moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Validation Study of an Artificial Intelligence-based Sleep Stage Classification for a Home Sleep Tracking Device
Trial ID: NCT06357039
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-03-21
Completion date: 2023-07-17
Sponsor: PNAPS Health Informatics and Space Technologies Inc.
Collaborators: Analog Devices
Conditions: Sleep; Sleep Disorder
Interventions: Sleep tracking device
Locations: Pnaps Health Informatics and Space Technologies Inc
Countries: Turkey (Türkiye)
Enrollment: 305
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
In this study, a two-part recursive convolutional neural networks model was developed, extracting features for each epoch window independently from before and after sleep onset (epoch encoder), and then trained in the context of long-term relationships in the sleep process (sequence encoder), using an approach similar to human expert classification based on information from single-channel forehead EEG and PPG (IR, Green, Red). The classification is based on guidelines from the American Academy of Sleep Medicine and calculated six parameters: total sleep duration (TST), wake (W), N1, N2, N3, and REM.

The validation study of the developed model and the device was conducted at the Sleep Disorders Centre of the Istanbul Medical Faculty using concurrent polysomnographic data from 305 male and female patients aged 18 to 65 years.

Eligibility criteria:
Inclusion Criteria:

* Participants suffering from sleep disorders
* Participants sent to PSG test by neurologists, pulmonologists, psychiatrists, and otolaryngologists

Exclusion Criteria:

* Anyone who has been diagnosed as having a contagious skin disease
* Participants who do not have consent to have an additional device in their forehead area
* Incomplete of sleep measurement

Outcomes:
Sleep Stages Classification Accuracy; Interoception analysis from PPG data collected from facial skin
Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals
Trial ID: NCT05838456
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-01-01
Completion date: 2022-05-27
Sponsor: The University of Texas Health Science Center, Houston
Collaborators: National Center for Advancing Translational Sciences (NCATS)
Conditions: Acute Ischemic Stroke (AIS)
Interventions: Viz.AI software
Locations: The University of Texas Health Science Center at Houston
Countries: United States
Enrollment: 443
Age range: 18 Years - None
Sex: ALL

Summary:
After onset of Acute Ischemic Stroke (AIS), every minute of delay to treatment reduces the likelihood of a good clinical outcome. A key delay occurs in the time between completion of computed tomography (CT) angiography of the head and neck and interpretation in the setting of AIS care.

The purpose of this study is to assess the effect of incorporating Viz.AI software, which via via a machine-learning algorithm performs artificial intelligence-based automated detection of large vessel occlusions (LVO) on CT angiography (CTA) images and alerts the AIS care team (diagnosis and treatment decisions will be based on the clinical evaluation and review of the images by the treating physician, per routine standard of care). The hypothesis is that integration of the software into the AIS care pathway will reduce delays in treatment. A cluster-randomized stepped-wedge trial will be performed across 4 hospitals in the greater Houston area.

Eligibility criteria:
Inclusion Criteria:

* Male or Female
* 18 years of age or older.
* Patients who present to the emergency department with signs and/or symptoms concerning for acute ischemic stroke.
* Patients who undergo CT angiography imaging
* Patients determined to have a large vessel occlusion acute ischemic stroke. This determination will be made based on official radiology report for the CT angiography imaging.

Exclusion Criteria:

* Patients with incomplete data on the electronic medical record.

Outcomes:
Time From Emergency Room Arrival to Initiation of Endovascular Stroke Therapy ("Door-to-groin" Time)
Computer Assisted Detection of Neoplasia During Colonoscopy Evaluation (CADeNCE)
Trial ID: NCT05888623
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-10-01
Completion date: 2023-12-31
Sponsor: VA Puget Sound Health Care System
Collaborators: VA Salt Lake City Health Care System; San Francisco Veterans Affairs Medical Center
Conditions: Colorectal Neoplasms
Interventions: Computer Assisted Detection
Locations: VA Puget Sound Health Care System
Countries: United States
Enrollment: 334200
Age range: None - None
Sex: ALL

Summary:
The goal of this cluster randomized study is to determine if artificial intelligence systems used during colonoscopy can improve the detection of precancerous polyps in the colon. The primary question it aims to answer is whether computer-assisted detection devices improve the proportion of colonoscopies found to have precancerous adenomatous polyps.

Secondary aims will assess if computer-assisted detection devices improve the proportion of colonoscopies found to other types of precancerous polyps known as sessile serrated lesions, or cancer of the colon and rectum. The study will also assess possible negative effects of use of computer-assisted detection (e.g., prolonging the procedure time or false-positive biopsies) and survey device users to learn about their experience with this technology.

The study team will provide computer-assisted detection devices to randomly chosen VA medical centers for use during colonoscopy and compare colonoscopy findings for patients who undergo colonoscopy at facilities that are equipped with these devices to the findings of patients who undergo colonoscopy at VA facilities that do not have these devices.

A survey will be distributed to physicians who perform colonoscopy to assess their experience using computer-assisted detection devices.

Eligibility criteria:
Inclusion Criteria:

* Colonoscopy performed at a Veterans Affairs (VA) medical center

Exclusion Criteria:

* Colonoscopy performed at VA medical centers that acquired computer-assisted detection artificial intelligence devices through non-random assignment
* Colonoscopy performed at a VA medical center where pathology results are not available

Outcomes:
Adenoma Detection Rate
An Oncology Nurse Artificial Intelligence Agent to Reduce Cancer Distress of Patients Undergoing Radiotherapy: A Mixed-Methods Randomized Controlled Trial
Trial ID: NCT06636188
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-01
Completion date: 2028-11
Sponsor: The University of Hong Kong
Collaborators: Queen Mary Hospital, Hong Kong; Queen Elizabeth Hospital, Hong Kong
Conditions: Head and Neck Cancer
Interventions: Digi-Coach Chatbot
Locations: The University of Hong Kong; Queen Mary Hospital
Countries: Hong Kong; Hong Kong
Enrollment: 138
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to assess the effectiveness of the Digi-Coach chatbot for reducing physical and psychological distress in people undergoing radiotherapy for head and neck cancer.

The main question it aims to answer is: How effective is the Digi-Coach chatbot in reducing physical and psychological distress?

Researchers will compare Digi-Coach chatbot to usual nursing care to see if Digi-Coach chatbot is effective for reducing physical and psychological distress in people undergoing radiotherapy for head and neck cancer.

Participants will have access to the Digi-Coach chatbot during radiotherapy in addition to usual care. The research nurse will provide training on how to navigate the chatbot and ask participants to query all their cancer-related concerns in the chatbot.

Eligibility criteria:
Inclusion Criteria:

* aged 18 and above
* are able to read and speak Cantonese or Mandarin fluently
* have a new, primary diagnosis of head and neck/laryngeal cancer
* are waiting to receive a full course of curative-intent, external beam radiotherapy with or without chemotherapy
* have a Karnofsky performance score ≥70
* have distress scored 3 or above on the NCCN distress thermometer

Exclusion Criteria:

* insufficient command of Chinese
* have any other factors precluding the ability to give informed consent or comply with the protocol

Outcomes:
National Comprehensive Cancer Network Distress Thermometer
NEUTROPHIL FLUORESCENCE AS AN EARLY BIOMARKER OF SEPSIS SEVERITY IN THE EMERGENCY DEPARTMENT
Trial ID: NCT07585942
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05-12
Completion date: 2027-12-12
Sponsor: University Hospital, Strasbourg, France
Conditions: Sepsis; Disseminated Intravascular Coagulation (DIC); Septic Shock
Locations: The University Hospitals of Strasbourg, Intensive Care Medicine Department - New Civil Hospital
Countries: France
Enrollment: 492
Age range: 18 Years - None
Sex: ALL

Summary:
Sepsis is a medical emergency whose prognosis depends on the early identification of patients at risk of septic shock, but the tools available in the Emergency Department remain insufficient. Neutrophils, key players in immunothrombosis, show alterations detectable via cell fluorescence (e.g., NE-SFL), which are strongly correlated with the severity of sepsis and septic DIC. The FLAMES study will evaluate, from the time of admission to the Emergency Department, the relevance of neutrophil fluorescence as an early biomarker of severity, either alone or integrated into an AI model based on the multiparametric data from the SthemA 801, with comparison to the Sysmex XN. It aims to address an unmet clinical need: the immediate stratification of the risk of severe forms of sepsis. Hypothesis: higher initial fluorescence will identify patients at risk of organ failure, septic shock, or DIC.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Admission to the ED
* Suspected or proven infection defined by the prescription of antibiotic therapy within 24 hours following admission to the ED
* Collection of an EDTA blood sample as part of routine care
* Patient informed and has not expressed opposition

Exclusion Criteria:

* Pregnancy or breastfeeding
* Legal protection measure (guardianship, curatorship, judicial protection)

Outcomes:
Evaluate the association between neutrophil fluorescence, whether isolated or integrated into an AI model based on CBC results combined with the advanced multiparametric capabilities of the SthemA 801 analyzer
The Application of Multimodal Artificial Intelligence Systems in Prostate Cancer Diagnosis and Prognosis Analysis
Trial ID: NCT06589154
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-10-10
Completion date: 2025-07-30
Sponsor: Shanghai Changzheng Hospital
Collaborators: Ningbo No. 1 Hospital; The First Affiliated Hospital of Soochow University; The First Affiliated Hospital of Guangzhou Medical University; Jiangsu Provincial People's Hospital; Cancer Institute and Hospital, Chinese Academy of Medical Sciences; Zhongda Hospital; Northern Jiangsu People's Hospital; Changhai Hospital; West China Hospital
Conditions: Healthy People; Benign Prostatic Hyperplasia; Prostate Cancer
Interventions: Multi-modal artificial intelligence model (BEAM)
Locations: Cancer Hospital, Chinese Academy of Medical Sciences; The First Aﬃliated Hospital of Guangzhou Medical University; Jiangsu Provincial People's Hospita; Zhongda Hospital, Southeast University; The First Affiliated Hospital of Soochow University; Northern Jiangsu People's Hospita; Changhai Hospital; Shanghai Changzheng Hospital; West China Hospital, Sichuan University; Ningbo No. 1 Hospita
Countries: China; China; China; China; China; China; China; China; China; China
Enrollment: 1651
Age range: 18 Years - 80 Years
Sex: MALE

Summary:
Prostate-specific antigen (PSA) testing has limited specificity for prostate cancer diagnosis, leading to a high rate of unnecessary biopsies. This multi-center study aims to develop and validate a non-invasive, multi-modal artificial intelligence model that combines cell-free DNA (cfDNA) profiles with multi-parametric MRI (mpMRI). The primary goal is to improve the accuracy of prostate cancer detection and risk stratification, particularly for men with PSA levels in the 4-10 ng/mL "gray zone," thereby providing a robust tool to guide clinical decision-making and reduce avoidable invasive procedures.

Eligibility criteria:
Inclusion Criteria:

* Men aged 18-80 years with a clinical indication for prostate or pelvic magnetic resonance (MR) examination.
* Patients with normal prostate, benign prostatic hyperplasia, or prostate cancer.
* First visit on January 1, 2014, or later.

Exclusion Criteria:

* Diagnosis of any other malignancy within the previous 5 years.
* Prior transurethral resection or enucleation of the prostate before imaging.
* Any condition deemed by the investigator to make the patient unsuitable for study participation.

Outcomes:
Sensitivity of Prostate Cancer Multimodal Model in Predicting Prostate Biopsy Pathology Outcomes (Benign or Malignant); Specificity of Prostate Cancer Multimodal Model in Predicting Prostate Biopsy Pathology Outcomes (Benign or Malignant); ROC value of Prostate Cancer Multimodal Model in Predicting Prostate Biopsy Pathology Outcomes (Benign or Malignant)
Development, Application, and Validation of a Comprehensive Classification Automation System for Allograft Precision Diagnostics in Adult and Pediatric Kidney Transplant Biopsies
Trial ID: NCT05306795
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2016-01-01
Completion date: 2022-12-01
Sponsor: Paris Translational Research Center for Organ Transplantation
Conditions: Kidney Transplant Rejection; Pathology
Locations: Paris Translational Centre for Organ Transplantation
Countries: France
Enrollment: 4000
Age range: None - None
Sex: ALL

Summary:
Since 1991, the Banff classification has been the gold standard for defining antibody-mediated rejection (AMR) and T-cell mediated rejection (TCMR), thereby guiding the treatment and management of transplant recipients. Starting from a pure histological approach, the classification has moved over the past three decades towards an integrated precision diagnosis system, which encompasses other expertise, such as immunology, immunogenetic, other basic sciences, biostatistics, data science, and artificial intelligence The counterpart of this constant refinement is that Banff rules are becoming complex to follow, with numerous possible scenarios leading to a high degree of inter-observer variability and misclassifications, which may lead to therapeutic consequences.

The aims of this study are:

1. To integrate and decode all Banff rules and develop a computer-based application - the Banff Automation System - which provides automated and reproducible diagnoses
2. To validate the ability of the Banff Automation System to reclassify rejection diagnoses in multicenter cohort studies and clinical trials.

Eligibility criteria:
Inclusion Criteria:

* Kidney transplant recipients
* At least one kidney transplant biopsy performed, assessed with the Banff classification

Exclusion Criteria:

* Inadequate biopsy according to the Banff classification (number of glomeruli inferior less than seven)
* Missing data for Banff lesion scores
* Missing data for donor-specific antibody status
* Missing data for C4d (degradation product of the classic complement pathway) staining

Outcomes:
Rejection-related diagnoses reclassified by the computer-based tool
Artificial Intelligence (AI) Support in Medical Emergency Calls - "The AISMEC-study" -Can Artificial Intelligence Improve the Precision in Identifying Acute Stroke in Emergency Medical Calls?
Trial ID: NCT04648449
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-09-01
Completion date: 2026-08-31
Sponsor: Haukeland University Hospital
Collaborators: Helse Vest; Western Norway University of Applied Sciences; Oslo University Hospital; The Norwegian Heart and Lung Patient Organization; Helsetjenestens driftsorganisasjon for nødnett HF (HDO); The Norwegian Stroke Register
Conditions: Stroke, Acute; Apoplexy; Brain; Emergencies; Communication, Multidisciplinary
Interventions: Artificial intelligence on emergency calls
Locations: Haukeland Universitetssykehus, Kirurgisk serviceklinikk, Nasjonalt kompetansesenter for helsetjenestens kommunikasjonsberedskap
Countries: Norway
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
More than 12.000 patients suffer acute stroke in Norway every year, but less than half of them reach hospital within the current treatment window for thrombolysis. Stroke is the third-highest cause of death and the number one cause of severe disability requiring long time care at institutions. Consequently this has a high impact on society, patients and relatives, in addition to high costs related to care estimated to approximately 10 billion NOK per year. Although there are few studies on emergency medical communication centres (EMCC) in Norway, some have shown that the performance of the emergency medical communication centres can be improved. This project will seek to amend EMCC´s handling of acute stroke inquiries using artificial intelligence (AI), thus contributing to getting the patient to hospital in time for optimal treatments.

Eligibility criteria:
Inclusion Criteria:

* All callers to medical emergency number 113 in Bergen

Exclusion Criteria:

* Age \<18

Outcomes:
Stroke recognition in medical emergency calls
RIabilitazione di Pazienti COvid e Loro Monitoraggio IN Casa Con la pIattaforma ARc-IntEllicare - (RICOMINCIARE)
Trial ID: NCT05074771
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-03-26
Completion date: 2022-03-30
Sponsor: Camlin Ltd
Collaborators: Università Politecnica delle Marche
Conditions: Post-COVID19; Parkinson Disease
Interventions: ARC intellicare
Locations: Università Politecnica delle Marche
Countries: Italy
Enrollment: 31
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The RESTART/RICOMINCIARE study is a pilot single-center, not controlled prospective, pre-post intervention study aimed at verifying feasibility and safety of a device-supported home rehabilitation for people suffering from mild to moderate disabilities due to respiratory or neurological conditions, related to Covid-19 or frailty condition (i.e. Parkinson Disease).

Eligibility criteria:
Inclusion Criteria:

A) Post-Covid-19 people: at home post-COVID-19 volunteers who have recovered from SARS-COV-2 infection (healing = two 24-hour consecutive oropharyngeal tampons negative for SARS-COV-2) from at least 15 days B) Frail people who score 9-12 on the Edmonton Frail Scale (EFS) due to neurological lesions of the motor system (e.g., Parkinson's Disease) or respiratory syndrome (e.g., COPD, pulmonary fibrosis)

All subjects have to meet the following criteria:

1. Men and women\> 18 years,
2. Moderate dyspnea in activities of daily living (ADL) (Barthel's dyspnea score ≤55) or walking difficulties or upper limb disabilities due to central or peripheral neurological lesions that, eventually, emerged following Covid-19 or Walking Handicap Scale (Perry and Garrett '95) ≤ 5
3. Informed consent signed

Exclusion Criteria:

1. Fever (TC ≥ 37 ° C)
2. Cough, cold, sore throat, diarrhoea or pneumonia signs and diagnosis
3. People with moderate to severe cognitive impairment (MoCA \<20)
4. Formal rehabilitation performed in the last month
5. Pre-existing disability related to neurodegenerative disorders or severe stroke (TACI); epilepsy, seizures and a history of severe dizziness and falls.
6. Severe non-stabilized comorbidities: oncological diseases in the active phase; NYHA stage IV congestive heart failure; severe respiratory failure requiring cough and support for breathing; life expectancy of less than 6 months.
7. Moderate or severe dependence in activities of daily living for any medical reason (such as the participant's inability to notice or suspect to comply with the protocol procedure) or other reason the investigator believes the participant is ineligible to study (Rankin mod. ≤ 8. Women of childbearing potential who are not using suitable valid methods of contraception

9\. Pregnancy

Outcomes:
Adherence Rate; Device-related adverse events; Attrition rate
Comparison of the Dental X-ray Analysis Performed by an Artificial Intelligence Algorithm and the Analysis Performed by Dentists
Trial ID: NCT06258798
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2024-11-01
Sponsor: Hospital of the Ministry of Interior, Kielce, Poland
Conditions: Oral Health; Dental Caries; Periapical Diseases; Tooth Loss
Interventions: Taking a dental X-ray
Locations: Department of Maxillofacial Surgery
Countries: Poland
Enrollment: 1025
Age range: 11 Years - None
Sex: ALL

Summary:
This cross-sectional study aims to perform a population-based assessment of the incidence of decay, dental fillings, root canal fillings, endodontic lesions, implants, implant and dental abutment crowns, pontic crowns, and missing teeth, taking into account the location.

Eligibility criteria:
Inclusion Criteria:

* Indications for dental X-ray confirmed by a written referral from the dentist or physician (both screening tests and tests performed for treatment purposes were allowed)
* Permanent dentition (after exfoliation is completed)

Exclusion Criteria:

* Patients with mixed dentition (exfoliation has not finished)

Outcomes:
Sensitivity; Specificity; Precision of the AI algorithm