moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Prospective, Multi-center Registry to Evaluate Safety and Efficacy of Hativ® ELectrocardiogram Monitoring on Patients with Hemodialysis
Trial ID: NCT06688565
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-13
Completion date: 2025-12-31
Sponsor: Hallym University Medical Center
Conditions: End-Stage Kidney Disease; Hemodialysis
Interventions: Hativ® electrocardiogram monitoring
Locations: Hallym University Sacred Heart Hospital
Countries: South Korea
Enrollment: 300
Age range: None - None
Sex: ALL

Summary:
The high mortality rate and costs of patients with end-stage renal disease (ESRD) are a medical and socio-economic burden, so managing their risk factors is very important. Recently, artificial intelligence (AI)-based electrocardiogram (ECG) analysis technology has made it possible to detect cardiovascular diseases using only ECGs by distinguishing minute differences in electrical currents in the heart that are difficult for humans to read, and active verification of its clinical effectiveness is in progress.

In this study, using Hativ®, a currently commercially available class II medical device, ECGs are measured every day at home and before and after dialysis. Additionally, ECGs are measured when chest discomfort or unusual symptoms were present, and the corresponding symptoms are monitored. It targets patients with smartphones over the age of 19 who are receiving maintenance hemodialysis for more than 3 months due to ESRD and excludes those with intracardiac electrodes and devices. Demographic characteristics, medications, blood tests, and echocardiography results are collected, and the results of additional questionnaires or cardiac tests are monitored for up to 3 months. During the study period, if there are any critical abnormalities or symptoms on the ECGs, the medical staff or the subject may request additional treatment.

In conclusion, by measuring ECGs before and after hemodialysis, the investigators aim to obtain data on the frequency and type of arrhythmia, confirm the relationship between cardiovascular disease and dialysis intervals. The investigators will also apply an AI-based ECG analysis model to the measured ECGs to determine the association with the mortality rate. Furthermore, the investigators plan to establish a basis for using AI analysis technology combined with a portable ECG device in the management of patients with ESRD and conduct follow-up research to improve prognosis.

Eligibility criteria:
Inclusion Criteria:

* patients with smartphones
* patients over the age of 19 who
* patients receiving maintenance hemodialysis for more than 3 months due to ESRD

Exclusion Criteria:

* those with intracardiac electrodes and devices

Outcomes:
Incidence of arrhythmia; Numeric value of electrocardiogram
Observational Study of Urine Metabolites in the Diagnosis of Disease
Trial ID: NCT06710067
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-10-01
Completion date: 2026-09
Sponsor: Luventix, Inc.
Conditions: Colorectal Cancer; Small Intestinal Bacterial Overgrowth Syndrome (SIBO); Crohn Disease; Celiac Disease
Locations: Unio Health Partners (Gastroenterology); Digestive Health Associates; Westside Gastro Care; Bass Medical Group (Gastroenterology)
Countries: United States; United States; United States; United States
Enrollment: 1250
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to validate a non-invasive, urine-based diagnostic technology for the detection and differentiation of various gastrointestinal (GI) diseases.

This research study intends to enroll participants across a range of demographics and GI disease states including colorectal cancer, small intestinal bacterial overgrowth (SIBO), Crohn\&#39;s disease, and Celiac disease, collect urine samples and clinical data, and use artificial intelligence and machine learning to build disease-specific models which can identify and differentiate a participants' specific GI disease.

The main questions it aims to answer are:

1. Does the platform identify a disease signal within each disease cohort, compared to normal controls?
2. How well does the test perform (e.g. sensitivity and specificity/false-positive rate)?

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years of age at time of enrollment.
* Able and willing to provide a one-time urine sample and comply with all study procedures for the study.
* Able to understand the study procedures, able to provide consent to participate in the study, and willing to authorize release of relevant protected health information by consenting to a HIPAA medical release form.

Exclusion Criteria:

* Known to be pregnant.
* A medical condition which, in the opinion of the Investigator and/or Sponsor, should preclude enrollment in the study.

Outcomes:
Disease signal detection; Test performance measures
SMART DIALYSIS - Scaling Machine Learning and Artificial Intelligence AlgoRithms to OpTimize the Performance and Delivery of Acute DIALYSIS.
Trial ID: NCT07312929
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-06-01
Completion date: 2031-06-30
Sponsor: University of Alberta
Conditions: Renal Dialysis; Renal Replacement Therapy; Renal Diseases; Quality Health Care
Interventions: intermittent OR continuous renal replacement therapies
Enrollment: 7500
Age range: None - None
Sex: ALL

Summary:
SMART DIALYSIS - Scaling Machine Learning and Artificial Intelligence AlgoRithms to OpTimize the Performance and Delivery of Acute DIALYSIS.

Hypothesis:

Can the investigators develop and implement Machine Learning and Artificial Intelligence Algorithms into Clinical Information Systems to Optimize the Prescription, Delivery, and Performance of Acute Dialysis?

Objective(s):

1. Identify variables surrounding identified Key Performance Indicators that may be used by Machine Learning and Artificial Intelligence algorithms to optimize the prescription and performance of acute dialysis.
2. Develop Machine Learning and Artificial Intelligence algorithms to help guide the prescription and delivery of acute dialysis in the development of Clinical Decision Support tools and Best Practice Advisories and create a ML/AI Augmented SMART DIALYSIS Digital Dashboard.
3. Implement and evaluate the performance of the developed Machine Learning and Artificial Intelligence algorithms on patient-centered and health economic outcomes.
4. Validate and benchmark the performance of the evaluated Machine Learning and Artificial Intelligence algorithms across multiple jurisdictions.

Eligibility criteria:
Inclusion Criteria

Patients admitted to an intensive care unit (ICU) who require acute renal replacement therapy, either intermittent or continuous.

Exclusion Criteria

Receipt of renal replacement therapy for less than 24 hours.

Pre-existing end-stage kidney disease.

Outcomes:
Identify Key Performance Indicators that may be used by Machine Learning algorithms.; Develop Artificial Intelligence and Machine Learning algorithms; Evaluate the performance of the developed Artificial Intelligence and Machine Learning algorithms.
Accuracy Assessment of Artificial Intelligence Versus Conventional Digital Design for Fixed Dental Prosthesis: (An Invitro Study)
Trial ID: NCT07432165
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-15
Completion date: 2026-06-17
Sponsor: October University for Modern Sciences and Arts
Conditions: Accuracy of Artificial Intelligence in Fixed Dental Prosthesis Design
Interventions: Conventional CAD-Based Fixed Dental Prosthesis Design; Artificial Intelligence-Based Fixed Dental Prosthesis Design
Locations: MSA University
Countries: Egypt
Enrollment: 1000
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
This in vitro study aims to evaluate the accuracy of an Artificial Intelligence (AI)-based automatic design system for fixed dental prosthesis (FDP) compared with conventional computer-aided design (CAD) software. Digital scans of teeth requiring fixed dental prosthesis will be collected and used to generate prosthetic designs using two approaches: human-designed CAD restorations and AI-generated restorations.

The primary outcome is design accuracy assessed using 3D superimposition and Intersection over Union (IOU) percentage. Secondary outcomes include margin detection performance measured using F1 score, precision, and recall. A total sample size of 438 scans will be analyzed.

The study will determine whether AI-generated prosthesis designs demonstrate comparable accuracy to conventional digital designs.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18-65 years Patients with a damaged tooth requiring a fixed dental prosthesis Available digital intraoral scans Adequate occlusal anatomy for analysis of opposing teeth

Exclusion Criteria:

* Incomplete or poor-quality digital scans Severe occlusal abnormalities affecting analysis Patients outside the specified age range

Outcomes:
Accuracy of AI-Designed Fixed Dental Prosthesis Compared to Human-Designed Prosthesis
Evaluation of AI-Enhanced Symptom Summarization in Weekly Radiotherapy Consultations: A Comparative Study
Trial ID: NCT06525181
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-22
Completion date: 2024-12-15
Sponsor: jaide
Collaborators: National Cancer Institute, Brazil
Conditions: Radiotherapy Side Effect; Pelvic Cancer; Patient
Interventions: Generative Artificial Intelligence; Standard weekly symptom intake
Locations: Instituto Nacional de Câncer José Alencar Gomes da Silva - INCA
Countries: Brazil
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
The study investigates the use of artificial intelligence (AI) and large language models (LLMs) to enhance the efficiency and accuracy of weekly treatment consultations (OTVs) in radiotherapy. It hypothesizes that an AI-enabled symptom summary tool will match traditional medical review methods in accuracy while saving time. The study includes patients undergoing pelvic radiotherapy and excludes those with pelvic reirradiation or who have undergone surgery. Patients will receive both standard and AI-assisted weekly consultations, with AI summaries generated using the OpenAI GPT-4 API. Blinded oncologists will compare the accuracy and quality of the AI-generated and doctor-generated summaries, while patients and doctors will rate these summaries. The primary objective is to evaluate the accuracy and time efficiency of AI-assisted symptom summaries compared to traditional methods.

Eligibility criteria:
Inclusion Criteria:

All patients undergoing radiotherapy in the pelvic region.

Exclusion Criteria:

Cases of pelvic reirradiation or operated cases.

Outcomes:
The Physician Documentation Quality Instrument-9 (PDQI-9)
Artificial Intelligence Analysis of Initial Scan Evolution of Traumatic Brain Injured Patient to Predict Neurological Outcome: Pilot Translational an Exploratory Study
Trial ID: NCT04058379
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-01-01
Completion date: 2021-10-12
Sponsor: University Hospital, Grenoble
Conditions: Trauma, Brain
Interventions: CT scan
Locations: University Hospital Grenoble
Countries: France
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
We assume that an early iterative automatic CT scan analysis (D0, D1 and D3) by different AI approaches will allow an early differentiation of the tissues evolution after TBI. Our objective is to couple theses scan profiles to a neurological evolution, measured by therapeutic intensity.

Eligibility criteria:
Inclusion Criteria:

* Age \> or = 18 years old
* Closed TBI
* Primary admission in Grenoble University Hospital
* Initial CT scan with visible cerebral lesion rated at least 3 on abbreviated injury score (AIS)
* In ICU for an expected length of 48 hours
* Social security system affiliation

Exclusion Criteria:

* Life expectation \<48 hours
* In ICU for more than 24h
* Transferred from another hospital
* Patients corresponding to articles L1121-5, L1121-6, L1121-7, L1121-8 (under legal protection) of French Public Health Code
* Patient in exclusion time of another study

Outcomes:
Clinical evolution during first 7 days in ICU with therapeutic intensity level (TILsum)
ERYthrocyte Morphology Using Flow Imaging on ImageSTREAM Cytometer
Trial ID: NCT05282888
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-03-08
Completion date: 2023-08
Sponsor: Centre Hospitalier Universitaire, Amiens
Collaborators: Hôpital Necker-Enfants Malades
Conditions: Red Blood Cells; Schistocytes; Artificial Intelligence
Interventions: blood sample
Locations: CHU Amiens Picardie
Countries: France
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
Erythrocyte morphology analysis is a key step in the diagnosis flowchart of anemia. It is performed on a peripheral blood smear after May Grümwald Giemsa staining. In the context of hemolytic anemias for example, it allows the recognition of therapeutic emergencies such as sickle cell disease crisis, malaria-induced hemolysis and thrombotic microangiopathy, the latter being characterized by the presence of schistocytes and justifying an immediate clinical care. However, cytological analysis of erythrocyte morphology requires pre-analytical interventions (smear spreading + staining), the quality of which determines the accuracy of the result. Moreover, it requires a good cytological expertise and may be sometimes subjective. For several years, alternative methods for erythrocyte morphology evaluation have been developed, based on automated hematology machines or automated microscopy. Nevertheless, none of them has yet proven itself in comparison with cytology, especially in the diagnosis of thrombotic microangiopathies. By combining the advantages of flow cytometry and microscopy, flow imaging appears to be a promising technology for the diagnosis of anemias: it does not require any pre-analytical intervention, does not require any spreading and analyzes a large number of events. Moreover, it can be coupled with artificial intelligence via the generation of an apprenticeship by the constitution of a large image data base, which then allows the recognition of the different red blood cells morphologies without human eyes. The objective of this study is to build a data base containing the main red blood cell morphologies relevant in anemia, and to validate it through a comparison in anemic patients of erythrocyte morphological assessment either directly on whole blood by flow imaging or routinely by cytological analysis of peripheral blood smear after by a trained operator.

Eligibility criteria:
Inclusion Criteria:

* Any patient for whom a blood test including a cytological evaluation of RBC morphology is performed in the routine laboratory of CHU Amiens

Exclusion Criteria:

* patients who have opposed the use of their personal data for research work

Outcomes:
Concordance between classical cytological erythrocyte analysis and ImageStream flow imaging erythrocyte analysis
Prospective Assessment of Risk Predictions of Organ Failure in the Intensive Care Unit - Comparing Accuracy of Human and AI Risk Predictions
Trial ID: NCT06814327
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-11-18
Completion date: 2026-05-15
Sponsor: ETH Zurich
Collaborators: Insel Gruppe AG, University Hospital Bern
Conditions: Circulatory Failure; Respiratory Failure; Renal Failure
Locations: University Hospital Inselspital, Berne
Countries: Switzerland
Enrollment: 499
Age range: 18 Years - None
Sex: ALL

Summary:
During this observational study, the investigators aim to assess the ability of ICU clinicians to predict the risk of impending organ failure and retrospectively compare it to the performance of previously published machine learning models. The central hypothesis of this study is that the treating physician can predict impending organ failure in adult ICU patients with similar accuracy as the best previously publishes machine learning models.

Eligibility criteria:
Inclusion Criteria:

* patient minimum age of 18 years
* emergency admission to the ICU
* arterial line in place

Exclusion Criteria:

* documented refusal (on the general consent form) to participate to clinical research
* patients with neurologic conditions that impair the patient's level of consciousness (including, but not limited to stroke, traumatic brain injury, intracranial hemorrhage, CNS infections; except polytrauma)
* patients on mechanical circulatory support systems (IABP, VA-ECMO, Impella, VAD) or extracorporeal membrane oxygenation (VV-ECMO) at any time during their ICU stay;
* patients receiving end-of-life care or are admitted for the sole purpose of evaluating organ donation

Outcomes:
Clinician prediction of circulatory failure within 8 hours compared to published ML models
Ophthalmic Diseases and AI: an Parallel Comparison RCT Study
Trial ID: NCT07154680
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-08-15
Completion date: 2025-01-30
Sponsor: North Sichuan Medical College
Collaborators: Afﬁliated Hospital of North Sichuan Medical College
Conditions: Eye Diseases
Interventions: GPT-4o mini;Claude 3 Haiku;Gemini 1.5 Flash;Llama 3.1 7OB;GPT-4o;Claude 3.5 Sonnet;Gemini 1.5 Pro;Llama 3.1 4O5B
Locations: Afﬁliated Hospital of North Sichuan Medical College
Countries: China
Enrollment: 2000
Age range: None - None
Sex: ALL

Summary:
Ophthalmic diseases are a major category of conditions affecting visual health, including but not limited to cataracts, glaucoma, retinal and choroidal diseases, and refractive errors (such as myopia, hyperopia, and astigmatism). With the advancement of technology, artificial intelligence (AI) is being increasingly applied in the field of ophthalmology. This clinical trial aims to evaluate the potential of large language models (LLMs) in ophthalmology.

The main questions to be addressed are:

1. Assessing the effectiveness of large language models (LLMs) in the diagnosis and treatment of ophthalmic diseases: Through randomized controlled trials (RCTs), evaluate the diagnostic and treatment effectiveness of LLMs in the field of ophthalmic diseases, exploring their potential to improve the quality and efficiency of ophthalmic care.
2. Investigating the role of LLMs in medical consultations: Explore the role and effectiveness of LLMs in medical consultations for ophthalmic diseases, including their ability to provide medical advice, explain diagnostic results, and help patients understand treatment plans.
3. Examining the ability of LLMs to adhere to ethical standards: Study how to ensure that LLMs comply with ethical standards and moral principles in ophthalmic medical consultations, safeguarding patient privacy and rights.
4. Providing new technological support for the field of ophthalmology: Through research on the application of LLMs in ophthalmic diseases, offer new technological support and innovations to enhance the quality and efficiency of ophthalmic care.
5. Exploring the differences between LLMs and ophthalmologists: By utilizing multiple large language models, compare the differences between LLMs and ophthalmologists in diagnostic outcomes, case analysis processes, and patient experiences during diagnosis and treatment.
6. Evaluating the effectiveness of LLMs in ophthalmic diseases: Collect patient complaints, fundus images, doctors' diagnoses, and diagnosis times from offline doctor consultations, as well as gather AI-generated medical advice, diagnostic efficiency, and diagnostic accuracy online. Ultimately, conduct comprehensive data analysis to determine the feasibility and effectiveness of LLMs in diagnosing and treating ophthalmic diseases.

Eligibility criteria:
Inclusion Criteria:

* There are patient complaints

Exclusion Criteria:

* No patient complaints

Outcomes:
Large Language Model Diagnostics
A Multicenter Prospective Paired Observational Study Evaluating the Performance of the PTFinder Dual-Channel Near-Infrared and White-Light Imaging System With AI-Assisted Analysis for Rapid Identification of Parathyroid Glands in Surgical Specimens
Trial ID: NCT07010380
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06-01
Completion date: 2029-12-31
Sponsor: Bo Wang,MD
Conditions: Thyroid Neoplasms; Hyperparathyroidism; Parathyroid Gland
Interventions: PTFinder Dual-Channel NIR Autofluorescence Imaging System; Offline Machine-Learning Algorithm (PTFinder-AI Beta)
Locations: Fujian Medical University Union Hospital
Countries: China
Enrollment: 180
Age range: 18 Years - None
Sex: ALL

Summary:
This study tests PTFinder, a dual-camera system that makes parathyroid glands glow on screen by capturing their natural near-infrared autofluorescence. After a thyroid or parathyroid operation, the removed tissue is scanned with PTFinder and then checked again under normal white light. We will measure how fast (seconds) and how accurately the device finds real glands, confirmed by frozen pathology or a rapid PTH strip. About 180 adult patients at three Chinese hospitals will join. The imaging adds only a few minutes and does not change any part of the surgery. We will also record blood calcium and PTH at 1 h, 24 h, and 7 d after surgery to see whether better gland recovery lowers low-calcium risk.Collected images will also be used to train and test a deep-learning model for fully automated parathyroid recognition; model performance metrics constitute secondary outcomes.

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years.
* Scheduled for elective total thyroidectomy, hemithyroidectomy, or parathyroidectomy with removal of thyroid/parathyroid tissue.
* Able and willing to provide written informed consent for participation and specimen imaging.

Exclusion Criteria:

* History of neck irradiation or prior neck surgery that may distort parathyroid anatomy.
* Pregnancy or breastfeeding.
* Confirmed or suspected parathyroid carcinoma, or thyroid carcinoma requiring en-bloc parathyroid resection.
* Inability to understand the study procedures or to comply with follow-up.

Outcomes:
Detection Rate of Pathology-Confirmed Parathyroid Glands per Specimen (PTFinder)
Nerve and Plane Block Data Collection Study for the Development of Artificial Intelligence-assisted Software
Trial ID: NCT06375603
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-12-01
Completion date: 2023-05-01
Sponsor: Smart Alfa Teknoloji San. ve Tic. A.S.
Collaborators: Ankara University
Conditions: Ultrasound Imaging of Anatomical Structures; Nerve Block
Interventions: Ultrasound scans
Locations: Ankara University School of Medicine
Countries: Turkey (Türkiye)
Enrollment: 200
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The primary objective of this observational study is to acquire ultrasound images (raw data) from nerve and plane block regions to develop artificial intelligence-guided nerve block software (Nerveblox). The study aims to acquire raw ultrasound video from 200 volunteers' planned nerve and plane block regions. These participants will undergo ultrasound scans administered by expert clinicians proficient in these techniques in their clinical practice. The data collection devices utilized in this study are FDA-cleared general-purpose ultrasound devices.

Eligibility criteria:
Inclusion Criteria:

* Male at least 18 years of age
* Female at least 18 years of age
* Health volunteers
* Able to confirm and sign the Informed Consent Form before enrolment the study

Exclusion Criteria:

* Male below 18 years of age
* Female below 18 years of age
* Unwilling to participate or unable to sign an informed consent form
* Inability to lie flat
* Anatomical deformity in the area to be scanned for nerve and plane blocks

Outcomes:
Assessment of image quality