moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
AutoMated Burn Diagnostic System for Healthcare
Trial ID: NCT05167461
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-05-24
Completion date: 2023-05-25
Sponsor: Indiana University
Collaborators: Eskenazi Health
Conditions: Burns Multiple; Burns; Thermal Burn; Burn Infection
Interventions: Collection of imaging by ultrasonography
Locations: Eskenazi Health
Countries: United States
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
The primary objective of this study is to develop a high accuracy and automated system that can provide early assessment of burn injuries with at least 90% accuracy in absence of burn experts, using AI and FDA cleared harmonic ultrasound TDI data based on the analysis of mechanical and hemodynamic properties of the subcutaneous burned tissue. Data collected in this study will lead to the development of better diagnostic tools that could inform clinical burn practices by enabling doctors to determine burn depth and the need for surgery with greater speed and accuracy, resulting in better clinical outcomes.

Eligibility criteria:
Subjects must meet all the following enrollment criteria to be eligible for participation in the study:

1. Male or female patients aged 18 years and above.
2. The patient has a thermal burn injury
3. Antibiotic use allowed
4. Patient does not need to have the ability to communicate
5. Inpatient or outpatient
6. Within 7 days of burn
7. No prior surgical debridement
8. Burn ≤ 75% of body surface
9. The patient or their legal representative agrees to comply with all compulsory study procedures and visit schedule
10. The patient or their legal representative agrees to abstain from enrollment in any other clinical trial for the duration of the study.
11. In the opinion of the investigator, the patient or their legal representative must be able to:

    1. Understand the full nature and purpose of the study, including possible risks and adverse events,
    2. Understand instruction, and
    3. Provide voluntary informed written consent/assent as appropriate for study participation.

Exclusion Criteria: Subjects who meet any of the following criteria are not eligible for participation in the study:

1. Unable to provide informed consent
2. Age \<18 years
3. Burn ≥ 75% of body surface
4. Burns caused by chemicals, electricity or radiation.
5. Patients presenting with only 3rd-degree/full-thickness wounds which require immediate autografting.
6. Burn injury has had prior surgical treatment.
7. Prisoners
8. Pregnant individuals
9. Unable to follow study schedule or understand study instructions

Outcomes:
Compare human assessment of burn depth to AI assessment
Using Artificial Intelligence as a Diagnostic Decision Support Tool to Help the Diagnosis of Skin Disease in Primary Healthcare in Catalonia
Trial ID: NCT04562168
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-01-15
Completion date: 2021-12-31
Sponsor: Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina
Collaborators: iDoc24; Institut Català de la Salut
Conditions: Skin Diseases
Interventions: Autoderm® dermatology search engine (ML model) testing
Locations: CAP Navàs
Countries: Spain
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
Background: Dermatological conditions are a relevant health problem. Machine learning models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, specially for skin cancer detection and classification.

Objective: The objective of this study is to perform a prospective validation of an image analysis ML model, which is capable of screening 44 different skin disease types, comparing its diagnostic capacity with that of General Practitioners (GPs) and dermatologists.

Methods: In this prospective study 100 consecutive patients who visit a participant GP with a skin problem in central Catalonia will be recruited, data collection is planned to last 7 months. Skin diseases anonymized pictures will be taken and introduced in the ML model interface, which will return top 5 accuracy diagnosis. The same image will be also sent as a teledermatology consultation, following the current workflow. GP, ML model and dermatologist/s assessments will be compared to calculate the precision, sensitivity, specificity and accuracy of the ML model.

Eligibility criteria:
Inclusion Criteria:

* Patients who have cutaneous disease reason-for-visit.
* Patients who provide written informed consent.
* Patients who are 18 years of age or older.

Exclusion Criteria:

* Patients with advanced dementia.
* Patients with a cutaneous lesion which can't be photographed with a smartphone and images with poor quality.
* Patients who have conditions associated with risk of poor protocol compliance.

Outcomes:
Sensitivity of the ML model; Specificity of the ML model; Accuracy of the ML model; Area under the receiver operating characteristic curve of the ML model
Clinical Validation Study of a CAD System With Artificial Intelligence Algorithms for Early Noninvasive in Vivo Cutaneous Melanoma Detection
Trial ID: NCT06221397
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-09-17
Completion date: 2023-11-13
Sponsor: AI Labs Group S.L
Collaborators: Servicio Vasco de Salud Osakidetza, Spain; Osakidetza; Hospital de Basurto; Hospital de Cruces
Conditions: Melanoma; Melanoma, Skin
Interventions: AI-based Computer-Aided Diagnosis (CAD) Software for Skin Lesion Analysis.
Locations: University Hospital of Cruces
Countries: Spain
Enrollment: 105
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to learn if a computer-aided diagnosis (CAD) system can help identify skin cancer (cutaneous melanoma). The research focuses on adults who have skin spots that a doctor thinks might be cancerous. The main questions the study aims to answer are:

Can the artificial intelligence (AI) tool accurately identify melanoma in skin images?

How does the tool's accuracy compare to the clinical judgment of expert skin doctors (dermatologists)?

Researchers will compare the results from the AI tool to the final diagnosis made by doctors or through a skin biopsy. A biopsy is a medical test where a small piece of skin is removed and checked in a lab.

Participants will:

Have their skin spots photographed using a special camera attached to a smartphone.

Allow researchers to use their clinical data and biopsy results for the study.

The study does not change the medical care participants receive. Doctors will continue to treat participants as they normally would. By testing this tool, researchers hope to find a way to detect skin cancer earlier and more accurately

Eligibility criteria:
Inclusion Criteria:

* Patients with skin lesions with suspected malignancy
* Age over 18 years old
* Patients who consent to participate in the study by signing the Informed Consent form

Exclusion Criteria:

* Patients under 18 years of age

Outcomes:
Area Under the ROC Curve (AUC) for Melanoma Detection; Accuracy for Melanoma Detection; Sensitivity for Melanoma Detection; Specificity for Melanoma Detection
Artificial Intelligence-assisted Integrated Care to Promote Colonoscopy Uptake in China: a Cluster Randomized Controlled Trial
Trial ID: NCT07261059
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-08
Completion date: 2026-12
Sponsor: Fudan University
Collaborators: Sun Yat-sen University; Shandong University; Shandong Cancer Hospital and Institute
Conditions: Colorectal Neoplasms; Colonoscopy
Interventions: AI-assisted integrated care
Enrollment: 400
Age range: 40 Years - 64 Years
Sex: ALL

Summary:
Colorectal cancer (CRC) ranks the second most common cancer and the fourth leading cause of cancer-related deaths in China. Early screening of CRC has been proven to reduce the incidence and mortality, with colonoscopy as the gold standard for CRC screening. This trial aims to evaluate the effectiveness of artificial intelligence-assistant integrated care for improving uptake rate of colonoscopy among high-risk individuals aged 40 to 64 in China. It's a two-arm, parallel cluster randomized controlled trial. The main question it aims to answer is whether the AI-assisted integrated care influence participants' screening-related knowledge, health beliefs, behavioral intention, and uptake of colonoscopy.

Participants will:

1. Be recruited and allocated into one of two groups according to the assigned clusters. Participants in one group will be invited to receive usual specialty care. In addition to usual specialty care, participants in the other group will receive AI-assisted integrated care provided by specialist and general practitioners collaboratively.
2. Complete a questionnaire survey on their knowledge, health beliefs, behavioral intention on CRC screening.
3. Have their colonoscopy status checked at the middle and end of trial.

Eligibility criteria:
Inclusion Criteria:

* Individuals who test positive on either the Colorectal Cancer Risk Assessment Scale or the fecal immunochemical test (FIT);
* Aged 40 \~ 64 years;
* Proficient in smartphone use and able to engage with the intervention;
* Provided informed consent .

Exclusion Criteria:

* History of colorectal cancer;
* Contraindications to colonoscopy,(e.g. severe cardiac, cerebral, lung diseases, or renal dysfunction).

Outcomes:
Uptake of colonoscopy; Time to completion of colonoscopy
(Professor Gong Renrong) Ward Matron
Trial ID: NCT06057688
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-08-01
Completion date: 2024-12-31
Sponsor: Renrong Gong
Conditions: Pulmonary Embolism; Respiratory Failure; Infection Complication; Cardiac Arrest
Locations: West China Hospital, Sichuan University.
Countries: China
Enrollment: 1770
Age range: 14 Years - 90 Years
Sex: ALL

Summary:
The goal of this observational study is to establish an intelligent early warning system for acute and critical complications of the respiratory system such as pulmonary embolism and respiratory failure. Based on the electronic case database of the biomedical big data research center and the clinical real-world vital signs big data collected by wearable devices, the hybrid model architecture with multi-channel gated circulation unit neural network and deep neural network as the core is adopted, Mining the time series trends of multiple vital signs and their linkage change characteristics, integrating the structural nursing observation, laboratory examination and other multimodal clinical information to establish a prediction model, so as to improve patient safety, and lay the foundation for the later establishment of a higher-level and more comprehensive artificial intelligence clinical nursing decision support system.

Issues addressed in this study

1. The big data of vital signs of patients collected in real-time by wearable devices were used to explore the internal relationship between the change trend of vital signs and postoperative complications (mainly including infection complications, respiratory failure, pulmonary embolism, cardiac arrest). Supplemented with necessary nursing observation, laboratory examination and other information, and use machine learning technology to build a prediction model of postoperative complications.
2. Develop the prediction model into software to provide auxiliary decision support for clinical medical staff, and lay the foundation for the later establishment of a higher-level and more comprehensive AI clinical decision support system.

Eligibility criteria:
Inclusion Criteria:

* aged from 14 to 90 years; ② Patients undergoing surgery under general anesthesia; ③ Informed consent to participate in this study

Exclusion Criteria:

* The operation duration is less than 1 hour; ② Patients who cannot wear sensors for vital signs monitoring due to local skin abnormalities; ③ Combined with bilateral axillary surgery; ④ Incomplete bilateral axillary skin

Outcomes:
Unplanned ICU admission
Prospective Validation of a Deep Learning Algorithm for Prediction of the Left Ventricular Ejection Fraction From Coronary Angiogram Videos in Patients With Acute Coronary Syndrome
Trial ID: NCT05317286
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-06-01
Completion date: 2024-02-29
Sponsor: Montreal Heart Institute
Collaborators: Ottawa Heart Institute Research Corporation
Conditions: Acute Coronary Syndrome; Left Ventricular Dysfunction
Locations: Robert Avram
Countries: Canada
Enrollment: 240
Age range: 18 Years - None
Sex: ALL

Summary:
Left ventricular ejection fraction (LVEF) is one of the strongest predictors of mortality and morbidity in patients with acute coronary syndrome (ACS). Transthoracic echocardiography (TTE) remains the gold standard for LVEF measurement. Currently, LVEF can be estimated at the time of the coronary angiogram but requires a ventriculography. This latter is performed at the price of an increased amount of contrast media injected and puts the patients at risk for mechanical complications, ventricular arrhythmia or atrio-ventricular blocks. Artificial intelligence (AI) has previously been shown to be an accurate method for determining LVEF using different data sources. Fur the purpose of this study, we aim at validating prospectively an AI algorithm, called CathEF, for the prediction of real-time LVEF (AI-LVEF) compared to TTE-LVEF and ventriculography in patients undergoing coronary angiogram for ACS.

Eligibility criteria:
Inclusion Criteria:

* Informed consent signed by the participant
* Acute coronary syndrome
* Creatinine clearance ≥30 ml/min/m2 according to MDRD

Exclusion Criteria:

* Creatinine clearance \<30 ml/min/m2 according to MDRD
* No indication to perform TTE in the 7 days following coronary angiogram
* Right bundle branch block
* Suspected or confirmed left ventricular thrombus
* Suspected or confirmed aortic dissection
* Ventriculography not feasible
* No left coronary system angiogram

Outcomes:
Area under the curve-Receiver Operating Characteristics (AUC-ROC) of the CathEF algorithm for differentiating a LVEF ≤ or >50%, compared to TTE-LVEF
Improving Artificial Intelligence-derived Algorithms for Estimating Length and Weight in NEonateS and infanTs up to 6 Months of Age
Trial ID: NCT07514182
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-26
Completion date: 2026-10
Sponsor: Danone Asia Pacific Holdings Pte, Ltd.
Collaborators: KK Women's and Children's Hospital
Conditions: Growth; Neonates; AI (Artificial Intelligence)
Locations: KK Women's and Children's Hospital
Countries: Singapore
Enrollment: 60
Age range: 0 Days - 6 Months
Sex: ALL

Summary:
The NEST study is a prospective, observational research study designed to collect clinical measurements and image data to develop and evaluate artificial intelligence (AI)-derived algorithms for estimating anthropometric parameters in neonates and young infants. The study focuses on infants from birth up to 6 months of age and aims to assess the accuracy of AI-based estimations of length, weight, and head circumference using photographs and/or video recordings captured during routine clinical care. These AI-derived measurements will be compared against standard clinical measurements obtained by trained healthcare professionals in neonatal and infant care settings.

Eligibility criteria:
Inclusion Criteria:

1. Infants up from birth up to 6 months of postnatal age (including neonates) who have been admitted to the NICU or SCN at the time of screening
2. Parent(s) should be able to comprehend the content of the study and be willing for their child to undergo video and photo recording, and to allow access to their blood sampling results (haemoglobin) taken as part of standard clinical practice
3. Written consent from parents and/or legally acceptable representative

Exclusion Criteria:

1. Infants who were born with gestational age of less than 28 weeks of gestational age
2. Infants who are intubated (i.e., endotracheal, nasotracheal intubation) at the time of screening
3. The investigator considers for any reason that the participant would not be suitable for the study
4. The participant has an existing medical condition that would prevent standardised measurement of length and/or head circumference e.g. structural abnormality of the lower limbs, orthopaedic conditions, hydrocephalus
5. Employees and/or children/family members or relatives of employees of Danone Global Research \& Innovation Center, Danone Asia Pacific Holdings Pte Ltd, or the participating site

Outcomes:
To evaluate the accuracy of the algorithm to estimate length (in cm)
Prediction of Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography
Trial ID: NCT05482269
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2011-01-01
Completion date: 2026-12-31
Sponsor: Shengjing Hospital
Conditions: Pulmonary Embolism and Thrombosis; Deterioration, Clinical; Artificial Intelligence
Interventions: no intervention
Locations: Shengjing Hospital
Countries: China
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
The investigators aim to build a predictive tool for Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography.

Eligibility criteria:
Inclusion Criteria:

* age of ≥ 18 years and a pulmonary embolism diagnosis based on CT pulmonary angiography

Exclusion Criteria:

* pregnancy
* reception of reperfusion treatment before admission
* missing data regarding CT parameters, echocardiography, cardiac troponin I (c-Tn I), and N-terminal-pro brain natriuretic peptide (NT-pro BNP) levels.

Outcomes:
Incidence of Treatment-Emergent Adverse Events
Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images
Trial ID: NCT07163767
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-01
Completion date: 2028-12-31
Sponsor: Guangdong Provincial People's Hospital
Conditions: Acute Myocardial Infarction (AMI); Electrocardiography; Artifical Intelligence
Interventions: Deep learning approach of ECG for AMI detection
Locations: Guangdong Provincial People's Hospital
Countries: China
Enrollment: 150000
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to develop and validate an artificial intelligence(AI)-based prediction model for new-onset acute myocardial infarction(AMI) using electrocardiogram(ECG) data. The main question it aims to answer is whether the AI-based ECG accurately forecast new-onset AMI by previous ECG data with 'normal' diagnosis?

Eligibility criteria:
Inclusion Criteria:

* Hospitalized in cardiology department with myocardial injury marker testing (troponin T/I).
* In-hospital patients with ECG records.

Exclusion Criteria:

* First ECG obtained in emergency department.
* ACS diagnosis within 1 month of first ECG.

Outcomes:
Acute myocardial infarction (AMI)
Using Machine Learning to Adapt Visual Aids for Patients With Low Vision
Trial ID: NCT04892316
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-07-27
Completion date: 2021-12-27
Sponsor: Sun Yat-sen University
Conditions: Ophthalmology; Artificial Intelligence; Low Vision Aids
Interventions: Diagnostic test
Locations: 2nd Affilliated Hospital of Fujian Medical University
Countries: China
Enrollment: 400
Age range: 3 Years - 105 Years
Sex: ALL

Summary:
According to the WHO's definition of visual impairment, as of 2018, there were approximately 1.3 billion people with visual impairment in the world, and only 10% of countries can provide assisting services for the rehabilitation of visual impairment. Although China is one of the countries that can provide rehabilitation services for patients with visual impairment, due to restrictions on the number of professionals in various regions, uneven diagnosis and treatment, and regional differences in economic conditions, not all visually impaired patients can get the rehabilitation of assisting device fitting.

Traditional statistical methods were not enough to solve the problem of intelligent fitting of assisting devices. At present, there are almost no intelligent fitting models of assisting devices in the world. Therefore, in order to allow more low-vision patients to receive accurate and rapid rehabilitation services, we conducted a cross-sectional study on the assisting devices fitting for low-vision patients in Fujian Province, China in the past five years, and at the same time constructed a machine learning model to intelligently predict the adaptation result of the basic assisting devices for low vision patients.

Eligibility criteria:
Inclusion Criteria:

* Low vision
* Aged 3 to 105

Exclusion Criteria:

* Severe systemic disease
* Failure to sign informed consent or unwilling to participate

Outcomes:
Accuracy of fitting results for assisting devices
Prospective, Randomized Controlled Study to Evaluate the Effect of Artificial Intelligence Assisted Optical Diagnosis of Advanced Adenomas
Trial ID: NCT05568992
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-10-06
Completion date: 2022-10-14
Sponsor: Shanghai Jiao Tong University School of Medicine
Conditions: Advanced Adenoma
Interventions: AI system of optical detection of advanced adenomas
Locations: Departments of Gastroenterology and Clinical Laboratory, Shanghai Renji Hospital, Shanghai Jiaotong University School of Medicine
Countries: China
Enrollment: 120
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
This study is a clinical validation of our developed a computer-aided optical dignosis of advanced adenoma using non-magnified NBI image. This study is a randomized clinical trial comparing endoscopists' optical recognition of advanced adenoma for sending to histological examination with our computer-aided system. The hypothesis of the study is that the developed computer-aided system increases the percent of sending actual advanced adenoma Intelligence Assisted Optical Diagnosis of Advanced Adenomas

Eligibility criteria:
Inclusion Criteria:

* Endoscopists with NBI experience

Exclusion Criteria:

* Endoscopists without colonoscopy and NBI experience

Outcomes:
Proportion of advanced adenomas for sending to histological examination