moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Development and Validation of an Automated Self-administered Visual Acuity System
Trial ID: NCT06540001
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-01
Completion date: 2025-08-01
Sponsor: Tan Tock Seng Hospital
Collaborators: Singapore Institute of Technology; Nanyang Technological University
Conditions: Visual Impairment
Interventions: Automated visual acuity
Enrollment: 100
Age range: 21 Years - 100 Years
Sex: ALL

Summary:
Visual acuity tests, commonly conducted in clinics and used for health screenings, are becoming more in demand due to an aging population. Current online self-eye check apps are limited as they don\&#39;t accurately reflect true distance vision assessed in clinical settings. These tests, performed by trained personnel, are time-consuming and can cause delays in clinics. This project aims to develop an automated Visual Acuity (VA) station using AI technologies like speech-to-text and computer vision, hypothesizing that it can match the accuracy of manual assessments by clinic staff, thus potentially reducing waiting times and improving efficiency.

Eligibility criteria:
Inclusion Criteria:

1. Patients age \&gt;21 and able to give consent
2. Patients who have at least counting finger vision
3. Patients who is able to speak in an audible and clear voice
4. Patients who is able to use a digital device independently (e.g. handphone)

Exclusion Criteria:

1. Patients on wheelchair/ walking aids
2. Patients with hearing difficulties
3. Patients with speech difficulties
4. Patients who have cognitive impairment
5. Patients who are hemiplegic/ motor dysfunction
6. Patients who have vision worse than counting fingers
7. Patients who are pregnant

Outcomes:
Best corrected visual acuity with and without pinhole using Snellen letters and numbers
Feasibility of Artificial Intelligence-based Heart Function Prediction Model Using Chest Radiography
Trial ID: NCT04996381
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-03-01
Completion date: 2022-09-01
Sponsor: Yonsei University
Conditions: Chest X-ray for Clinical Evaluation
Interventions: Scanning Chest X-rays and performing AI algorithms on images
Locations: Yongin Severance Hospital
Countries: South Korea
Enrollment: 505
Age range: 20 Years - 90 Years
Sex: ALL

Summary:
The investigators will develop an artificial intelligence model to predict left ventricular ejection fraction using chest radiographic images and transthoracic echocardiography data.

Eligibility criteria:
Inclusion Criteria:

* Adults who are 20 years and older
* Patient who visited the emergency room or outpatient clinic due to dyspnea and chest pain

Exclusion Criteria:

* Patient refusal
* Uncertain radiographs or transthoracic echocardiography
* Uncertain tests results

Outcomes:
Left Ventricular Ejection Fraction < 40%
Automatic Evaluation of Inflammation Activity in Ulcerative Colitis Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence
Trial ID: NCT04131530
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-10
Completion date: 2019-12
Sponsor: Shandong University
Conditions: Ulcerative Colitis; Artificial Intelligence; Confocal Laser Endomicroscopy
Interventions: The diagnosis of Artificial Intelligence and endoscopist
Locations: Department of Gastroenterology, Qilu Hospital, Shandong University
Countries: China
Enrollment: 60
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables to evaluate the inflammation activity of ulcerative colitis with excellent correlation with histopathology. However this requires much experience, which limits the application of pCLE. The investigators designed a computer-aided diagnosis program using deep neural network to make diagnosis automatically in pCLE examination and contrast its performance with endoscopists.

Eligibility criteria:
Inclusion Criteria:

* aged between 18 and 80; diagnosed as UC

Exclusion Criteria:

* Patients under conditions unsuitable for performing CLE including coagulopathy , impaired renal or hepatic function, pregnancy or breastfeeding, and known allergy to fluorescein sodium; Inability to provide informed consent

Outcomes:
The diagnosis efficiency of Artificial Intelligence
Evaluating the Feasibility of Artificial Intelligence Algorithms in Clinical Settings for Classification of Normal, Wheeze and Crackle Sounds Acquired From a Digital Stethoscope
Trial ID: NCT05268263
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-01-06
Completion date: 2022-02-22
Sponsor: Innova Smart Technologies (Pvt.) Ltd
Collaborators: Lady Reading Hospital, Pakistan; NOABIO LLC
Conditions: Respiratory; Lung
Interventions: Artificial Intelligence Algorithm
Locations: Lady Reading Hospital, Pakistan
Countries: Pakistan
Enrollment: 60
Age range: None - None
Sex: ALL

Summary:
Assessing the feasibility and testing the accuracy of the developed artificial intelligence algorithms for detection of wheezes and crackles in patients with lung pathologies in clinical settings on unseen local patient data acquired through three digital stethoscopes.

Eligibility criteria:
Inclusion Criteria:

* Ages all
* Written consent provided

Exclusion Criteria:

* Subject condition unstable
* Chest wall deformity or wounds in adhesive application areas
* Written consent not provided

Outcomes:
Testing the accuracy of artificial intelligence models for detection of wheeze, crackles, and normal lung sounds by measuring the sensitivity and specificity; Clinical validation of AI models for detection of wheeze, crackles, and normal lung sounds by comparison with gold standard
Muscle MRI Outlining of Neuromuscular Diseases Using Artificial Intelligence
Trial ID: NCT06917430
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-01
Completion date: 2035-01-01
Sponsor: Rigshospitalet, Denmark
Conditions: Becker Muscular Dystrophy; FSHD - Facioscapulohumeral Muscular Dystrophy; Hypokalemic Periodic Paralysis
Interventions: No intervention
Enrollment: 120
Age range: 18 Years - None
Sex: ALL

Summary:
Background and aim:

Neuromuscular diseases encompass a range of conditions affecting muscle cells, nerves, or the interaction between the two. A common pathological feature of these conditions is the pro-gressive replacement of muscle tissue with fat, which can be visualised using magnetic reso-nance imaging (MRI). MRI-based fat quantification serves as a key biomarker for disease characterisation, progression tracking, and treatment assessment. Currently, manual segmenta-tion of MRI scans for fat quantification is very time-consuming, requiring individual muscle delineation. Therefore, an artificial intelligence (AI) model is being developed to automate the segmentation. The aim of this study is to validate this AI model and assess its possibilities and limitations.

Method:

The study is ongoing. Retrospective MRI scans of patients with four different muscle diseases (anoctaminopathy, Becker muscular dystrophy, facioscapulohumeral muscular dystrophy, and hypokalemic periodic paralysis) are collected and manual delineation used for training the AI-model is being performed. The intramuscular fat fraction of individual muscles of the pelvis, thigh, and calf will be analysed using the AI model. The performance of the AI model will be compared to manual segmentation. The AI will be evaluated on metrics such as segmentation accuracy and time efficiency.

Eligibility criteria:
Inclusion Criteria:

* Genetically verified diagnosis of neuromuscular diseases.
* Age above 18 years

Exclusion Criteria:

* Contraindications to perform an MRI
* Competing disorders and other muscle disorders, which may alter measurements. The investigator will decide whether the competing disorder can significantly influence the results

Outcomes:
Difference in fat fraction between manual and AI outlining.
Speech-derived Digital Biomarkers for Assessing Mild Cognitive Impairment and Alzheimer's Disease Diagnosis Accuracy
Trial ID: NCT06994767
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-19
Completion date: 2026-01-09
Sponsor: Hackensack Meridian Health
Collaborators: Canary Speech Inc.
Conditions: Mild Cognitive Impairment (MCI); Alzheimer Disease
Interventions: The Canary Application
Locations: Hackensack University Medical Center
Countries: United States
Enrollment: 150
Age range: 50 Years - None
Sex: ALL

Summary:
The goal is to assess the accuracy of an application that analyzes voice characteristics to diagnose patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). The main question is whether the application's diagnosis is the same as the clinician's for MCI and AD patients.

Eligibility criteria:
Inclusion Criteria:

* Males and females between the ages of 50 and above
* Willing and capable of participating in all aspects of the study
* Willing and capable of giving informed consent
* Diagnosis of MCI or Alzheimer's by the physician
* For the control group: Without diagnosis of MCI or Alzheimer's or other types of dementia
* Subjects are free of any medical condition(s) that, according to exclusion criteria, might audibly affect voice characteristics
* Subject must be able to speak and read with fluency in the English language.

Exclusion Criteria:

* All participants below 50 years
* Failure to provide informed consent
* Individuals with the following medical conditions will be excluded:

  i) Parkinson's disease ii) Huntington's disease iii) Cancer and active cancer treatments
* Persons with speech characteristics (heavy accents, slurring, stuttering, etc.) that would interfere with voice recording or voice analysis

Outcomes:
The primary objective of the study is to assess the sensitivity biomarkers in patients with MCI and AD diagnosis; The primary objective of the study is to assess the specificity of vocal biomarkers in patients with MCI and AD diagnosis
Improving Prostate Lesion Classification and Diagnostic Accuracy Using Machine Learning: A Comprehensive Evaluation and Development of a PI-RADS 3 Classifier
Trial ID: NCT06116344
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-01-01
Completion date: 2023-08-24
Sponsor: Paracelsus Medical University
Conditions: Prostate Cancer
Locations: Department of Radiology and Nuclear Medicine, Klinikum Nuernberg, Paracelsus Medical University, Germany
Countries: Germany
Enrollment: 173
Age range: 18 Years - 90 Years
Sex: MALE

Summary:
The investigators propose an AI methodology combining machine learning, histological results and expert image interpretation for the development of a PI-RADS 3 classifier.

Eligibility criteria:
Inclusion Criteria:

1. Only patients with a clinical indication for mp prostate MRI will be included in this prospective study.
2. No allergies to GBCA

Exclusion Criteria:

1\. Contraindications for MRI

Outcomes:
Normalized Quantitative Signal - Intensity - Measurements with Region of Interest drawn in specific T2-weighted axial MRI Images; Quantitative Signal - Intensity - Measurements with Region of Interest in specific in Apparent diffusion coefficient (ADC) axial MRI Images; Quantitative Signal - Intensity - Measurements with Region of Interest in specific in high b-value (800, 1500, 4000) axial MRI Images; Signal - Intensity - Measurements with Region of Interest in specific dynamic contrast enhanced (DCE) MRI Images
AI for the Prediction of Obesity-Related Vascular Diseases - Validation Study
Trial ID: NCT06595134
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-07-01
Completion date: 2028-07
Sponsor: University Hospital, Bonn
Collaborators: European Union
Conditions: Acute Cardiovascular Disease
Interventions: Non Interventional
Locations: Medical University Vienna; University Hospital Leuven; University Hospital Pilsen; University Hospital Bonn; University Hospital Mannheim; University Hospital Zürich
Countries: Austria; Belgium; Czechia; Germany; Germany; Switzerland
Enrollment: 1200
Age range: 45 Years - None
Sex: ALL

Summary:
The study is aimed at patients who suffer from severe obesity and receive a computerised tomography (CT) scan of the heart. A total of 1200 patients across Europe are to take part in the study. It is a multi-centre, controlled, open, randomised study. As part of the study, a citizen app will be installed on a smartphone for randomly selected study participants. A fitness tracker will also be issued so that certain parameters, such as heart rate and daily number of steps, can be recorded.

Eligibility criteria:
Inclusion Criteria:

* Age greater than or equal to 45 years at the time of signing informed consent
* BMI greater than or equal to 30 kg/m2
* Patients with suspected CVD (\>15% pretest probability \[ESC guidelines\]), referred for cardiac CT examinations (consisting of coronary calcium scoring and coronary CT angiography)
* Informed consent of the patient

Exclusion Criteria:

* Any of the following CV conditions within 2 months prior to study inclusion: myocardial infarction, stroke,
* Hospitalization for unstable angina pectoris or transient ischemic attack or due to congestive heart failure.
* Planned coronary, carotid, or peripheral artery revascularisation known on the day of inclusion
* Presently classified NYHA IV heart failure
* Having uncontrolled diabetes mellitus (HbA1c ≥ 11%) at day of inclusion
* Having uncontrolled hypertension (SBP ≥ 160 mmHg and/or DBP ≥ 100 mmHg) as measured at inclusion
* Having severe renal impairment measured as an eGFR \< 30 mL/min/1.73 m2 at inclusion
* Alanine aminotransferase (ALT) or alkaline phosphatase (APT) level \< 3.0 x the upper limit of normal (ULN)for the reference range
* Total bilirubin level \>1.5 x the ULN for the reference range
* History of an active or untreated malignancy or are in remission from a clinically significant malignancy for less than 5 years
* Previous organ transplantation or awaiting an organ transplant
* Pregnancy or breastfeeding

Outcomes:
An improved accuracy in prediction of CVD progression by us-ing the AI-POD risk score; An improved accuracy in prediction of CVD progression by us-ing the AI-POD risk score; An improved accuracy in prediction of CVD progression by us-ing the AI-POD risk score
West Hertfordshire Inflammatory Bowel Disease Technology Study
Trial ID: NCT05000242
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-01-31
Completion date: 2026-03-01
Sponsor: Perspectum
Collaborators: West Hertfordshire Hospitals NHS Trust
Conditions: Inflammatory Bowel Diseases
Locations: West Hertfordhsire Teaching Hospitals NHS Trust
Countries: United Kingdom
Enrollment: 200
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
A retrospective and prospective, observational, non-interventional, cohort study to develop quantitative metrics from tools used as standard of care when diagnosing, assessing and monitoring patients with inflammatory bowel disease.

Eligibility criteria:
Inclusion Criteria:

* Male or female aged 16 or over
* Patients with known or suspected Inflammatory Bowel Disease scheduled to undergo clinical, endoscopic or radiological assessment
* Patients with known Inflammatory Bowel Disease under ongoing clinical monitoring, who have previously undergone clinical, endoscopic or radiological assessments.
* Participant willing and able to give informed consent for participation in the study

Exclusion Criteria:

* Participant who is, or suspects that they are, pregnant at the time of assessment
* Any other cause, including significant disease or disorder which, in the opinion of the investigator, may either put the participant at risk because of participation in the study, or may influence the participant's ability to participate in the study

Outcomes:
Development and presentation of computer generated metrics derived from endoscopic videos.; Development and presentation of computer - generated metrics derived from histology slides.; Development and presentation of computer generated metrics derived from Magnetic Resonance Elastography/ Computerised Tomography Enterography / Magnetic Resonance Imaging Pelvis scans.
Assisting Pulmonary Disease Diagnosis With Ophthalmic Artificial Intelligence Technology
Trial ID: NCT05847894
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-06-29
Completion date: 2026-05
Sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University
Collaborators: The First Affiliated Hospital of Guangzhou Medical University; Shenzhen Third People's Hospital; Guangzhou Kindness Health Care Center (Guangzhou Jiubang Shanxin Clinic Ltd), Guangzhou, China
Conditions: Pulmonary Diseases; Ophthalmological Diagnostic Techniques; Artificial Intelligence
Interventions: Ophthalmic examination; Pulmonary Examination
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen University; Guangzhou Kindness Health Care Center (Guangzhou Jiubang Shanxin Clinic Ltd); the First Affiliated Hospital of Guangzhou Medical University; Shenzhen Third People's Hospital
Countries: China; China; China; China
Enrollment: 10000
Age range: None - None
Sex: ALL

Summary:
This study intends to collect ophthalmologic examination results, pulmonary examination results and related indexes from patients with pulmonary disease and control populations, and combine big data analysis and artificial intelligence technology to explore whether new methods can be provided for early screening strategies for pulmonary disease with the aid of ophthalmologic examination, and thus assist in identifying the types of pulmonary disease and determining disease prognosis.

Eligibility criteria:
Inclusion Criteria:

* Those aged ≥18 years; or those aged \<18 years who can cooperate with the relevant examination and are accompanied and informed by a guardian;
* People with respiratory-related diseases who were to undergo pulmonary examination, or those who volunteered to participate in the trial through publicity recruitment;
* expected survival time of 3 months or more;
* Those with no previous serious underlying disease and no history of serious eye disease;
* Those who can cooperate with ophthalmologic and pulmonary-related examinations and have regular follow-up examinations;
* Those who gave informed consent to the study prior to the trial and voluntarily signed the informed consent form;
* Other conditions that can be included in the study as judged by the investigator.

Exclusion Criteria:

* Patients who are unable to complete ophthalmology or pulmonary-related examinations and regular follow-ups due to serious diseases, trauma or surgery (serious ophthalmology diseases such as extremely poor vision that cannot be fixed, ocular atrophy, severe refractive interstitial clouding that prevents fundus photography, etc.);
* People with poor compliance due to various reasons such as alcohol or drug dependence, or mental disorders;
* Those without informed consent;
* Other conditions judged by the investigator to be unsuitable for participation in the trial.

Outcomes:
Area Under the Receiver Operating Characteristic curve
Assessing the Accuracy of ChatGPT-4 in Interpreting Arterial Blood Gas Results
Trial ID: NCT06456866
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-06-14
Completion date: 2024-12-02
Sponsor: Kanuni Sultan Suleyman Training and Research Hospital
Conditions: Blood Gases
Interventions: interpretation
Locations: Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital
Countries: Turkey (Türkiye)
Enrollment: 398
Age range: 18 Years - None
Sex: ALL

Summary:
Assessment of Acidosis and Alkalosis, Evaluation of Hypoxemia and Hyperoxemia, Evaluation of Hemoglobin Parameters, Assessment of Electrolytes, Evaluation of Metabolic Parameters (Glucose, Lactate, Bilirubin)

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 and above will be included in the study.
* The study will evaluate arterial blood gas results.

Exclusion Criteria:

* Venous blood gas results,
* Blood gas results with calibration errors,
* Blood gas results with incomplete data

Outcomes:
blood gases sample interpretation
Artificial Intelligence-powered Low-Dose Computed Tomography for Screening of Pancreatic Cancer
Trial ID: NCT07117045
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-15
Completion date: 2032-12-30
Sponsor: Changhai Hospital
Collaborators: The Affiliated People's Hospital of Ningbo University; Jiaxing University Affiliated Second Hospital; Meinian Onehealth Healthcare Holdings Co., Ltd; Ruici Medical Examination Institution
Conditions: Pancreatic Cancer; Intraductal Papillary Mucinous Neoplasm; High-grade Pancreatic Intraepithelial Neoplasia; PDAC - Pancreatic Ductal Adenocarcinoma; Mucinous Cystic Neoplasm
Interventions: Diagnostic Evaluation for Positive AI Findings
Locations: Meinian Onehealth Healthcare Holdings Co., Ltd; Ruici Medical Examination Institution; Changhai Hospital; Jiaxing University Affiliated Second Hospital; Ningbo University Affiliated People's Hospital
Countries: China; China; China; China; China
Enrollment: 400000
Age range: 50 Years - None
Sex: ALL

Summary:
Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with early diagnosis crucial for improving survival. Due to the absence of effective screening methods, most patients are diagnosed at advanced stages. The population undergoing low-dose computed tomography (LDCT) screening significantly overlaps with those at high risk for PDAC; however, traditional imaging methods have limited sensitivity for detecting pancreatic lesions. This study utilizes the Pancreatic Cancer Detection with Artificial Intelligence (PANDA) system to enhance LDCT for pancreatic cancer screening in a prospective, multicenter, observational cohort. PANDA will analyze LDCT images, followed by a multidisciplinary team (MDT) reassessment of abnormal interpretations. Based on MDT evaluation, individuals will be recalled for further examination, placed under a personalized follow-up plan, or monitored for at least one year. The primary outcomes include pancreatic cancer detection rate, positive predictive value, consensus rate, and recall rate, while secondary outcomes focus on early-stage cancers, resectable tumors, and safety indicators such as false positive rates and unnecessary procedures. This study aims to assess the effectiveness and safety of AI-assisted LDCT for PDAC detection, providing a practical solution for improving public health and enhancing early diagnostic capabilities.

Eligibility criteria:
Inclusion Criteria:

1. Age 50 years and above.
2. Voluntary signing of informed consent.
3. Completion of LDCT examination.

Exclusion Criteria:

1. Previous history of pancreatic cancer.
2. Abdominal inflammation or diagnosis of acute pancreatitis within 6 months.
3. Poor image quality due to ascites, pancreatic trauma, thoracic/abdominal surgery, radiotherapy or chemotherapy.
4. Research subjects unable to complete follow-up due to physical or other reasons.

Outcomes:
Pancreatic cancer detection rate; Positive predictive value; Consensus rate; Recall rate