moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Evaluating The Efficacy of Pathways Parent-Mediated Intervention's Mutual Gaze Protocol on Social Skills in Young Children Suspected of Having Autism: A Randomized Control Trial
Trial ID: NCT06596226
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-09-10
Completion date: 2026-06-01
Sponsor: The University of Texas at Dallas
Collaborators: Texas Higher Education Coordinating Board
Conditions: Autism
Interventions: Pathways Parent Mediated Intervention; Pathways Parent Mediated Intervention without Mutual Gaze
Locations: University of Texas at Dallas
Countries: United States
Enrollment: 80
Age range: 16 Months - 30 Months
Sex: ALL

Summary:
Purpose of the Study: The goal of this clinical trial is to find out if a technique called the "mutual gaze procedure," used in Pathways Early Intervention (Pathways), is the key to helping improve social communication, language, and everyday skills in young children (16-30 months old) who are at high risk for autism, particularly those from diverse cultural and language backgrounds.

What Will Happen: Researchers will compare two versions of the Pathways Intervention:

* Version 1: Includes mutual gaze strategies.
* Version 2: Does not include mutual gaze strategies.

What to Expect: Participants will:

* Attend 12 sessions of Pathways Intervention, each lasting 1.5 hours (or 15 weeks if there are cancellations).
* Come to the clinic for a developmental check-up three times: before starting Pathways, right after completing Pathways, and three months after finishing Pathways.

Eligibility criteria:
Inclusion Criteria:

* Children must be between 16-30 months old at the start of the study;
* Children must receive social disability index of 7 or lower on the EarliPointTM assessment with a researcher (i.e., human) confirming social challenges are present;
* Parents must report no other known neurological or genetic concerns or disorders
* Parents must be fluent in English
* Parents must live within a 30-mile radius of the Callier Center Dallas.

Exclusion Criteria:

* Children younger than 16 months or older than 30 months at the start of the study
* Children who are not at high risk for autism based on an EarliPointTM assessment (with researcher confirmation of social challenges)
* Children whose parents report they have any other known neurological or genetic concerns or disorders;
* Children whose parents are not fluent in English.

Outcomes:
Improved Social skills; Improved Social Communication Skills; Change in Adaptive Functioning
Effect of Static vs. Conversational AI-Generated Messages on Colorectal Cancer Screening Intent: a Randomized Controlled Trial
Trial ID: NCT07107152
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-11
Completion date: 2025-06-21
Sponsor: University of Pennsylvania
Conditions: Colorectal Cancer Control and Prevention
Interventions: single GPT generated message; AI Chatbot; Expert-Written Patient Materials
Locations: Participants are recruited online via Prolific
Countries: United States
Enrollment: 915
Age range: 45 Years - 75 Years
Sex: ALL

Summary:
The goal of this clinical trial is to assess whether messages generated by a large language model (LLM), including both static and conversational formats, can increase colorectal cancer (CRC) screening intentions among U.S. adults aged 45-75 who have never completed CRC screening. The main questions it aims to answer are:

Do personalized, AI-generated messages increase the self-reported likelihood of completing a stool-based CRC screening test within 12 months?

Do they also increase intent to undergo colonoscopy screening within 12 months?

Researchers will compare four groups: (1) no message control, (2) expert-written patient education materials, (3) a single AI-generated persuasive message, and (4) a motivational interviewing-style AI chatbot. These comparisons will help assess whether a conversational format offers added benefit over static AI or expert-generated content.

Participants will:

Be randomly assigned to one of the four study arms

Spend at least 3 minutes reading or interacting with their assigned material

Complete pre- and post-intervention surveys assessing intent to receive CRC screening

Receive messages tailored to their self-reported demographics, including age, political ideology, gender, education, community setting (urban, rural, suburb), self-reported health, and the last time they saw their PCP

Eligibility criteria:
Inclusion Criteria:

* Adults aged 45 to 75 years
* Currently residing in the United States
* Self-reported no prior colorectal cancer screening (or unsure if screened)
* Able to read and understand English
* Consent to participate in a behavioral research study

Exclusion Criteria:

* Previous colonoscopy, stool test, sigmoidoscopy, or CT colonography
* Failure to pass pre- or post-intervention attention checks
* Identified as non-human respondents by reCAPTCHA or other automated bot detection
* Completion time significantly shorter than the median, indicating inattentiveness

Outcomes:
Change in Self-Reported Likelihood of Completing Stool Test Screening for Colorectal Cancer; Change in Self-Reported Likelihood of Completing Colonoscopy Screening for Colorectal Cancer
Assessment of Decision Support System Software in Extraction and Anchorage Planning Among Adult Patients Using Computer Algorithm
Trial ID: NCT05348109
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-08-22
Completion date: 2023-04
Sponsor: Cairo University
Conditions: Cases That Need Extraction of Teeth in Orthodontics; Anchorage Planning
Interventions: Well finished cases
Locations: Walaa Mohamed Hassan Gadallah
Countries: Egypt
Enrollment: 80
Age range: 15 Years - 35 Years
Sex: ALL

Summary:
It was introduced in dentistry to be used in innovative research and development in addition to facilitating the decision in complicated cases and ensure high patient care quality. In the field of Orthodontics in specific, many studies previously mentioned the idea of artificial intelligence showing very promising results and high degree of reliability. It was used in different domains in orthodontics like diagnosis, treatment planning, evaluation of treatment outcome

Eligibility criteria:
Inclusion Criteria:

1. Cases with well finished orthodontic treatment.
2. Cases with history of crowding more than 10 mm and requiring extraction.
3. Cases with no severe skeletal discrepancy.
4. Well documented cases with both pre-operative and post-operative records.
5. Patients with a full set of permanent teeth erupted

Exclusion Criteria:

1. Improperly finished orthodontic cases.
2. Cases with mild crowding managed by treatment options other than extraction.
3. Growing patients or showing any residual growth remaining in cephalometric analysis
4. Cases with severe skeletal discrepancy.
5. Poorly documented cases.
6. Patients not sticking to anchorage plan

Outcomes:
To study the efficiency of the program decisions in terms of extraction/non-extraction and Anchorage planning decisions
CT Prediction for Transcatheter Tricuspid Interventions
Trial ID: NCT06951126
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-07-19
Completion date: 2030-06-01
Sponsor: Heart and Diabetes Center North-Rhine Westfalia
Collaborators: Ludwig-Maximilians - University of Munich; University Hospital Schleswig-Holstein; University of Cologne; Heart Center Leipzig - University Hospital; Charite University, Berlin, Germany; University of Bern; University Medical Center Mainz
Conditions: Tricuspid Regurgitation; Computed Tomography
Locations: Herz- und Diabeteszentrum
Countries: Germany
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of this study is to enhance the predictability of therapeutic success in transcatheter tricuspid valve intervention (TTVI) for patients with severe tricuspid regurgitation (TR). This will be achieved through automated analyses of pre-interventional computed tomography (CT) scans.

Severe tricuspid regurgitation is associated with poor patient outcomes. In advanced stages, pharmacological therapy becomes ineffective, and surgical intervention carries a high mortality risk. Given this clinical challenge, catheter-based treatment of the tricuspid valve has become a focal point of research.

One well-established treatment strategy is percutaneous tricuspid valve intervention, which aims to reduce regurgitation either through annuloplasty, leaflet-based edge-to-edge repair or valve replacement. This approach has been shown to significantly decrease the severity of regurgitation, leading to a dramatic reduction in symptom burden and a marked improvement in quality of life.

However, predicting which patients will benefit most from TTVI and determining the optimal technique for each individual remain largely unresolved challenges.

Artificial intelligence (AI)-powered software, such as heart.ai by LARALAB (Munich), enables automated measurement of anatomical structures captured via CT imaging. This technology already allows for rapid and precise assessment of cardiac chambers and the tricuspid annulus throughout the entire cardiac cycle, facilitating a comprehensive three-dimensional evaluation of right heart anatomy.

To refine patient selection and optimize procedural strategies for TR treatment, the researcher work a multi-center collaboration to analyze treatment outcomes and patient response to specific therapeutic approaches.

Eligibility criteria:
Inclusion Criteria:

* the patient underwent full cycle cardiac computed tomography for analysis of valvular heart disease
* a transcatheter tricuspid valve intervention is performed
* the patient is 18 years or older

Exclusion Criteria:

* none

Outcomes:
Mortality
Korotkoff Sounds Dynamically Reflect Changes in Cardiac Function Based on Deep Learning Methods: A Prospective, Multicenter Study
Trial ID: NCT06444425
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2024-06-01
Completion date: 2025-12-31
Sponsor: The Fourth Affiliated Hospital of Zhejiang University School of Medicine
Collaborators: Zhejiang Taizhou hospital; The People's Hospital of Quzhou; Zhejiang Quhua Hospital; Hong Kong Applied Science and Technology Research Institute
Conditions: Heart Failure; Deep Learning
Locations: The Fourth Hospital Affiliated to Zhejiang University School of Medicine
Countries: China
Enrollment: 685
Age range: 18 Years - None
Sex: ALL

Summary:
The Korotkoff Sounds（KS）, which have been in use for over a century, are widely regarded as the gold standard for measuring blood pressure. Furthermore, their potential extends beyond diagnosis and treatment of cardiovascular disease; however, research on the KS remains limited. Given the increasing incidence of heart failure (HF), there is a pressing need for a rapid and convenient prehospital screening method. In this study, we propose employing deep learning (DL) techniques to explore the feasibility of utilizing KS methodology in predicting functional changes in cardiac ejection fraction (LVEF) as an indicator of cardiac dysfunction.

Eligibility criteria:
Inclusion Criteria:

1. Patients who were admitted to the Cardiology Department of the aforementioned four Groups (The Fourth Affiliated Hospital of School of Medicine of Zhejiang University, Zhejiang Taizhou Hospital, Quzhou People\&amp;#39;s Hospital, Zhejiang Quhua Hospital)between June 2024 and December 2024, and successfully underwent echocardiographic examinations.
2. Individuals aged between 18 and 90 years with a resting heart rate ranging from 60 to 100 beats per minute.
3. Demonstrating good compliance, capable of cooperating in completing multiple blood pressure measurements within 72 hours following the completion of echocardiography.
4. Willingness to voluntarily participate in the study.

Exclusion Criteria:

1. Patients with acute decompensated heart failure, acute myocardial infarction, atrioventricular block;
2. Cerebral hemorrhage, severe infection, active digestive tract ulcer, severe hematological diseases, severe liver and kidney dysfunction or other serious medical or surgical conditions;
3. Pregnant or lactating women;
4. Patients with mental illness;
5. Individuals who have participated in other clinical trials within the past 3 months.

Outcomes:
Cardiac Dysfunction
A Prospective Pragmatic Cluster-Randomized Care-as-Usual Controlled Study to Evaluate the Impact of an ECG-Based AI Algorithm to Detect Low Left Ventricular Ejection Fraction on Diagnosis Rates of LVEF ≤40% in the Outpatient Setting
Trial ID: NCT05867407
Status: TERMINATED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-06-13
Completion date: 2025-05-30
Sponsor: Anumana, Inc.
Collaborators: Mayo Clinic
Conditions: Ventricular Ejection Fraction
Interventions: Anumana Low EF AI-ECG Algorithm; Care-as-Usual
Locations: Mayo Clinic Arizona; Mayo Clinic Florida; Mayo Clinic Rochester; Duke Health; University of Texas Southwestern
Countries: United States; United States; United States; United States; United States
Enrollment: 11610
Age range: 18 Years - None
Sex: ALL

Summary:
A prospective, cluster-randomized, care-as-usual controlled trial to evaluate the impact of an ECG-based artificial intelligence (ECG-AI) algorithm to detect low left ventricular ejection fraction (LVEF) on diagnosis rates of LVEF ≤ 40% in the outpatient setting.

The objective of this study is to evaluate the impacts of an ECG-AI algorithm to detect low LVEF and an associated Medical Device Data System when used during routine outpatient care. The study will be conducted in 2 phases: feasibility assessment phase and clinical impact phase.

Eligibility criteria:
Inclusion Criteria:

* Males and females 18 years or older (including females who are pregnant, breastfeeding and/or lactating)
* Digital ECG captured or available within site for ECG-AI analysis at point-of-care

Exclusion Criteria:

* Known history of LVEF ≤ 40%
* Known history of systolic heart failure
* Known history of heart failure with reduced ejection fraction
* Opted out of electronic health record-based research

Outcomes:
Diagnosis rates of low ejection fraction of less than or equal to 40 percent by echocardiography compared to care-as-usual
AI-based System for Lung Tuberculosis Screening: Diagnostic Accuracy Evaluation
Trial ID: NCT05889364
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2018-02-01
Completion date: 2023-12-30
Sponsor: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Conditions: Tuberculosis, Pulmonary
Interventions: AI-based x-ray analysis and triage ("normal/tuberculosis suspected")
Locations: Research and Practical Center of Medical Radiology, Department of Health Care of Moscow
Countries: Russia
Enrollment: 308
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Testing of AI solutions to assess diagnostic accuracy for tuberculosis detection.

Eligibility criteria:
Inclusion Criteria:

* no pathology in a lung on chest x-ray
* signs of lung tuberculosis on chest x-ray

Exclusion Criteria:

* any pathology in the lungs (except tuberculosis)

Outcomes:
Diagnostic accuracy metric 1; Diagnostic accuracy metric 2; Diagnostic accuracy metric 3; Diagnostic accuracy metric 4; Diagnostic accuracy metric 5; Diagnostic accuracy metric 6
Endoscopic Multimodal Assessment Using Advanced Imaging Integrated AI to Predict Recurrence in pOSt-oPerativE CRohn's Disease - PROSPER Study
Trial ID: NCT06505304
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-05-01
Completion date: 2026-05-31
Sponsor: University College Cork
Collaborators: The Leona M. and Harry B. Helmsley Charitable Trust
Conditions: Crohn Disease
Interventions: Colonoscopy; Intestinal biopsies; Confocal laser endomicroscopy; Intestinal ultrasound; Stool; Blood; Saliva; Clinical follow-up
Locations: University of Leuven; University of Calgary; University Hospital Erlangen; Cork University Hospital; Mercy University Hospital; University College Dublin; University College Hospitals Galway; Rabin Medical Centre; Istituto Clinico Humanitas; ASST Spedali Civili; ASST Fatebenefratelli Sacco; IRCCS Cà Granda Ospedale Maggiore; University Vita-Salute San Raffaele; University Federico II; IRCCS San Matteo
Countries: Belgium; Canada; Germany; Ireland; Ireland; Ireland; Ireland; Israel; Italy; Italy; Italy; Italy; Italy; Italy; Italy
Enrollment: 225
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This is a multicentre prospective international observational study. This study aims to introduce a novel multidimensional approach to precision imaging, enabling the identification and stratification of high-risk patients who can potentially benefit from early treatments to halt the progression of Crohn's disease (CD). The investigators will develop a novel endoscopic assessment system using endoscopic enhanced imaging (EEI) to evaluate early post-surgical changes and predict post-operative CD recurrence (POCr). By integrating with immune marker profiling, clinical data, and AI assessment of EEI and histology, the investigators further plan to improve risk stratification and reduce interobserver variability.

Eligibility criteria:
Inclusion Criteria:

* Patients aged between 18 years and 75 years.
* Established diagnosis of CD at least six months prior to study.
* Patients who have undergone intestinal resection within 3 months before study entry or have surgery planned.

Exclusion Criteria:

* Inability to provide consent.
* Presence of serious co-morbidities (clinical contraindication).
* Presence of ostomy.
* Pregnancy or breastfeeding.
* Contraindication for colonoscopy or biopsies.
* Boston Bowel Preparation Scale Score \<2 in the rectum plus left-sided colon.

Exclusion criteria for pCLE only:

* Allergy to nuts or shellfish.
* Severe or uncontrolled asthma.
* Use of beta blockers.
* Previous history of reaction to fluorescein.

Patients excluded from pCLE can still enter the study and undergo only standard-of-care endoscopy.

Outcomes:
Early post-operative endoscopic recurrence; Early post-operative clinical recurrence
Contribution to Disease Diagnosis and Prognosis Based on the Fusion of Expert Knowledge, Nuclear Medicine Imaging and Artificial Intelligence (AI) Tools
Trial ID: NCT05963841
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-08-01
Completion date: 2027-08
Sponsor: Centre Hospitalier Universitaire de Besancon
Collaborators: Franche-Comté Electronique Mécanique Thermique et Optique - Sciences et Technologies, UMR 6174
Conditions: Hyperparathyroidism
Interventions: treatment program
Locations: CHU Besancon
Countries: France
Enrollment: 1
Age range: 18 Years - None
Sex: ALL

Summary:
Artificial intelligence algorithms for parathyroid adenoma detection in parathyroid scans The aim of the study is to develop programs for the automatic subtraction of planar and tomographic parathyroid images and for the localization of parathyroid adenomas using artificial intelligence tools.

Inclusion criteria Patients having undergone a double isotope parathyroid diagnostic examination using the standard Besançon University Hospital protocol (double injection of 123I and 99mTc, pinhole image, low energy high resolution (LEHR) and (single-photon emission tomography / Computed tomography) SPECT/CT +/- (positron emission tomography (PET) Choline).

Secondary endpoints Concordance between diagnoses made by nuclear physicians using both methods (current method and new treatment method).

Eligibility criteria:
Inclusion Criteria:

* Men and women over 18 years of age who underwent dual-isotope parathyroid scintigraphy.
* No refusal to use data for research purposes
* Retrospective data (after 13/04/2022: Date of posting of patient information in the department) and prospective data

Exclusion Criteria:

* Patient refusal to use data for research purposes
* Patients who did not benefit from the standard acquisition protocol (123I injection followed by 99mTc 3 hours after LEHR, PINHOLE and SPECT/CT acquisitions)

Outcomes:
Development of an artificial intelligence diagnostic program
A Prospective Non-Interventional Study Using a Multi-Modal Prognostic Test (Ataraxis) for Evaluating the Clinical Integration in Early-Stage Invasive Breast Cancer
Trial ID: NCT07327970
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-20
Completion date: 2027-12-31
Sponsor: Young-Joon Kang
Conditions: Breast Cancer
Interventions: multi-modal foundation AI test
Locations: Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea
Countries: South Korea
Enrollment: 150
Age range: 18 Years - None
Sex: FEMALE

Summary:
This study evaluates the real-world clinical workflow integration of a previously developed artificial intelligence (AI) prognostic test in breast cancer patients receiving neoadjuvant chemotherapy, and validates its accuracy in predicting treatment response.

The Ataraxis AI test analyzes digitized images of tumor biopsy slides combined with basic clinical information (age, tumor stage, hormone receptor status) to generate a risk score. Prior studies showed the AI test can predict cancer recurrence with accuracy comparable to or better than existing genomic tests.

The study has two stages:

* Stage 1 (30 patients): Assess whether the AI test can be practically integrated into routine clinical workflow, including ease of use, report clarity, and time requirements.
* Stage 2 (70-120 additional patients): Validate the accuracy of AI-predicted pathological complete response (pCR) rates against actual surgical outcomes.

This study uses a blinded design where treating physicians remain blinded to AI results until post-surgical pCR assessment. AI analysis is performed by the research coordinator in collaboration with Ataraxis. After pCR evaluation, AI results are disclosed and physicians complete surveys assessing hypothetical treatment changes. This design eliminates AI influence on treatment decisions and ensures independent validation.

Participants are adults with Stage I-III breast cancer planned for neoadjuvant chemotherapy. The study involves no additional procedures beyond standard care except for completing surveys about the AI test experience.

Eligibility criteria:
Inclusion Criteria:

* Histologically confirmed Stage I-III invasive breast cancer
* Planned for neoadjuvant chemotherapy
* H\&E-stained slides available from core needle biopsy
* Age 18 years or older
* Able to provide written informed consent

Exclusion Criteria:

* Metastatic breast cancer (Stage IV)
* Not a candidate for neoadjuvant chemotherapy
* H\&E slides not obtainable from core needle biopsy
* Unable to provide informed consent

Outcomes:
Stage 1 - Feasibility: Clinical Workflow Compatibility Score; pCR Prediction: pCR Prediction Accuracy (AUC-ROC)
Frequency-time Analysis of Pathological Lung Sounds: Detection and Quantification of Pathological Sounds in Patients With Cystic Fibrosis, Pulmonary Fibrosis or COPD (Chronic Obstructive Pulmonary Disease)
Trial ID: NCT06399094
Status: TERMINATED
Study type: OBSERVATIONAL
Start date: 2024-07-18
Completion date: 2025-08-30
Sponsor: Groupe Hospitalier de la Region de Mulhouse et Sud Alsace
Conditions: Pulmonary Fibrosis; Chronic Obstructive Pulmonary Disease; Cystic Fibrosis
Interventions: Lung sound recordings
Locations: Hôpitaux Universitaire de Strasbourg; GHRMSA - Hôpital Emile Muller
Countries: France; France
Enrollment: 23
Age range: 18 Years - None
Sex: ALL

Summary:
The main objective of the study is to assess the potential of time-frequency representation and analysis of pulmonary sounds collected with an electronic stethoscope, as part of the routine monitoring of patients with cystic fibrosis, COPD or pulmonary fibrosis.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Suffering from one of the following pathologies: cystic fibrosis, pulmonary fibrosis, COPD
* Not opposed to participating in the study

Exclusion Criteria:

* Person under court protection, guardianship or curatorship
* Person deprived of liberty by judicial or administrative decision
* Patient with a history of thoracic surgery, thoracic deformity, heart failure or other relevant illness at the investigator's discretion.

Outcomes:
Lung sounds visible in their representation as time-frequency images