moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Comparative Analysis of AI Software and Conventional Colonoscopy for Enhanced Polyp Detection and Diagnosis: A Randomized Controlled Trial
Trial ID: NCT06077435
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-03-01
Completion date: 2026-06-01
Sponsor: Sahlgrenska University Hospital
Conditions: Colonic Polyp; Colon Lesion
Interventions: AI
Locations: Sahlgrenska University Hospital
Countries: Sweden
Enrollment: 915
Age range: 50 Years - 90 Years
Sex: ALL

Summary:
Purpose \& Research Questions

The purpose of this study is to evaluate whether artificial intelligence (AI) improves the detection of polyps and whether the system can classify the type and severity of detected changes. The investigators will also assess if there are any differences between the various AI systems and whether the polyps that may be missed are benign or malignant.

Eligibility criteria:
Inclusion Criteria:

* Age \> 50 years
* Elective colonoscopy

Exclusion Criteria:

* Patient declines to participate in the study.
* Age \< 50 years
* Unprepared bowel

Outcomes:
To compare the effectiveness of AI systems in characterizing polyps with conventional histology; Comparison of Adenoma Detection Rates (ADR) between colonoscopies assisted by Artificial Intelligence (AI) and conventional colonoscopies without AI; To assess and compare the costs associated with AI-assisted colonoscopy versus conventional colonoscopy.
A Study to Assess the Clinical and Cost-effectiveness of the Galen Prostate Artificial Intelligence Histology System in Diagnosing Clinically Important Prostate Cancer on Prostate Biopsy Tissue.
Trial ID: NCT05228197
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-03-11
Completion date: 2025-04-30
Sponsor: Imperial College London
Collaborators: National Institute for Health Research, United Kingdom
Conditions: Prostatic Neoplasms
Interventions: Biopsy & Imaging
Locations: University Hospitals Coventry and Warwickshire Nhs Trust; Chelsea and Westminster Hospital Nhs Foundation Trust - Chelsea; Chelsea and Westminster Hospital Nhs Foundation Trust - West Middlesex; Imperial College Healthcare Nhs Trust; University College London Hospitals Nhs Foundation Trust; University Hospital Southampton Nhs Foundation Trust
Countries: United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 750
Age range: 18 Years - None
Sex: MALE

Summary:
The primary objective is to determine whether the Galen Prostate AI system has sufficient diagnostic accuracy and health economic value to be used for triage of pathology slides within the NHS.

Eligibility criteria:
Inclusion Criteria:

* Patients with a prostate (either cis-male gender or trans-female gender with no prior hormone use at all).
* Age 18 years or above.
* Undergoing prostate biopsy as a result of an elevated serum PSA or abnormal digital rectal exam, who have undergone a pre-biopsy multi-parametric MRI and advised to undergo prostate biopsies.

(Please note: the Calibration stage requires patients who have already undergone a biopsy and the pathology has been processed over the prior 0 to 12 months).

Exclusion Criteria:

* Unwilling or unable to give consent.
* Any duration or type or dose of androgen deprivation therapy in the 6 months prior to screening.
* Any prior radiotherapy to the prostate or pelvis (including the prostate) or ablation or chemical treatment of the prostate for treating cancer: these types of treatment affect the anatomy of prostate tissue microstructure for which Galen Prostate AI is not currently validated. NB: any treatment for benign enlargement of the prostate is permitted.

Outcomes:
Galen Prostate AI; Composite Health Outcome (Cost-Consequence Analysis); Composite Health Outcome (Cost-Utility Analysis)
Usability User Testing of the Easy-EyeFM AI Platform
Trial ID: NCT06834906
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-26
Completion date: 2025-09
Sponsor: Tsinghua University
Collaborators: First Hospital of Tsinghua University; Beijing Tsinghua Changgeng Hospital; Augenarzt-Praxisgemeinschaft Gutblick AG; Sankara Nethralaya; Department of Medical Services Ministry of Public Health of Thailand; Moorfields Eye Hospital NHS Foundation Trust; Pomeranian Hospitals; Shanghai Health and Medical Center; Hong Kong Eye Hospital; Royal Victoria Eye and Ear Hospital; Hanyang University Guri Hospital; Alshifa Trust Eye Hospital; Korle-Bu Teaching Hospital, Accra, Ghana; Nippon Medical School Tama Nagayama Hospital
Conditions: Ocular Diseases
Interventions: EasyEyeFM-AutoML-AI-platform test; AutoML-EasyEyeFM-AI-platform test
Locations: Tsinghua University
Countries: China
Enrollment: 84
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Easy-EyeFM is a code-free artificial intelligence platform designed for ophthalmologists to provide diagnostic and treatment recommendations and help doctors develop customized diagnostic models.

This project aims to evaluate the usability and user-friendliness of the Easy-EyeFM platform for physicians. The study will gather feedback from medical professionals to evaluate the intuitiveness and effectiveness of the platform in supporting model customization and medical image analysis tasks.

Subjects will participate in the trial of the Easy-EyeFM platform and the comparison with the existing commercial platform (AutoML) at the appointed time. The researchers will inform subjects of relevant precautions and assist them to read the user manual before the test. Subjects will complete model customization and picture diagnosis within the prescribed test time, and according to the test results, Score the human-computer interaction experience of the AI platform, fill in the questionnaire, and finally complete the evaluation content.

Eligibility criteria:
Inclusion Criteria:

* Medical students or doctors
* Have enough time to participate in this program, about 1 hour continuous assessment
* Agree to sign the informed consent form

Exclusion Criteria:

* Poor computer and mobile phone use and reading ability
* Poor language expression or dialect category is not included in the speech recognition function of the platform

Outcomes:
System Usability Scale
Evaluation of a Sarcopenia Prediction Model and Development and Effectiveness of a Nursing Guidance Mobile App
Trial ID: NCT05363033
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-07-01
Completion date: 2022-12
Sponsor: National Taipei University of Nursing and Health Sciences
Conditions: Prediction of High-risk Populations for Sarcopenia
Interventions: sarcopenia nursing guidance mobile application was developed
Locations: Cheng Hsin General Hospital
Countries: Taiwan
Enrollment: 120
Age range: 20 Years - 80 Years
Sex: ALL

Summary:
We evaluated a model for the prediction of high-risk populations for sarcopenia in home settings, developed a sarcopenia nursing guidance mobile application, and assessed the effectiveness of this application in influencing sarcopenia-related knowledge and self-care behavior among participants.

Eligibility criteria:
Inclusion criteria were being a member of the public, aged ≥20 years and \<80 years, and having experience using mobile devices or smartphones. Exclusion criteria were not belonging to a high-risk population for sarcopenia and/or being unable to use the application platform.

Outcomes:
changes in sarcopenia-related knowledge and awareness regarding self-care
Percentage Visualized Mucosa as a Marker for the Quality of Colonoscopy
Trial ID: NCT04482452
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-06-08
Completion date: 2022-05-19
Sponsor: Insel Gruppe AG, University Hospital Bern
Conditions: Colonoscopy
Interventions: Colonoscopy
Locations: Inselspital
Countries: Switzerland
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
Primary hypothesis: The percentage of mucosa visualized during the withdrawal of a colonoscopy with reference to the position in the colon by means of the scope-guide is an appropriate parameter for assessment of the quality of colonoscopy and correlates with the probability of the detection of adenomas.

Secondary hypothesis:

The investigators hypothesize that the percentage of visualized mucosa differs according patient specific parameters (e.g. gender, age, BMI, bowel preparation), procedure specific parameters (e.g. patient position, device, buscopan, fentanyl, withdrawal time) and investigator specific parameters (professional experience as resident and consultant).

Study Procedure/Evaluation:

The endoscopic images and scope guide from colonoscopy of 500 patients will be filmed. The investigators will correlate the detection of at least one adenoma in the cohort of included patients with the percentage of visualized mucosa during the withdrawal of a colonoscopy. Additional endpoints concerning the quality of the colonoscopy will be assessed as well.

Eligibility criteria:
Inclusion Criteria:

* Signed informed consent.
* Ability to understand and follow study procedures and understand informed consent.
* Complete colonoscopy.
* Age at least 18 years.

Exclusion Criteria:

\- Participation in another clinical study interfering with study procedures.

Outcomes:
Correlation of detection of at least one adenoma with the percentage of visualized mucosa
Influence of Wolters Classification of Pulpitis and the Radiographic Caries Depth Detected Using Artificial Intelligence on the Success of Full Pulpotomy in Permanent Mature Molars With Irreversible Pulpitis: A Randomized Controlled Trial
Trial ID: NCT06597487
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-15
Completion date: 2025-07-01
Sponsor: Misr International University
Conditions: Pulpitis - Irreversible
Interventions: Full pulpotomy; Conventional root canal treatment
Locations: Misr International University
Countries: Egypt
Enrollment: 58
Age range: 20 Years - 45 Years
Sex: ALL

Summary:
This study aims to evaluate the effect of prognostic factors, such as the degree of pulpal inflammation and the radiographic caries depth, on the success of full pulpotomy in mature permanent molars with irreversible pulpitis.

Eligibility criteria:
Inclusion Criteria:

* Mature permanent molars with irreversible pulpitis.
* Presence of bleeding pulp tissues from all canals.
* The tooth is not periodontally compromised.

Exclusion Criteria:

* Non-vital teeth.
* Uncontrolled bleeding from any of the canals after ten minutes of application of hemostatic agent.
* Molars with immature roots.

Outcomes:
Percentage of Successful Cases; Diagnostic accuracy (Sensitivity and Specificity) of an artificial intelligence software in detecting carious pulp exposure
The Accuracy and Reliability of Orthodontic Cephalometry Analysis Using Web-Based Artificial Intelligence Program
Trial ID: NCT07246018
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-01-02
Completion date: 2023-06-30
Sponsor: International Islamic University Malaysia
Conditions: Cephalometry; Orthodontic; Cephalometric Analysis
Interventions: Manual Cephalometric Tracing; AI-Based Cephalometric Analysis (WeDoCeph Software)
Locations: Orthodontic Specialist Clinic, Kulliyyah of Dentistry
Countries: Malaysia
Enrollment: 40
Age range: None - None
Sex: ALL

Summary:
This study compares the accuracy and reliability of artificial intelligence (AI) software for analyzing dental X-rays to the traditional manual tracing method used by dentists.

Lateral cephalometric radiographs are special X-rays of the head used in orthodontics (teeth straightening) to measure jawbone positions, tooth angles, and facial proportions. Traditionally, orthodontists manually trace these X-rays using pencil and paper to identify key landmarks and make measurements. This manual method is time-consuming and can vary between different practitioners or even when the same practitioner measures twice.

AI-based software can automatically identify these landmarks and perform measurements instantly. This study examined 40 dental X-rays to determine if the AI software (WeDoCeph) is as accurate and more reliable than manual tracing.

Each X-ray was measured twice - once manually by a trained examiner and once by AI software - at two different times (4 weeks apart). The researchers compared 15 different measurements, including 8 angles and 7 distances, to assess accuracy and reliability.

Eligibility criteria:
Inclusion Criteria:

* Pretreatment/post-treatment lateral cephalometric radiographs
* High-quality cephalograms with visible anatomical landmarks

Exclusion Criteria:

* Patients with surgical rigid fixations, orthodontic appliances and dental prostheses visible on radiographs
* Very poor quality/diagnostically unacceptable radiographs
* Patients with syndromes or with craniofacial deformities

Outcomes:
Intraclass Correlation Coefficient (ICC) for repeated manual measurements; Intraclass Correlation Coefficient (ICC) for repeated AI measurements; Mean differences between manual and AI-based measurements at T₀; Mean differences between manual and AI-based measurements at T₁
A Randomized Controlled Multicenter Study of Artificial Intelligence Assisted Digestive Endoscopy
Trial ID: NCT04071678
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-08-01
Completion date: 2021-12-30
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Conditions: Artificial Intelligence
Interventions: Careful examination during endoscopic procedures to identify lesions
Locations: Cai J Ting
Countries: China
Enrollment: 3600
Age range: 18 Years - None
Sex: ALL

Summary:
Digestive endoscopy center of the second affiliated hospital of medical college of zhejiang university and engineers of naki medical co., ltd. in Hong Kong independently developed an ai-assisted diagnostic model of digestive endoscopy in the early stage, namely the deep learning model.The deep learning model through the early stage of the study, is able to identify lesions of digest tract.The sensitivity for the diagnosis of some diseases, such as colon polyps, is 99%. On the one hand, this auxiliary diagnostic model can guide endoscopic examination for beginners; on the other hand, it can improve the detection rate of lesions and reduce the rate of missed diagnosis; on the other hand, the overall operating efficiency of the endoscopic center is improved, which is conducive to the quality control of endoscopic examination. Now the AI-assisted diagnostic model has been further improved, and it is planned to carry out further clinical verification in the digestive endoscopy center of our hospital. It is connected to the endoscopic system of our hospital and used simultaneously with the existing image-text system of endoscopy to compare the practicability, sensitivity and specificity of AI-assisted diagnosis model in the diagnosis of digestive tract diseases, and focus on the quality control of endoscopic examination.

Eligibility criteria:
Inclusion Criteria:

* Voluntarily sign the informed consent for this study
* Stable vital signs
* Over 18 years old
* Patients requiring painless gastroenteroscopy for various reasons

Exclusion Criteria:

* Unable or unwilling to sign a consent form, or unable to follow research procedures
* have contraindications to painless gastroenteroscopy
* Vital signs are unstable
* The lesions have been identified by gastroenteroscopy in other hospitals, which is to further confirm the patients who come to our hospital for endoscopic examination
* Endoscopic treatment, such as polypectomy, pylorus narrow dilatation and so on

Outcomes:
Changes of detection rate of digestive tract lesions assisted by artificial intelligence gastroenteroscopy; The accuracy of AI-assisted diagnostic model evaluating the intestinal readiness score
Heart Failure Monitoring With a Portable Ultrasound Device With Artificial Intelligence Assisted Tools: A Multi-Phase Observational Feasibility Study
Trial ID: NCT05594940
Status: TERMINATED
Study type: OBSERVATIONAL
Start date: 2022-09-21
Completion date: 2024-02-23
Sponsor: Butterfly Network
Conditions: Congestive Heart Failure
Locations: John Muir Health Hospital, Concord Medical Center; John Muir Health Hospital, Walnut Creet Medical Center
Countries: United States; United States
Enrollment: 22
Age range: 18 Years - None
Sex: ALL

Summary:
The primary objective of this study is to evaluate the feasibility of a novel app to facilitate heart failure patients to acquire lung ultrasound images and evaluate the quality of those images to expert scans, and to evaluate for relationships between the ultrasound data and pulmonary pressure data from the Abbott CardioMEMS system.

Eligibility criteria:
Inclusion Criteria:

1. Documented diagnosis of heart failure at least three months prior to the date of consent
2. HF currently managed with the Abbott CardioMEMS device
3. Able to read and write in English.
4. At least 18 years of age or older on the date of consent.
5. Willing and able to provide written consent to participation
6. Expressed willingness to capture AI assisted US recordings immediately before or after taking their CardioMEMS measurements
7. Access to WiFi or cellular data connection at home
8. In the opinion of the investigator or delegate the subject, must be capable of complying with study procedures.

Exclusion Criteria:

1. Previous experience using an ultrasound device on themselves or others
2. Women who are pregnant or plan to become pregnant during the study

Outcomes:
Patients Image Quality; Novice Image Quality; B-Line count CardioMEMS comparison
Cardiovascular Disease in Patients With Chronic Kidney Disease: Polish Kidney- Heart Project
Trial ID: NCT06083168
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-12-01
Completion date: 2036-01-31
Sponsor: Medical University of Silesia
Collaborators: University of Warmia and Mazury in Olsztyn; University of Liverpool
Conditions: Chronic Kidney Disease; Cardiovascular Diseases
Locations: Wojewódzki Szpital Specjalistyczny w Olsztynie; Samodzielny Publiczny Szpital Kliniczny Nr 1 im. prof. Stanisława Szyszko Śląskiego Uniwersytetu Medycznego w Katowicach
Countries: Poland; Poland
Enrollment: 6000
Age range: 18 Years - None
Sex: ALL

Summary:
This is an observational longitudinal study which will collect routine demographic, laboratory and clinical parameters of patients with chronic kidney disease (CKD) in the Silesian and Warmia and Mazury Regions (Poland) aimed at predicting incident cardiovascular disease and cardiovascular and renal events using machine learning and artificial intelligence approaches. There will be a subgroup analysis of patients with diabetes and CKD.

Eligibility criteria:
Inclusion Criteria:

* CKD defined as estimated glomerular filtration rate (eGFR) \< 60ml/min/1.73 m2 and/or urine albumin creatinine ratio ( UACR) \> 30 mg/g lasting at least 3 months
* CKD regardless of eGFR/albuminuria when documented otherwise (by means of imaging, renal biopsy result, genetic background, etc)

Exclusion Criteria:

* Death during hospital stay
* Terminal stage of cancer
* Lack of an informed consent

Outcomes:
Incident Coronary Artery Disease; Myocardial Infarction; New onset heart failure or exacerbation of heart failure; Stroke or Transient Ischemic Attack; Peripheral Vascular Disease Peripheral Vascular Disease Peripheral Vascular Disease Peripheral Vascular Disease Peripheral Vascular Disease; Atrial Fibrillation; Death Due to Cardiovascular Cause; Progression of Chronic Kidney Disease; End-Stage Renal Disease or Renal Death
An Artificial Intelligence Based Approach for Selecting the Optimal Day for Triggering.
Trial ID: NCT07515118
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-08
Completion date: 2027-09
Sponsor: Fundacion Dexeus
Conditions: Infertility; Ovarian Stimulation; Artificial Intelligence
Interventions: STIMAI®.; Routine clinical management
Locations: Dexeus Mujer Sabadell; Dexeus Mujer Sant Cugat; Dexeus Mujer Reus; Hospital Universitario Quiron Dexeus; Dexeus Mujer Tarragona
Countries: Spain; Spain; Spain; Spain; Spain
Enrollment: 644
Age range: 18 Years - 42 Years
Sex: FEMALE

Summary:
To evaluate, in a randomized controlled trial, whether AI-guided monitoring and ovulation triggering leads to clinical outcomes comparable to those achieved through physician-led decision-making in patients undergoing ovarian stimulation for IVF.

Eligibility criteria:
Inclusion Criteria:

* Undergoing COS for IVF with autologous oocytes, oocyte donation and elective fertility preservation with all monitoring USS (ultrasound scan) conducted at our centers.

Exclusion Criteria:

* Medically indicated fertility preservation
* Inability to attend clinic visits for monitoring.

Outcomes:
MII oocytes
Qualitative Research Among Physicians and Junior Doctors Into the Preconditions for Implementing a Clinical Decision Support System (CDSS) Based on Artificial Intelligence (AI) in the ICU
Trial ID: NCT05303025
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-04-13
Completion date: 2022-10-31
Sponsor: University Ghent
Collaborators: Research Foundation Flanders
Conditions: Artificial Intelligence; Qualitative Research; Decision Support Systems, Clinical
Interventions: Survey; Semi-structured group discussion
Locations: OLV Aalst; ZNA Ziekenhuizen; Ghent University Hospital
Countries: Belgium; Belgium; Belgium
Enrollment: 69
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this study is to explore the different attitudes and preconditions of potential end-users (doctors \& physicians in training) required to achieve successful clinical implementation of models based on artificial intelligence (i.e. both machine learning and knowledge-driven techniques) as clinical decision support software.

Eligibility criteria:
Inclusion Criteria:

* Medical specialist or specialist in training working in intensive care at the time of the study.

Exclusion Criteria:

* Age \< 18 yo

Outcomes:
Baseline attitudes towards artificial intelligence and big data in medicine; Identify subdomains of the antimicrobial stewardship cycle with potential for AI/Big data application; Identify perceived potential benefits and harms when applying AI in the antimicrobial stewardship cycle.; Identify prerequisites that need to be fulfilled when AI/Big data based clinical decision support systems are used bedside from the viewpoint of the participants.