circulating biomarkers for early prediction of high-risk coronary plaques. Patients diagnosed with chronic coronary syndrome (CCS) or non-ST-segment elevation acute coronary syndrome (NSTE-ACS), with marginal lesions or obstructive lesions in major coronary arteries detected by noninvasive coronary CT angiography (CCTA) or invasive coronary angiography (ICA), will be consecutively enrolled. Optical coherence tomography (OCT), with or without other intracoronary imaging modalities such as intravascular ultrasound (IVUS) and near infrared spectroscopy (NIRS), will be performed. Liquid chromatography-mass spectrometry (LC-MS/MS), bioinformatic analysis, and machine learning methods will be performed to characterize plasma proteomic profiles. The cohort will be followed-up every 3 months for 2 years. The association of novel biomarkers with the occurrence of major adverse cardiovascular events (MACEs) will be examined.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Patients with CCS or NSTE-ACS
* Receive CCTA scan or ICA, with marginal lesions (DS between 40%-69%) or obstructive lesions (DS ≥70% or CT-FFR/FFR \<0.8) in major coronary arteries
* Receive invasive coronary angiography and OCT, with or without other intracoronary imaging techniques such as IVUS and NIRS

Exclusion Criteria:

* Receive percutaneous coronary intervention (PCI) within 6 months
* Prior history of myocardial infarction or heart failure
* Prior history of coronary artery bypass graft (CABG)
* Abnormal liver function (serum alanine aminotransferase \[ALT\] level exceeding 3 times the upper limit of normal) or abnormal kidney function (eGFR ≤30%)
* Familial hypercholesterolemia
* Estimated survival ≤ 1 year
* Malignant tumor
* Pregnant or lactation, or have the intention to give birth within one year
* Poor compliance, unable to follow-up

Outcomes:
Prediction performance of high-risk plaques by novel biomarkers
Comparative Analysis of Diabetic Retinopathy Images by Retina Specialists Versus EyeStar's Artificial Intelligence Software of Images Captured by Pictor Plus Retinal Camera
Trial ID: NCT03912961
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-01-21
Completion date: 2019-10-15
Sponsor: Retina Global
Collaborators: VisionQuest Biomedical LLC
Conditions: Diabetic Retinopathy
Interventions: Picture of the retina with a handheld retinal camera
Locations: Centro Medico Kolping; Clinica Incor; Hospital Universitario Japones
Countries: Bolivia; Bolivia; Bolivia
Enrollment: 1034
Age range: 18 Years - None
Sex: ALL

Summary:
In this clinical trial, we plan to evaluate the usefulness of artificial intelligence (AI) software paired with a handheld retinal camera to compare diabetic retinopathy status in Bolivian patients as read by retina specialists versus the AI software.

Eligibility criteria:
Inclusion Criteria:

1. Male or female subjects who are at least 18 years old who have full legal capacity to volunteer on the date the informed consent is signed;
2. Subjects who can follow the instructions by the clinical staff at the clinical site, and can attend examinations on the scheduled examination dates;
3. Subjects who have been diagnosed with Type I or Type II diabetes mellitus.

Exclusion Criteria:

1. Subjects unable to tolerate ophthalmic imaging;
2. Subjects who have been scheduled for but have not yet undergone the following surgeries: Cataract Surgery (including any related follow-up procedures) or Vitrectomy.
3. Subject blind in one eye due to any cause
4. Subjects who have a condition or a situation, which in the investigator's opinion, may put the subject at increased risk, confound study data, or interfere significantly with the subject's study participation.

Outcomes:
Number of subjects where the image readout by retina specialists match the readouts by EyeStar AI system for identifying diabetic eye disease
AI-assisted Quality Control Study of Multimodal Data in the Epidemiological Survey of Shanghai Nicheng Cohort Study
Trial ID: NCT06961461
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-01
Completion date: 2025-08-15
Sponsor: Shanghai 6th People's Hospital
Conditions: Quality Control; Cohort Studies
Interventions: AI-assisted quality control tool
Enrollment: 900
Age range: None - None
Sex: ALL

Summary:
This study is based on the Nicheng Cohort study. This study intends to analyze whether AI assistance can effectively improve the efficiency and accuracy of quality control of data collected in large-scale epidemiological surveys based on traditional quality control processes.

Eligibility criteria:
Inclusion Criteria:

1. Be proficient in using computers;
2. The person responsible for questionnaire quality control needs to have good dialect recognition ability;
3. Have a basic understanding or high acceptance of AI-assisted tools, and be able to adapt to the learning and application of new technologies
4. Be able to participate in the research throughout the process, abide by the research process, receive training, and be willing to complete quality control tasks as required.

Exclusion Criteria:

1. The person responsible for questionnaire quality control cannot understand or recognize Shanghai Nanhui dialect proficiently;
2. Unfamiliar with AI-assisted tools and difficult to accept technical operations;
3. Unable to participate in the research, receive training or complete the specified tasks due to other work or academic reasons;

Outcomes:
Accuracy
Level of Ghrelin With Anorexia Nervosa (AN) Patients. Subgroup AN: With and Without Tubefeeding.
Trial ID: NCT00825461
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2009-03
Completion date: 2012-12-31
Sponsor: Maastricht University Medical Center
Conditions: Anorexia Nervosa
Locations: University Hospital Maastricht
Countries: Netherlands
Enrollment: 20
Age range: 12 Years - 35 Years
Sex: FEMALE

Summary:
The PICOWO research group in corporation with the University of Amsterdam (Artificial Intelligence) has developed a computer simulationmodel of eating disorders (Bosse, Delfos, Jonker \& Treur, 2003). In a running Phd project we want to add ghrelin to the model. The idea for this current research is to collect clinical information about the functioning and (patho)physiology of ghrelin in patients with AN, so we can extend the simulation.

Eligibility criteria:
Inclusion Criteria:

* Studypopulation: Patiënts: all female, non -age coupled.
* Subgroup patients without tube feeding, with tube feeding and a healthy matched control group.
* Control group: healty volunteers of the University of Maastricht.

Exclusion Criteria:

* Patients with a history of eating disorder different than AN or a psychiatric history.
* Control group: no history of eating disorders or other psychiatric disabilities. Normal physical examination, ecg, laboratorium (blood cells, liver, kidneys)

Outcomes:
Concentration of ghrelin in blood.
Effectiveness and Usability of a Chatbot-delivered Screening and Brief Intervention for Alcohol Reduction in Working-age Adults: A Proof-of-concept Randomised Controlled Trial
Trial ID: NCT06602882
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-01
Completion date: 2025-06-30
Sponsor: The University of Hong Kong
Conditions: Excessive Alcohol Consumption
Interventions: chatbot-delivered screening and brief intervention
Locations: School of Nursing
Countries: Hong Kong
Enrollment: 300
Age range: 18 Years - 59 Years
Sex: ALL

Summary:
Alcohol abuse led to 5.3% of all deaths and 5.1% of all disability-adjusted life years globally in 2016, representing a heavier public health burden than diabetes, tuberculosis or HIV/AIDS (as documented in the World Health Organization (WHO) Global Status Report on Alcohol and Health). The increasing consumption of alcohol for a few decades has led to a higher risk of cirrhosis, cancers, hypertension, and cardiovascular and cerebrovascular diseases. Strengthening of the prevention and treatment of alcohol abuse has been incorporated in the Sustainable Development Goals (SDG3) by the United Nations.

Strong evidence from a meta-analysis demonstrated the efficacy of screening and brief intervention (SBI) in reducing weekly alcohol consumption. Although SBI is known to be effective in reducing alcohol consumption in at-risk drinkers, barriers to implementing SBI have been an issue. A systematic review identified that common barriers to the routine delivery of SBI by doctors and nurses included a lack of alcohol-related knowledge, time, confidence, ability, and incentive to intervene; worrying about offending patients; and SBI being an uncomfortable and frustrating task.

To scale up behavioural change interventions in primary care for expanding the scalability and reachability, artificial intelligence (AI) and AI-chatbots have been increasingly used in recent years. A systematic review showed that chatbots for mental health counselling were effective and safe. Other reviews also reported that chatbots might improve physical activity, diet, and weight management and oncology care. However, having searched PubMed and the Cochrane Library, there was no a randomised controlled trial on the use of an AI-chatbot for alcohol reduction.

Eligibility criteria:
Inclusion Criteria:

1. aged 18-59 years
2. being employed in the past 12 months
3. a modified Alcohol Use Disorders Identification Test (AUDIT-C) score ≥3
4. having a smartphone with internet access
5. able to communicate in Chinese

Exclusion Criteria:

1. mentally and/or
2. physically unable to complete the intervention and/or the two follow-ups
3. undergoing any alcohol-related intervention

Outcomes:
Alcohol consumption in grams per week