moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Validation of a Smartphone-based Intelligent Diagnosis and Measurement for Strabismus
Trial ID: NCT05615519
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-12-02
Completion date: 2023-04-01
Sponsor: Sun Yat-sen University
Conditions: Strabismus; Exotropia; Esotropia; Vertical Strabismus
Interventions: A new technology based on 3D reconstruction and deep learning algorithm to achieve an automatic diagnosis of strabismus based on patient-sourced videos of programmatic cover tests.
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Countries: China
Enrollment: 300
Age range: 3 Years - None
Sex: ALL

Summary:
The current measurement methods of strabismus include the corneal light reflection method, prism alternate covering, etc., which especially rely on the subjective experience of doctors, and there is a large error between different measurers， leading to serious underestimation of strabismus prevalence and insufficient care for strabismus patients. Here, the investigators established and validated an artificial intelligence system to achieve an automatic diagnosis of strabismus based on patient-sourced videos of programmatic cover tests. Three-dimensional reconstruction methods are used to digitize the parameters of head and eye positions. This system has been integrated into a smartphone platform to be further validated through hospital-based and population-based clinical trials.

Eligibility criteria:
Inclusion Criteria:

The quality of facial videos should be clinically acceptable.

Outcomes:
The effectiveness of smartphone-based diagnosis; The consistency between manual and smartphone measurement
A Clinical Trial With a Self-controlled, Multicenter, Pediatric EEG Intelligent Analysis System to Assist in Diagnosis
Trial ID: NCT06918457
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06-03
Completion date: 2026-07-01
Sponsor: Kunming Children's Hospital
Conditions: Pediatric Epilepsy
Interventions: AI EEG analysis system
Enrollment: 1000
Age range: None - 18 Years
Sex: ALL

Summary:
A diagnostic accuracy study on Artificial intelligence EEG analysis system assisted doctors to diagnose pediatric epilepsy.

Eligibility criteria:
Inclusion Criteria:

* Age \< 18 years old
* Children with suspected epilepsy

Exclusion Criteria:

* During EEG monitoring, the patients had other serious neurological diseases and mental diseases concurrently
* Used medication that affect EEG data within 3 weeks, such as sedatives and anti-epileptic medications
* Substandard data quality, such as data lack of key records, electrode connection discontinuity, insufficient recording time, or the presence of serious artifacts
* Incomplete or missing data
* Equipment or operational abnormalities, data for which EEG monitoring has not been performed continuously

Outcomes:
To evaluate the coincidence rate between doctors' independent diagnosis and the diagnosis recommended by the AI system.
Development and Validation of an Artificial Intelligence-assisted System for Bowel Cleanliness Assessment Based on Withdrawal Distance Weighting
Trial ID: NCT07150130
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-01
Completion date: 2028-10-01
Sponsor: Fudan University
Conditions: Colorectal Adenoma
Interventions: No Intervention: Observational Cohort
Locations: Huadong hospital, Fudan university
Countries: China
Enrollment: 700
Age range: None - None
Sex: ALL

Summary:
To address the limitations of current AI-based systems that rely on the assumption of a "constant withdrawal speed," this study proposes the integration of the UPD-3 endoscopic positioning system. By using colonoscope withdrawal videos in combination with UPD-3 imaging data as training samples, we aim to develop an AI-powered bowel cleanliness assessment system that incorporates "withdrawal distance" as a weighting factor. This approach is expected to yield a more reliable, objective, and clinically applicable intelligent assessment system that better aligns with real-world clinical practice and endoscopists' operational habits.

Eligibility criteria:
Inclusion Criteria:

* Clear colonoscopy images suitable for BBPS scoring
* Complete and clear colonoscopy videos suitable for BBPS scoring
* Clear colonoscopy videos with a stable UPD-3 positioning system, without signal drift, disappearance, or other disruptions

Exclusion Criteria:

* Blurred colonoscopy images
* Colonoscopy images taken from the small intestine or outside the patient's body
* Colonoscopy images captured during irrigation or instrument manipulation
* Colonoscopy images obtained during chromoendoscopy
* Colonoscopy videos that do not contain the complete withdrawal process
* Videos in which the UPD-3 colonoscopic positioning system exhibited signal drift, disappearance, or other instability

Outcomes:
Prediction accuracy of the models
Video-based Assessment of Preschool Children's Gross Motor Development for Early Intervention Screening
Trial ID: NCT07054437
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06-11
Completion date: 2026-08-31
Sponsor: Chang Gung Memorial Hospital
Collaborators: National Taiwan Normal University
Conditions: Gross Motor Development Delay
Interventions: Gross Motor Development Screening Tool
Locations: Linkou Chang Gung Memorial Hospital
Countries: Taiwan
Enrollment: 250
Age range: 1 Year - 6 Years
Sex: ALL

Summary:
Artificial intelligence (AI) is currently one of the global focal points for industrial development, with its applications in healthcare steadily increasing, such as in disease prediction, image diagnosis, and drug development. AI assists healthcare professionals in clinical decision-making by training relevant models through algorithms, thereby enhancing medical efficiency and quality.

Currently, standardized tools are used in clinical settings to screen and assess various aspects of child development. Children's motor development levels are determined by comparing their performance against established norms. However, the current assessment methods primarily rely on on-site visual observation and recording by evaluators, which demands significant time and human resources.

This research aims to establish an automated screening tool for gross motor development in early intervention, suitable for independently walking children aged one to six years old in Taiwan. The goal is to reduce the time cost of manual assessment and enable remote healthcare applications.

Eligibility criteria:
Inclusion Criteria:

* Legal guardian willing to provide written informed consent.
* Males and females aged 1 to 6 years old.
* Capable of independent walking.

Exclusion Criteria:

\- Non-native Chinese speakers.

Outcomes:
Accuracy of AI-based gross motor development screening model compared to pediatric therapist's CDIIT gross motor subscale assessment
Machine Learning for Prediction of Therapy Response in Autoimmune Hepatitis
Trial ID: NCT07378202
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-05
Completion date: 2028-01
Sponsor: Hannover Medical School
Collaborators: Technische Universität Dresden
Conditions: Autoimmune Hepatitis
Locations: Else Kroener Fresenius Center for Digital Health, Technical University Dresden; Hannover Medical School
Countries: Germany; Germany
Enrollment: 5000
Age range: None - None
Sex: ALL

Summary:
The 5th International Autoimmune Hepatitis Group (IAIHG) research workshop emphasized the integration of large clinical cohorts with artificial intelligence (AI) for enhanced prediction of therapy responses and outcomes in Autoimmune Hepatitis (AIH). This project aims to develop and validate machine learning (ML) models using data from the R-Liver registry and other international cohorts. After rigorous preprocessing to ensure data uniformity and quality, the investigators will identify and characterize factors influencing therapy response. They will then implement ML models to predict complete biochemical response (CBR) at 6 and 12 months, using five-fold cross-validation, and validate these models in external cohorts from Spain, Canada, and the international AIH group, ensuring robustness and generalizability.

Finally, the investigators will prospectively validate the models in newly registered cases, assessing both short-term and long-term outcomes. This project seeks to advance personalized treatment strategies in AIH, facilitating timely adjustments in therapy and improving patient prognosis through AI-driven decision support. This projects' interdisciplinary team, with expertise in clinical AI and hepatology, is well-equipped to address these challenges and enhance the clinical management of AIH.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of Autoimmune hepatitis
* Consent to provide data to the specific registry
* available follow-up data at least within the first 12 months

Exclusion Criteria:

* variant syndromes
* extrahepatic autoimmune disease
* immunosuppressive treatment at diagnosis

Outcomes:
Number of patients with complete biochemical response at 6 months after diagnosis; Number of patients with complete biochemical response at 12 months after diagnosis
A Case-Control Study To Determine The Suitability Of Artificial Intelligence For Leukemia Diagnostics
Trial ID: NCT04466059
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-01-05
Completion date: 2025-07-31
Sponsor: Munich Leukemia Laboratory
Conditions: Hematologic Malignancy; Leukemia; Minimal Residual Disease; Lymphoma; Blood Cancer
Interventions: Automated AI-Guided Diagnosis of Hematological Malignancies
Locations: MLL Munich Leukemia Laboratory
Countries: Germany
Enrollment: 25000
Age range: 18 Years - None
Sex: ALL

Summary:
To the best of our knowledge, BELUGA will be the first prospective trial investigating the usefulness of deep learning-based hematologic diagnostic algorithms. Taking advantage of an unprecedented collection of diagnostic samples consisting of flow cytometry datapoints and digitalized blood-smears, categorization of yet undiagnosed patient samples will prospectively be compared to current state-of-the-art diagnosis at the Munich Leukemia Laboratory (hereafter MLL). In total, a collection of 25,000 digitalized blood smears and 25,000 flow cytometry datapoints will be prospectively used to train an AI-based deep neuronal network for correct categorization. Subsequently, the superiority will be challenged for the primary endpoints: sensitivity and specificity of diagnosis, most probable diagnosis, and time to diagnose. The secondary endpoints will compare the consequences regarding further diagnostic work-up and, thus, clinical decision making between routine diagnosis and AI guided diagnostics. BELUGA will set the stage for the introduction of AI-based hematologic diagnostics in a real-world setting.

Eligibility criteria:
Inclusion Criteria:

* Patients having been diagnosed with a suspected hematological disorder
* The suspected diagnoses constitute a primary diagnosis
* Only samples of patients min.18 years of age will be used
* Samples must suffice quality attributes which are denoted in "Exclusion Criteria"

Exclusion Criteria:

* The sample is not fit for state-of-the-art diagnosis or fails initial quality control. For quality insurance, we will exclude samples in heparin- instead of EDTA. Samples with damage due to atmospheric reasons (freeze-thaw damage or elevated temperature) will be excluded.
* Samples with too scarce material jeopardizing routine gold-standard diagnosis will be excluded.
* Bone marrow aspirates without sufficient material to assess malignant or healthy hematopoiesis.

Outcomes:
Sensitivity and Specificity of AI Guided diagnostics in Hematology
Development of the PAIN (Pain AI iNtervention) Platform for Patients at Home
Trial ID: NCT05474274
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2022-11-23
Completion date: 2027-11
Sponsor: Mayo Clinic
Conditions: Pain
Interventions: Machine learning algorithms
Locations: Mayo Clinic Florida
Countries: United States
Enrollment: 70
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this research is to identify physiological markers to determine pain intensity and build an Artificial Intelligence (AI) enabled system to objectively measure pain intensity. Researchers hope to personalize pain medication regimens to help prevent medication over-use.

Eligibility criteria:
Inclusion Criteria:

* Patients undergoing low-risk outpatient plastic surgery procedures with expected pain intensities ranging from mild to severe.

Exclusion Criteria:

* Patients with treated or untreated cardiopulmonary syndromes.
* Patients with treated or untreated ophthalmologic pathologies.
* Patients with skin pathologies that prevent us from using the TENS device.
* Patients with pathologies or conditions preventing them from appropriately using their voice.
* Patients with barriers to effective communication.
* Patients with poor digital literacy.
* Patients incapable of taking oral medication.
* Patients who are currently taking medical therapy for chronic pain.
* Patients with a previous diagnosis of severe anxiety disorders.
* Patients who are immobile at baseline.

Outcomes:
Using machine Learning for Postoperative Pain Pain Prediction
ProstateAI - A Prospective Paired Validating Cohort Study to Evaluate an MRI-based Optimized Prostate Cancer Diagnostic Pathway Powered by Artificial Intelligence
Trial ID: NCT06360523
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-06-14
Completion date: 2028-03-31
Sponsor: Chinese University of Hong Kong
Conditions: Prostate Cancer
Interventions: AI modal; Standard review
Locations: Prince of Wales Hospital, Chinese University of Hong Kong
Countries: Hong Kong
Enrollment: 368
Age range: 18 Years - None
Sex: MALE

Summary:
It is a prospective paired-cohort study for diagnostic test evaluation. The study aim to determine the accuracy of AI review and investigate whether AI review could detect MRI visible significant cancer as effective as radiologist review. MRI image of about 368 men recommended for biopsy will be reviewed by an AI model and an experienced radiologist, respectively. AI review (index) and radiologist review (standard) will be blinded to each other, while biopsy urologists will be well-informed of the findings of both AI review and radiologist review and make personalized biopsy plan by combining both findings. The pathological results of MRI-ultrasound fusion biopsy (reference) will serve as the gold standard to assess the diagnostic accuracy.

Eligibility criteria:
Inclusion Criteria:

* Patients recommended for prostate biopsy for diagnosis or re-assessment.
* Men at least 18 years or over
* Patients with prostate MRI image eligible for radiologist review and AI review.
* Patient Informed Consent is signed.

Exclusion Criteria:

* Patient contraindicated to prostate biopsy
* Patient failed to complete the biopsy procedure

Outcomes:
Prostate Cancer detection rate of ISUP grade group ≥2
Translational Identification of Medication Repurposing Candidates to Slow Cognitive Decline in Dementia
Trial ID: NCT05817188
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-01-01
Completion date: 2025-03-01
Sponsor: Karolinska Institutet
Collaborators: KTH Royal Institute of Technology; Region Stockholm
Conditions: Alzheimer Dementia; Dementia Disorders
Interventions: No intervention
Locations: Karolinska Institutet
Countries: Sweden
Enrollment: 100000
Age range: 40 Years - None
Sex: ALL

Summary:
Dementia creates a great personal and societal burden and there are currently no treatments to stop memory loss. Many patients with dementia take medications to treat other conditions, e.g. high blood pressure and cholesterol. Some of these medications may have central effects on the pathophysiological processes leading to dementia.

Medication repurposing is a cost and time-effective way to discover new treatments. Swedish registers are a unique tool to detect medication candidates for repurposing. The Swedish Dementia Registry (SveDem) has \>100000 patients and ca. 144000 measures of cognition over time (measured with the Mini-Mental State Examination-MMSE), making it perfect to examine whether certain medications are associated with less cognitive decline in patients with dementia. Cholesterol and kidney function affect treatment and cognition and are available for 25000 patients. The investigators propose a study combining traditional cohort studies and newer artificial intelligence (machine learning) techniques to detect medications associated with slower cognitive decline in patients from SveDem. The investigators will test pre-specified hypotheses since the investigators suspect specific medications are better candidates to prevent cognitive decline, but the investigators will also let the machine learning algorithms explore the data to find associations that the investigators do not suspect in advance. Finally, the candidate medications will be tested in animal and cellular models to determine the mechanisms of their effects.

Eligibility criteria:
Inclusion Criteria:

All patients with dementia registered in SveDem.

Exclusion Criteria:

Lack of Swedish personal identification number or coordination number.

Outcomes:
Cognitive decline
Artificial Intelligence for Mental Health Diagnoses and Treatment Plans: A Protocol for Actors and Patients
Trial ID: NCT06417827
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-12
Completion date: 2024-12-31
Sponsor: Sheba Medical Center
Conditions: Mental Health Diagnosis
Interventions: SP patients-actors; Patients; Control group- patients
Locations: Sheba Medical Center, Psychiatry Department
Countries: Israel
Enrollment: 220
Age range: 18 Years - None
Sex: ALL

Summary:
Artificial intelligence (AI) is becoming prevalent in modern medicine and psychiatry. AI is based on a wide variety of computer algorithms classified under machine learning (ML).

The purpose of the present study is to evaluate the potential for mental health diagnosis using AI.

In the first part of the study, the AI will conduct an interview with standardized patients \[SP\] (actors) presenting a psychiatric illness. The AI will present a differential diagnosis and treatment plan. Immediately afterward, the actors will be interviewed by a board-certified psychiatrist, who will also give a differential diagnosis and a treatment plan. The results of the AI and psychiatrist will be compared.

In the second part of the study, AI will examine patients coming for consultation by a psychiatrist in the inpatient units, outpatient units, or in the emergency room (ER) at Sheba Medical center. The AI results will be compered to the psychiatrist diagnosis.

Eligibility criteria:
Inclusion Criteria:

For the SPs part:

* Actors SPs employed by "MSR"- the Israeli Center for Medical Simulation, trained to simulate symptoms of psychiatric illnesses.
* Over the age of 18
* Sign an informed consent

For the patient part:

* Patients arriving for intake at the psychiatric clinic, the various psychiatric departments and the ER at the Sheba Medical Center.
* Patients over the age of 18
* Sign an informed consent

Exclusion Criteria:

* Patients participating in another study
* Patients under hospitalization order or court order

Outcomes:
Percent agreement of the diagnosis that provide by the psychiatrist and the AI - SPs (actors) part; Percent agreement of the diagnosis that provide by the psychiatrist and the AI- Patients part
Clinical Validation of an Artificial Intelligence-Based Scoring System for the Modified Rankin Scale (mRS) in Patients With Stroke
Trial ID: NCT07544927
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-10
Completion date: 2028-12-31
Sponsor: Xuanwu Hospital, Beijing
Collaborators: Capital Medical University; People's Hospital of Beijing Daxing District; Beijing Tiantan Hospital; The First Hospital of Fangshan District,Beijing
Conditions: Stroke
Enrollment: 490
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to validate the clinical performance of an artificial intelligence (AI)-based automatic scoring system for the Modified Rankin Scale (mRS). The core comparison is the consistency and accuracy between the AI-generated scores and standardized manual mRS follow-up assessments performed by trained professionals. The goal is to provide a convenient, efficient, and objective tool for stroke prognosis assessment, reduce the subjective variability of manual scoring, and optimize the stroke follow-up workflow.

Eligibility criteria:
Inclusion Criteria:

1. Age ≥ 18 years, male or female.
2. Clinically diagnosed with stroke, and confirmed by cranial CT/MRI to have stroke.
3. Clinically stable, with basic communication ability at discharge or outpatient visit. The patient or a fixed family caregiver is able to cooperate with telephone follow-up at 1 week after discharge or outpatient visit.
4. Signed informed consent by the patient or their legally authorized representative.

Exclusion Criteria:

1. Neurological deficits caused by non-stroke etiologies (e.g., brain tumor, traumatic brain injury, encephalitis).
2. Presence of severe disturbance of consciousness, severe cognitive impairment, psychiatric disorders, or global aphasia at discharge/outpatient visit, preventing effective communication; neither the patient nor family can cooperate with follow-up or assessment.
3. Combined with severe multi-organ failure (e.g., cardiac, hepatic, renal, respiratory), with an expected survival of less than 1 month, making completion of the 1-week follow-up impossible.
4. Long-term bedridden without a fixed caregiver, with no confirmed contact for follow-up, or refusal to participate in telephone follow-up and mRS assessment.
5. Incomplete clinical data, preventing baseline data collection.

Outcomes:
Agreement Between Artificial Intelligence (AI)-Based and Manual Modified Rankin Scale (mRS) Assessments
The Effect of an Education Supported by ChatGPT Integration on Nursing Students' Knowledge and Motivation Levels Regarding Aspiration Practice
Trial ID: NCT07096518
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-04
Completion date: 2025-07-21
Sponsor: Izmir University of Economics
Collaborators: The Scientific and Technological Research Council of Turkey
Conditions: Endotracheal Aspiration; Nursing Education; Artificial Intelegence; Knowledge; Motivation
Interventions: ChatGPT-Integrated Lecture
Locations: İzmir University of ;Economics
Countries: Turkey (Türkiye)
Enrollment: 32
Age range: 20 Years - 70 Years
Sex: ALL

Summary:
This study was designed to examine the effects of a Chat Generative Pre-trained Transformer (ChatGPT)-integrated lecture on nursing students' knowledge, motivation, and knowledge retention related to endotracheal suctioning. The effects of this Artificial Intelligence (AI)-supported lecture were compared with those of a traditional lecture. ChatGPT, an AI-based language model, was used to provide interactive and personalized support during the learning process.

Eligibility criteria:
Inclusion Criteria:

* Undergraduate nursing students who are enrolled in the Surgical Units Nursing II course
* Students who voluntarily agree to participate in the study

Exclusion Criteria:

* Students who do not attend the planned lecture session
* Students who do not respond to the pre- or post-intervention surveys

Outcomes:
Change in Endotracheal Aspiration Knowledge Score