moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps
Trial ID: NCT04586556
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-12-18
Completion date: 2022-05-11
Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)
Conditions: Adenomatous Polyps
Interventions: Polyps detection by Artificial Intelligence
Locations: Université de Montréal; Centre Hospitalier Universitaire de Montréal; IHU Strasbourg
Countries: Canada; Canada; France
Enrollment: 372
Age range: 45 Years - 80 Years
Sex: ALL

Summary:
The investigators hypothesize that the clinical implementation of a deep learning AI system is an optimal tool to monitor, audit and improve the detection and classification of polyps and other anatomical landmarks during colonoscopy. The objectives of this study are to generate preliminary data to evaluate the effectiveness of AI-assisted colonoscopy on: a) the rate of detection of adenomas; b) the automatic detection of the anatomical landmarks (i.e., ileocecal valve and appendiceal orifice).

Eligibility criteria:
Inclusion Criteria :

* Signed informed consent
* Age 45-80 years
* Indication to undergo a lower GI endoscopy.

Exclusion Criteria :

* Coagulopathy
* Poor general health, defined as an American Society of Anesthesiologists (ASA) physical status class \>3
* Emergency colonoscopies
* Hospitalized patients
* Known inflammatory bowel disease (IBD)
* Patients currently in the emergency room

Outcomes:
Number of polyps detected; Evaluation of the automatic report of the colonoscopy quality indicators
Utilizing Augmented Artificial Intelligence for Aminoacidopathies: Experience of Collaborative Laboratory Integrated Reporting From Pakistan
Trial ID: NCT05437445
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-08-01
Completion date: 2020-08-30
Sponsor: Aga Khan University Hospital, Pakistan
Conditions: Inborn Errors of Metabolism
Interventions: Plasma amino acid assay
Locations: Aga Khan University Hospital
Countries: Pakistan
Enrollment: 2081
Age range: 1 Year - 16 Years
Sex: ALL

Summary:
The objective of the study was to interpret metabolic profiles of plasma amino acid (PAA) and compare reference intervals (RI) of PAA data from Pakistan with data from other countries using Clinical Laboratory Integrated Reports (CLIR). One-year data (reference and cases data) of twenty-two PAA, analyzed by ion exchange high performance chromatography at Biochemical Genetics Laboratory (BGL) of Aga Khan University Pakistan, was compiled in a comma-separated values (.csv) file and uploaded on CLIR Software using AAQP (Amino Acid in Plasma) application for statistical analysis. Among total of 2081 PAA profiles, 92% (n=1913) were completely normal with all PAA values falling within the age-specific reference range. A concordance of 98.8% was noted between the reporting done by the BGL at AKU and then after applying CLIR tools.

Eligibility criteria:
Inclusion Criteria:

All plasma samples during 1 year received at Biogenetic Lab

Exclusion Criteria:

Subjects with age greater than 16 years

Outcomes:
postanalytical interpretation of concentration of plasma leucin, isoleucine and valine in nmol/L using Clinical Laboratory Integrated Reports (CLIR)
A Prospective Multicenter Study of AI-assisted Gastroscopic Varices Diagnosis
Trial ID: NCT05293457
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-04-25
Completion date: 2022-12-25
Sponsor: Renmin Hospital of Wuhan University
Conditions: Artificial Intelligence
Locations: Renmin Hospital of Wuhan University
Countries: China
Enrollment: 793
Age range: 18 Years - None
Sex: ALL

Summary:
Validation of the accuracy of AI in assisting gastroscopic varices diagnosis through a prospective multicenter study

Eligibility criteria:
Inclusion Criteria:

1. Male or female aged 18 years or above;
2. Gastroscopy and related examinations are required to further clarify the characteristics of digestive tract diseases;
3. Patients with liver cirrhosis who have signs or symptoms of portal hypertension and have clinical indications for endoscopic screening for varicose veins.
4. Able to read, understand and sign the informed consent;
5. The researcher believes that the subjects can understand the process of the clinical study, are willing and able to complete all the research procedures and follow-up visits, and cooperate with the research procedures.

Exclusion Criteria:

1\) Pregnant or breastfeeding women; 2) Patients with a history of esophagogastric surgery; 3) Patients who have received interventional or endoscopic treatment in the past; 4) The patient has high-risk diseases or other special conditions that are not suitable for participating in clinical trials; 5) The patient is clearly diagnosed with esophageal or gastric diverticulum, polyps, cancer and other diseases that may affect the observation of varicose veins; 6) The patient's endoscopic video cannot reach a consensus diagnosis and treatment opinion after being watched by three experts.

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Outcomes:
The accuracy of artificial intelligence
Study on the Medical Education Capability of the EyeTeacher Artificial Intelligence Platform
Trial ID: NCT06759012
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-05
Completion date: 2025-12-30
Sponsor: Tsinghua University
Conditions: Medical Education
Interventions: EyeTeacher
Locations: University of Melbourne; Peking Union Medical College Hospital; University of Ghana; Sankara Nethralaya; University of Malaya; Duke-NUS medical school; Moorfields Eye Hospital
Countries: Australia; China; Ghana; India; Malaysia; Singapore; United Kingdom
Enrollment: 252
Age range: 21 Years - 40 Years
Sex: ALL

Summary:
With the rise of generative artificial intelligence and large language models, medical education is undergoing a significant transformation. Numerous studies have highlighted the limitations of traditional educational knowledge acquisition and the potential impact of artificial intelligence on medical education, resident training, and continuing education for clinical practitioners. However, there is a lack of real-world experiments on the effectiveness of AI-integrated education.

Artificial intelligence can provide extensive educational resources and tools that are not limited by geographical location or language, thereby lowering the barrier to accessing high-quality medical education and promoting educational equity. Nevertheless, the performance of AI models trained by different medical institutions or healthcare systems may vary.

To offer a more universal, accessible, high-quality, and interconnected educational journey. We have developed a virtual ophthalmology teacher, which developed based on foundational model and large language models. This model, named EyeTeacher provide comprehensive theoretical knowledge and clinical skills enhancement for untrained medical students. To verify the effectiveness of our EyeTeacher across different national ophthalmology education systems and languages, we plan to implement a randomized controlled trial. This trial will assess the clinical capabilities of all participants and explore the advantages and disadvantages of the system compared to traditional teaching methods.

Eligibility criteria:
Inclusion Criteria:

* Clinical medical students who have not taken ophthalmology courses
* Age 21-40
* Gender not restricted
* Sign the informed consent form

Exclusion Criteria:

* Refusal of the research protocol.
* Participants unwilling or unable to understand and complete the questionnaire.

Outcomes:
Grade of ophthalmology examination
Examining Mechanisms of Action in Superpower Glass Behavioral Intervention for Children With Autism
Trial ID: NCT04049981
Status: TERMINATED
Study type: INTERVENTIONAL
Phase: PHASE1; PHASE2
Start date: 2019-09-04
Completion date: 2020-07-01
Sponsor: Stanford University
Conditions: Autism Spectrum Disorder; Autism
Interventions: Superpower Glass
Locations: Stanford University
Countries: United States
Enrollment: 20
Age range: 4 Years - 8 Years
Sex: ALL

Summary:
The following study aims to understand the mechanism of action at work in a novel artificial intelligence (AI) tool that runs on Google Glass through an Android app to deliver social emotion cues to children with autism during social interactions. This study will examine 2 versions of software on the Google Glass based wearable intervention system. Participants will receive 1 of 2 versions of the software and use the device at home for 4 weeks. This novel device will use a camera, microphone, head motion tracker to analyze the behavior of the subject during interactions with other people. The system is designed to give participants non-interruptive social cues in real-time and will record social responses that can later be used to help aid behavioral therapy. It is hypothesized that both mechanisms under investigation will contribute to social gains in children over the 4 week period of use.

Eligibility criteria:
Inclusion Criteria:

* Parent of a child with a professional diagnosis of autism who is at least 18 years old.
* Child with autism is 4-8 years old
* Parent/Guardian is able to drive to Stanford University for 2 in lab appointments.(Lives within driving distance of Stanford University).

Exclusion Criteria:

* Parent/Guardian unable to speak and read in English.
* Child with autism receives Social Communication Questionnaire score\>15

Outcomes:
Change in socialization subscale scores of the Vineland Adaptive Behavior Scales,3rd Edition (VABS-III) from baseline to week 4.; Change in Parent Rated Social Responsiveness Scale 2 (SRS-2) from baseline to week 4
Development and Multicenter Validation of a Personalized Transcranial Alternating Current Stimulation Protocol for Cognitive Impairment in Older Adults
Trial ID: NCT07208734
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-01
Completion date: 2027-11-30
Sponsor: Xuanwu Hospital, Beijing
Conditions: Alzheimer s Disease
Interventions: Transcranial Alternating Current Stimulation (tACS); AI-based Cognitive Training; Sham intervention
Locations: Xuanwu Hospital, Capital Medical University, Beijing, Beijing 100053
Countries: China
Enrollment: 460
Age range: 50 Years - 90 Years
Sex: ALL

Summary:
This study aims to develop and test new personalized treatments for older adults with cognitive impairment.

Project 1: Create a personalized cognitive training program using computer algorithms to match training tasks to individual needs. About 300 participants will join a randomized trial at hospitals and community health centers.

Project 2: Develop a personalized brain stimulation program (tACS) based on brain imaging and artificial intelligence. About 160 participants will be enrolled to test safety and effectiveness.

Project 3: Build a framework for ethical data management by reviewing international practices and consulting experts.

Together, these projects will provide evidence for safe, effective, and personalized care, while ensuring responsible use of research data.

Eligibility criteria:
Inclusion Criteria:

1. Inpatients or outpatients aged 50-90 years;
2. Patients diagnosed with amnestic mild cognitive impairment (aMCI) due to AD or mild dementia due to AD, based on the 2018 NIA-AA clinical diagnostic criteria established by the National Institute on Aging and the Alzheimer's Association;
3. Neuropsychological assessment showing MMSE scores between 18 and 26, and a CDR score of 0.5 or 1;
4. Able to communicate fluently in Chinese (non-illiterate);
5. For those currently receiving cholinesterase inhibitor therapy (e.g., donepezil or rivastigmine), the treatment dose must be stable, defined as a fixed dosage administered continuously for at least six weeks, with no adjustment of the medication regimen during the study period;
6. Provision of written informed consent.

Exclusion Criteria:

1. Documented history of cerebrovascular stroke, with clear neurological deficits at onset and corresponding responsible lesions on neuroimaging;
2. Moderate to severe white matter lesions (Fazekas score 3-6);
3. Presence of any cause of consciousness disorder;
4. Severe aphasia or motor disability that prevents completion of neuropsychological assessments;
5. Current psychiatric disorders;
6. History of alcohol dependence, drug addiction, traumatic brain injury, epilepsy, encephalitis, normal-pressure hydrocephalus, or other neurological disorders that may cause cognitive impairment;
7. Systemic diseases that may contribute to mild cognitive impairment (e.g., hepatic or renal insufficiency, endocrine disorders, vitamin deficiencies);
8. Considered unsuitable for participation by the investigators.

Outcomes:
Moca score
Retrospective Analysis of the Correlation Between CT/MRI Imaging Features and Pathology, Prognosis in Patients With Renal Tumors
Trial ID: NCT06167863
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-08-31
Completion date: 2023-10-31
Sponsor: Zhen Li
Conditions: Radiomics; Deep Learning; Artificial Intelligence; Body Composition
Interventions: radiomics
Locations: Zhen Li
Countries: China
Enrollment: 1000
Age range: None - None
Sex: ALL

Summary:
Renal cell carcinoma (RCC) is the most common malignant tumor in the kidney with a high mortality rate. Traditional imaging techniques are limited in capturing the internal heterogeneity of the tumor. Radiomics provides internal features of lesions for precise diagnosis, prognosis prediction, and personalized treatment planning. Early and accurate diagnosis of renal tumors is crucial, but it's challenging due to morphological and pathological overlap between benign and malignant lesions. The accurate diagnosis of RCC, especially for small tumors, remains a significant challenge. Recent studies have shown a relationship between body composition, obesity, and renal tumors. Common indicators like body weight and BMI fail to reflect body composition accurately. Research on the role of body composition, including adipose tissue, in tumor pathology could improve clinical diagnosis and treatment planning.

Eligibility criteria:
Inclusion Criteria:

* Our hospital admits patients with renal tumors in the urology department. The diagnosis is confirmed through surgical pathology, and the patients' imaging data is obtained through contrast-enhanced Computed Tomography or Magnetic Resonance examination in the radiology department.

Exclusion Criteria:

* Patients who have undergone puncture, microwave, interventional therapies before the examination, or who have received chemotherapy or radiotherapy;
* Patients with poor respiratory coordination, resulting in significant image artifacts;
* Lesions are cystic, without discernible regions of interest, or with multiple regions of necrosis within the lesion;
* Lesions are too small, with a diameter of less than 1cm;
* Thin-slice imaging is not available in the CT scan.

Outcomes:
WHO/ISUP grade pathologically
Optimization of Late Imagery Rescripting Research Using Generative Artificial Intelligence
Trial ID: NCT07565714
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04
Completion date: 2026-07
Sponsor: University of Social Sciences and Humanities, Warsaw
Conditions: Generalized Anxiety; Fear of Failure; Rumination
Interventions: Imagery Rescripting Intervention
Locations: SWPS University (University of Social Sciences and Humanities); Poznań Laboratory of Affective Neuroscience; SWPS University of Social Sciences and Humanities; Poznań Laboratory of Affective Neuroscience
Countries: Poland; Poland
Enrollment: 40
Age range: 18 Years - 35 Years
Sex: ALL

Summary:
The aim of the study is to examine the effect of imagery rescripting (ImRs) in the context of utilizing large language models (LLMs). Intervention will involve the prior presentation of the most aversive fragment of the memory, the so-called 'hotspot.' This intervention will allow for the replication of the effect described by Dibbets and Arntz (2016), according to which the prior activation of the most emotional element of a memory enhances the effectiveness of ImRs.

The study is also significant due to another ongoing study in which a substantial number of participants have already been examined; however, due to the exhaustion of funds, it was not possible to utilize the remainder of the recruited sample. Investigating an additional condition will allow for a more complete utilization of the available participant pool and significantly increase the project's scientific value by comparing the traditional ImRs mechanism with its AI-generated version.

Eligibility criteria:
Inclusion Criteria:

* Age 18-35
* Score ≥ 8 on GAD-7 (Plummer et al., 2016)
* Ability to recall at least two childhood memories involving parental criticism

Exclusion Criteria:

* History of prolonged physical or sexual abuse
* Current psychotherapy or psychopharmacology
* PTSD diagnosis (DSM-5 screening)
* Substance abuse (TAPS tool)

Outcomes:
Generalized Anxiety Disorder DSM Scale (GAD); The Performance Failure Appraisal Inventory; Intrusive Thought Frequency (Rumination Inventory - adapted); Skin Conductance Level (SCL); Emotional Response Ratings (Subjective)
Artificial Intelligence-assisted Diagnosis and Prognostication in COVID-19 Using Electrocardiograms and Imaging
Trial ID: NCT04510441
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-05-26
Completion date: 2022-05-01
Sponsor: Imperial College London
Collaborators: Imperial College Healthcare NHS Trust; Chelsea and Westminster NHS Foundation Trust; London North West Healthcare NHS Trust
Conditions: Coronavirus
Interventions: Nil intervention
Locations: London North West University Healthcare NHS Trust; Chelsea and Westminster Hospital NHS Foundation Trust; Imperial College London (Hammersmith campus); St Mary's Hospital
Countries: United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
Coronavirus Disease 2019 (COVID-19) has been widespread worldwide since December 2019. It is highly contagious, and severe cases can lead to acute respiratory distress or multiple organ failure. On 11 March 2020, the WHO made the assessment that COVID-19 can be characterised as a pandemic. With the development of machine learning, deep learning based artificial intelligence (AI) technology has demonstrated tremendous success in the field of medical data analysis due to its capacity of extracting rich features from imaging and complex clinical datasets. In this study, we aim to use clinical data collected as part of routine clinical care (heart tracings, X-rays and CT scans) to train artificial intelligence and machine learning algorithms, to accurately predict the course of disease in patients with Covid-19 infection, using these datasets.

Eligibility criteria:
Inclusion Criteria:

* have ECGs, Chest x-ray and/or chest CT imaging (with or without contrast)
* positive laboratory Covid-19 virus nucleic acid test (RTPCR assay with throat swab samples) or clinical suspicion for Covid-19 infection
* be aged \>18 years

Exclusion Criteria:

* Suboptimal ECGs, chest radiographs or CT studies for deep learning methods due to artefacts including severe
* motion artefacts which causes blurring of the contours of or significant artefacts due to metallic prosthesis which causes image degradation
* Time-interval between ECGs, chest CT and the RT-PCR assay was longer than 7 days

Outcomes:
Accuracy of machine learning to be able to predict outcome of coronavirus (COVID-19) infection; Accuracy of machine learning to be able to predict prognosis of coronavirus (COVID-19) infection
Advanced GC Multi-omic Characterization in EU and CELAC Populations
Trial ID: NCT04015466
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-06-12
Completion date: 2023-12-31
Sponsor: Fundación para la Investigación del Hospital Clínico de Valencia
Collaborators: Instituto Nacional de Cancerologia de Mexico; Amsterdam UMC, location VUmc; Pontificia Universidad Catolica de Chile; Vall d'Hebron Institute of Oncology; INSTITUTO ALEXANDER FLEMING; Hospital Central del IPS; IPATIMUP - Instituto De Patologia E Imunologia Molecular Da Universidade Do Porto
Conditions: Gastric Cancer
Locations: Instituto Alexander Fleming; Pontificia Universidad Católica de Chile; Instituto Nacional de Cancerología de México; VU Medical Centre; GenPat; Institute of Pathology and Immunology of University of Porto; Vall d'Hebron Institut d'Oncologia; Hospital Clínico Univeristario de Valencia
Countries: Argentina; Chile; Mexico; Netherlands; Paraguay; Portugal; Spain; Spain
Enrollment: 800
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
Observational study (cohort type) of advanced GC patients that will be recruited prospectively to study biological factors associated with the disease and relevant clinical outcomes.

Eligibility criteria:
Cases:

* Inclusion criteria:

  * Subjects ≥18 years old.
  * GC diagnosis stages III and IV (including gastroesophageal junction cancer) and/or a gastroscopy indication due to the high diagnostic suspicion of GC as part of the study of his disease.
  * Has given and signed the IC to participate in this study.
* Exclusion criteria:

  • Patients diagnosed with GC early disease (stage I and II) suitable for resectable strategy.
* Withdrawal criteria:

  * Patients initially recruited with high suspicion of GC diagnosis but not confirmed by the pathological report.

Controls:

* Inclusion criteria (only for microbiome analysis):

  * Subjects ≥18 years old.
  * Subjects to whom a gastroscopy is indicated within clinical care and is confirmed absent of GC in the same centres will be matched in age (+/- 10 years), gender and pertaining from the same region of the GC case.
  * Has given and signed the IC to participate in this study.
* Exclusion criteria:

  * Subjects from a different geographic area from the cases.
  * Patients with high suspicion of GC.
  * Patients with previous histopathologic diagnosis of peptic (gastric or duodenal) ulcer disease and/or atrophy or intestinal metaplasia.
  * Patients that have received antimicrobials during the 4 weeks period prior to the endoscopy.
  * Patients that have received proton pump inhibitors or H2-receptor antagonists, at least 2 weeks prior to the endoscopy

Outcomes:
Development of a new diagnostic algorithm for gastric cancer that includes molecular landscape, patient history, histopathological and environmental factors, personal microbiome and immune landscape
Collection of Fingertip Video for Determination of Heart Rate
Trial ID: NCT06696339
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-10-03
Completion date: 2025-03-01
Sponsor: PreemptiveAI, Inc
Conditions: Heart Rate in the General Public
Interventions: PreemptiveAI SDK
Locations: Paul Allen Institute for Artificial Intelligence
Countries: United States
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to assess the accuracy of the PreemptiveAI SDK to measure heart rate for adults as compared to an FDA-approved ground truth device.

Participants will:

* Complete a demographics and health history survey
* Apply and wear ground truth equipment such as a pulse oximeter and ECG
* Measure their heart rate 8 times with 4 different smartphones using the PreemptiveAI SDK

Eligibility criteria:
Inclusion Criteria:

* Adults with both normal and abnormal heart rates

Exclusion Criteria:

* Under 18 years of age
* Unable to speak and read English
* Deemed not suitable to participate by Investigator due to cognitive impairment, acute illness, or serious physical or mental health condition
* Essential tremor in left or right hand
* Raynaud's disease
* Severe calluses on left or right index finger
* Unable to produce a heart rate measurement due to low signal quality during the training phase (i.e. low perfusion, unable to hold hand steady, etc)

Outcomes:
Heart Rate Error