moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Enhancing Embryo Selection With AiVF: A Major Advancement in ART - A Retrospective Study
Trial ID: NCT07383480
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2025-04-30
Sponsor: University Hospital, Clermont-Ferrand
Conditions: Reproductive Techniques, Assisted; Artificial Intelligence; Embryo Transfer
Locations: CHU de Clermont-Ferrand
Countries: France
Enrollment: 333
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
In assisted reproductive technology (ART), selecting the most viable embryos from a large number of fertilized eggs is crucial. While techniques like morphological assessment, time-lapse monitoring systems, and pre-implantation genetic testing have improved the process, implantation success rates remain limited. The introduction of artificial intelligence in embryo evaluation, such as automated systems like EMA by AIVFTM, provides a promising alternative to enhance the accuracy and effectiveness of embryo selection. This study aims to assess the performance of the EMA system compared to traditional methods by examining its ability to rank embryos based on their potential for successful implantation, with the goal of increasing the chances of a successful pregnancy. This is the first clinical evaluation of this platform in France, offering new opportunities to improve decision-making in in-vitro fertilization.

Eligibility criteria:
Inclusion Criteria:

* IVF or ICSI treatment between January 2022 and December 2023
* Fresh or frozen sperm from the partner or a donor
* Embryo culture up to the 105th hour
* Fresh or frozen embryo transfer on Day 5
* Known implantation data (clinical pregnancy assessed by the presence of cardiac activity at 6 weeks via ultrasound)

Exclusion Criteria:

* Epididymal or testicular sperm
* Embryo culture with transfer on Day 2 or Day 3
* ICSI with oocyte donation or the use of thawed oocytes
* Transfer of two embryos resulting in a singleton pregnancy
* No blastocyst obtained on Day 5

Outcomes:
Comparison of artificial intelligence (AI) Score and Laboratory Embryo Classification
A Pilot Study: Retrospective Evaluation of 3 Colonic Adenoma Detection Strategies During a Colonoscopy: Endoscopy Cap Associated With the Artificial Intelligence GI GENIUS ™ System, the Artificial Intelligence GI GENIUS ™ Alone and Colonoscopy Alone
Trial ID: NCT05080088
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-04-06
Completion date: 2021-07-08
Sponsor: Centre Hospitalier Universitaire de Nīmes
Conditions: Adenoma Colon; Colorectal Cancer; Colorectal Polyp; Colonic Adenocarcinoma
Locations: CHU Nîmes
Countries: France
Enrollment: 90
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
Retrospective study, single blind (patient), allowing a posteriori clinical data collection of 90 patients during their passage to the ambulatory endoscopy circuit, to consider 3 groups and thus to deduce a colonic adenoma detection rate for each arm :

* Colonoscopy Only Group
* Artificial intelligence only group (IA GI GENIUS ™ alone)
* Endoscopic Cap and Artificial Intelligence Group (endoscopy cap associated with the GI GENIUS ™ IA System)

Eligibility criteria:
Inclusion Criteria :

* 18 years \< Patient \< 85 years
* Need to perform a primary and / or secondary screening colonoscopy for colorectal cancer
* Patients candidates for outpatient care

Exclusion Criteria :

* History of inflammatory digestive tract disease
* Failed previous colonoscopy
* Known familial polyposis
* Biopsy / polypectomy contraindication (coagulation disorder, treatment with CLOPIDOGREL / Anticoagulant)

Outcomes:
Colonic Adenomas Detection Rate according to the use of the Combine Techniques GENIUS software - Cap or Only GENIUS software
Application of BaiXiaoAi Companion AI in the Diagnosis, Treatment, and Follow-up Management of Oncology Patients: A Single-Center, Prospective, Exploratory Study
Trial ID: NCT07396142
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-14
Completion date: 2027-11-30
Sponsor: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Conditions: Symptoms and Signs; Artificial Intelligence; Artificial Intelligence Mobile Application; Needs Assessment
Interventions: No Intervention: Observational Cohort
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
This is a prospective, single-center, exploratory study designed to evaluate the accuracy, user engagement, and user experience of the BaiXiaoAi Companion AI. Upon signing the informed consent form and enrollment, a dedicated "Doctor-Nurse-Patient-AI" WeChat group will be established for each participant. Within the group, the BaiXiaoAi AI will provide timely responses based on patient communications and proactively push information regarding disease management and patient education.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18 years or older;
* Patients with a confirmed diagnosis of malignant neoplasms who are currently undergoing treatment or in follow-up, or their primary caregivers;
* Able to read and communicate in Chinese and independently use WeChat;
* Willing and able to provide informed consent.

Exclusion Criteria:

* Presence of severe psychiatric disorders (e.g., schizophrenia, bipolar disorder) or cognitive impairment (MMSE score \< 24);
* Inability to use WeChat or communicate in Chinese;
* Considered by the investigators to be unsuitable for participation due to psychological or physical conditions.

Outcomes:
Accuracy and safety of AI-assisted patient communication
Usefulness of Deep-Learning Image Reconstruction for Cardiac Computed Tomography Angiography - a Prospective, Non-randomized Observational Trial
Trial ID: NCT03980470
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-05-08
Completion date: 2019-06-20
Sponsor: University of Zurich
Conditions: Coronary Artery Disease
Interventions: TrueFidelity
Locations: University Hospital
Countries: Switzerland
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
Cardiac CT allows the assessment of the heart and of the coronary arteries by use of ionising radiation. Although radiation exposure was significantly reduced in recent years, further decrease in radiation exposure is limited by increased image noise and deterioration in image quality. Recent evidence suggests that further technological refinements with artificial intelligence allows improved post-processing of images with reduction of image noise.

The present study aims at assessing the potential of a deep-learning image reconstruction algorithm in a clinical setting. Specifically, after a standard clinical scan, patients are scanned with lower radiation exposure and reconstructed with the DLIR algorithm. This interventional scan is then compared to the standard clinical scan.

Eligibility criteria:
Inclusion Criteria:

* Patients referred for cardiac CT angiography
* Age ≥ 18 years
* Written informed consent

Exclusion Criteria:

* Pregnancy or breast-feeding
* Enrollment of the investigator, his/her family members, employees and other dependent persons
* Renal insufficiency (GFR below 35 mL/min/1.73 m²)

Outcomes:
Subjective Image Quality
Detecting Asymmetry With Artificial Intelligence: Consistency Evaluation of the Looksmaxxing Application in Photo-Based Facial Analysis
Trial ID: NCT07197359
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-01
Completion date: 2025-10-15
Sponsor: Konya Necmettin Erbakan Üniversitesi
Conditions: Mandibular Asymmetry
Interventions: Looksmaxxing AI Facial Analysis
Locations: Necmettin Erbakan University
Countries: Turkey (Türkiye)
Enrollment: 90
Age range: 10 Years - 40 Years
Sex: ALL

Summary:
This study aims to evaluate whether an artificial intelligence application called Looksmaxxing AI will be able to correctly identify chin deviation (chin asymmetry) from standard frontal facial photographs. A total of 540 photographs will be included in the study. The eye areas will be covered to protect identity. Each photo will be analyzed by the AI, and its answers will be compared with clinical reality. The accuracy of two versions of the software (Looksmaxxing 4o and 5) will be assessed. The results may help show whether simple photo-based analysis can support early detection of chin asymmetry, especially in areas with limited access to orthodontic examination.

Eligibility criteria:
Inclusion Criteria:

* Absence of any craniofacial anomaly

No major wound or scar in the facial or neck region

No history of previous orthodontic treatment

For male participants: absence of a long beard that could affect the appearance of the chin

Availability of standardized frontal facial photographs taken in natural head position

Exclusion Criteria:

* Photographs in which the patient's face appears slightly angled or turned sideways

Blurred or low-quality photographs with reduced clarity

Outcomes:
Accuracy of Looksmaxxing AI in Detecting Chin Deviation
Age and Sex-Specific PREValence of AcqUirEd VALVular Heart DiseasE (PREVUE-VALVE)
Trial ID: NCT05357404
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-04-21
Completion date: 2025-04
Sponsor: Cardiovascular Research Foundation, New York
Collaborators: CVS Caremark; Hawthorne Effect Inc.; Columbia University; Vanderbilt University Medical Center; University of Michigan; Walgreens
Conditions: Valvular Heart Disease
Locations: Hawthorne Effect
Countries: United States
Enrollment: 2870
Age range: 65 Years - 85 Years
Sex: ALL

Summary:
The PREVUE-VALVE study will establish reliable, population-based estimates of Valvular Heart Disease (VHD) prevalence among older Americans and allow for the development and validation of several innovative tools to aid in the detection and diagnosis of Valvular Heart Disease (VHD).

Eligibility criteria:
Inclusion Criteria:

* Age 65-85
* Ability to provide informed consent
* Stably domiciled in a residence that can be accessed by study personnel

Exclusion Criteria:

* History of complex congenital heart disease

Outcomes:
Prevalence of Valvular Heart Disease
Prospective, Randomized, Multicenter, Tandem Study Evaluating the Safety and Effectiveness of the CB-17-08 Augmented Endoscopy System for the Detection of Mucosal Colorectal Polyps in Adult Patients Undergoing Screening or Surveillance Colonoscopy for CRC.
Trial ID: NCT03954548
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-02-17
Completion date: 2021-05-07
Sponsor: Cosmo Artificial Intelligence-AI Ltd
Conditions: Colorectal Cancer
Interventions: CB-17-08 CADe
Locations: Mayo Clinic Scottsdale; Geisinger Medical Center; Mayo Clinic Jacksonville; Kansas City VA Medical Center; Ascension St. John's Hospital; Mayo Clinic Eau Claire; Mayo Clinic La Crosse; ASL Roma 1 (Presidio Nuova regina Margherita); Queen Alexandra Hospital; Oxford University Hospitals
Countries: United States; United States; United States; United States; United States; United States; United States; Italy; United Kingdom; United Kingdom
Enrollment: 249
Age range: 45 Years - None
Sex: ALL

Summary:
The aim of this study is to assess the performance of the CB-17-08 to help endoscopists find potential mucosal polyps during the colonoscopy procedure, without significant noise disturbing the endoscopist attention, nor negative interference with the lesions detection than with the standard colonoscopy alone: the study will investigate whether the use of the device provides an increase in the number of adenomas per colonoscopy as compared to standard colonoscopy. The study will also evaluate the safety of the CB-17-08, assessing if the use of the system increases the total number of excisions without a commensurate number of adenomas as compared to standard colonoscopy.

Eligibility criteria:
Inclusion Criteria:

* Male and female patients age: ≥45 years;
* Patients presenting to the endoscopy unit for a screening colonoscopy or a surveillance colonoscopy for CRC;
* Willingness to undergo tandem colonoscopies with and without the use of CB-17-08 on the same day and in the same procedural setting;
* Ability to provide written, informed consent and understand the responsibilities of trial participation;

Exclusion Criteria:

* The subject is pregnant or is planning a pregnancy during the study period;
* History of inflammatory bowel disease (IBD);
* History of colon resection;
* History of Familial adenomatous polyposis (FAP) syndrome or of Serrated Polyposis Syndrome (SPS);
* History of overt lower GI bleeding;
* History of colonic stricture;
* History of radiation therapy to the abdomen or pelvis;
* Patients with contraindications to colonoscopy such as presence of acute diverticulitis or toxic megacolon;
* Subjects with particular symptoms (e.g diarrhea) who, per clinical practice, have to undergo random biopsies in the colon.

Fecal Occult Blood Test (FOBT) or Fecal Immunochemical Test (FIT) positive patients will not be excluded from the study.

Outcomes:
Adenoma miss rate [AMR]
Artificial Intelligence for Highly Myopic Cataract
Trial ID: NCT06616506
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2016-01-01
Completion date: 2035-12-31
Sponsor: Shanghai High Myopia Study Group
Collaborators: Eye and ENT Hospital of Fudan University
Conditions: Cataract; High Myopia; Myopia; Automatic Judgement; Risk Reduction; Diagnoses Disease
Interventions: newly-invented AI model
Locations: Eye and ENT Hospital of Fudan University
Countries: China
Enrollment: 500000
Age range: None - None
Sex: ALL

Summary:
This study aims to establish a multi-center artificial intelligence model for the accurate calculation of intraocular lens and its clinical application in highly myopic cataract, so as to improve the accuracy of the prediction of postoperative diopter in highly myopic cataract patients.

Eligibility criteria:
Inclusion Criteria:

Axial length ≥ 26 mm and patients with high myopia complicated by cataracts who visit our hospital.

Preoperative anterior segment photography and cataract severity assessment, along with fundus OCT examination, have been performed.

If surgical treatment is conducted, the postoperative follow-up period should be no less than 1 month.

There are no missing clinical data before and after the surgery.

Exclusion Criteria:

Patients who are unable to cooperate with the required examinations for this study.

Outcomes:
a multi-center artificial intelligence model for the accurate calculation of intraocular lens and its clinical application in highly myopic cataract; a multi-center artificial intelligence model for the cataract grading and its clinical application in highly myopic cataract
The Establishment of Tongue Examination Analysis From the Video Recording
Trial ID: NCT04923763
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-10-17
Completion date: 2025-12-31
Sponsor: China Medical University, Taiwan
Conditions: Medicine, Chinese Traditional
Locations: China Medical University
Countries: Taiwan
Enrollment: 300
Age range: 20 Years - None
Sex: ALL

Summary:
This project intends to record the process of tongue extension through a dynamic video, and establish the tongue examination analysis from the video recording.

In this study, 300 general public will be invited for examinations, and high-quality tongue examination videos will be recorded by digital cameras. After the tongue examination video is properly calibrated, the computer analyzes the tongue examination parameters of each frame and the dynamic parameters, thereby establishing the automatic tongue video analysis system

Eligibility criteria:
Inclusion Criteria:

* general public

Exclusion Criteria:

* unable to complete tongue examination

Outcomes:
Tongue video analysis
Validation of Artificial Intelligence-Driven Cephalometric Analysis as a Reliable Tool for Orthodontic Diagnosis and Treatment Planning
Trial ID: NCT07315152
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05
Completion date: 2028-06
Sponsor: Al-Azhar University
Conditions: Malocclusion
Interventions: Artificial Intelligence-Driven Cephalometric Analysis
Locations: Faculty of Dentistry, Al-Azhar University
Countries: Egypt
Enrollment: 55
Age range: 12 Years - 30 Years
Sex: ALL

Summary:
This study is designed to evaluate whether artificial intelligence can analyze cephalometric images in orthodontics as a reliable tool for diagnosis and treatment planning. The study will include orthodontic patients who need cephalometric evaluation. Participants will have their X-ray images analyzed using both the AI system and traditional manual methods. The study will compare the results to see how closely the AI measurements match the standard measurements. This information may help patients, families, and health care providers understand how AI can support orthodontic diagnosis and treatment planning.

Eligibility criteria:
Inclusion Criteria:

* No systemic disease.
* Not receiving medical treatment that could interfere with bone metabolism.
* Good level of oral hygiene.
* No periodontal disease or radiographic evidence of bone loss.

Exclusion Criteria:

* Periodontally compromised patients.
* Presence of systemic diseases.
* Drug dependencies.
* Uncooperative patients.

Outcomes:
Accuracy of AI-driven cephalometric analysis
TAS Test: Determining the Feasibility and Validity of Online Motor-cognitive Testing for Early Detection of Alzheimer's Disease
Trial ID: NCT05194787
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-03-05
Completion date: 2030-12
Sponsor: University of Tasmania
Collaborators: National Health and Medical Research Council, Australia; University of Sydney; University of Leeds
Conditions: Alzheimer Disease; Dementia; Age-related Cognitive Decline
Interventions: Cognitive test scores, clinical diagnosis and blood biomarkers
Locations: University of Tasmania
Countries: Australia
Enrollment: 3000
Age range: 50 Years - None
Sex: ALL

Summary:
Global dementia prevalence is rising. Alzheimer's disease (AD), the most common cause, has devastating effects on people's quality of life. AD has a preclinical (pre-AD) period of 10-20 years when brain pathology silently progresses before any cognitive symptoms appear. Current tests for pre-AD are invasive, costly and unsuitable for screening at population level. Similar to screening for pre-diabetes and carcinoma in situ, it is important to detect AD at the preclinical stage in order to offer early interventions before the pathology progresses to the irrerversible degenerative stage. In the study, research will develop a new scalable test (TAS Test) by combining two innovative ideas: hand-movement tests to detect pre-AD \>10 years before cognitive symptoms begin; and computer vision so people can "self-test" online using home computers. This unique approach builds on recent discoveries that hand-movement patterns change in pre-AD. The research team will use exquisitely precise computer vision methods to automatically analyse movement data from thousands of participants, and combine this with machine learning of overall motor-cognitive performance. The project team has access to 3 well-phenotyped cohorts, \>10,000 existing participants and a cutting-edge assay for a blood AD biomarker, ptau181. The research team will develop a TAS Test algorithm to classify hand-movement and cognitive test data for pre-AD risk (p-taua181 levels) and determine TAS Test's precision to prospectively predict 5-year risks of cognitive decline and AD.

Eligibility criteria:
Inclusion Criteria: \> 50years old

Exclusion Criteria: established diagnosis of dementia

Outcomes:
Develop and validate the optimal TAS Test protocol to detect pre-AD (p-tau 181 positivity); Prospectively validate TAS Test to predict risks of cognitive decline; Prospectively validate TAS Test to predict risks of AD diagnosis
Predicting Patient-level New Onset Atrial Fibrillation From Population-based Nationwide Electronic Health Records: A Precision Medicine Investigation Using Artificial Intelligence
Trial ID: NCT04657900
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-11-02
Completion date: 2023-10-31
Sponsor: University of Leeds
Collaborators: British Heart Foundation
Conditions: Atrial Fibrillation
Interventions: Observational
Locations: University of Leeds
Countries: United Kingdom
Enrollment: 140000
Age range: 18 Years - None
Sex: ALL

Summary:
Atrial fibrillation (AF) is a major cardiovascular health problem: it is common, chronic and incurs substantial health-care expenditure as a result of stroke, sudden death, heart failure and unplanned hospitalisation. There is a compelling argument for the early diagnosis of AF, before the first complication occurs, but population-based screening is not recommended. Strategies to identify individuals at higher risk of new onset AF are required. previous risk scores have been limited by data and methodology. The investigators will use routinely collected hospital-linked primary care data and focus on the use of artificial intelligence methods to develop and validate a model for the prediction of incident AF. Specifically, the investigators will investigate how population-based data may be used for precision medicine using a deep neural networks learning model. Using clinical factors readily accessible in primary care, the investigators will provide a method for the identification of individuals in the community who are at risk of AF, as well as when incident AF will occur in those at risk, thus accelerating research assessing technologies for the improvement of risk prediction, and the targeting of high-risk individuals for preventive measures and screening.

Eligibility criteria:
Inclusion Criteria:

* Diagnosed AF after 1 January 2009 (Identified using Read codes (for the CPRD patient profile) and ICD-10 codes (for HES events)
* In Clinical Practice Research Datalink -Global initiative for chronic Obstructive Lung Disease (CPRD-GOLD) and eligible for data linkage.
* Have at least 1-year follow-up in the period between 1st Jan 1998 and 31st December 2018.

Exclusion Criteria:

* Under 18 at date of the first registration in CPRD
* Diagnosed with AF before 1st Jan 1998
* In CPRD-GOLD and not eligible for data linkage
* Has less than one year follow up in CPRD

Outcomes:
To develop and validate a deep learning hierarchical model for predicting the risk, and where appropriate period, of new onset AF; To identify and quantify the magnitude of predictors of new onset AF