moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Identification of Vocal Biomarkers to Monitor the Health of People With a Chronic Disease
Trial ID: NCT04848623
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-06-26
Completion date: 2031-05-01
Sponsor: Luxembourg Institute of Health
Conditions: Chronic Disease
Locations: Luxembourg Institute of Health
Countries: Luxembourg
Enrollment: 50000
Age range: 15 Years - None
Sex: ALL

Summary:
The CoLive Voice research project aims to identify vocal biomarkers of severe conditions and frequent health symptoms. The project is based on digital technologies and statistical algorithms. This is an international anonymous survey where vocal recordings are collected simultaneously with large validated clinical and epidemiological data, in the context of various chronic diseases or frequent health symptoms in the general population.

Eligibility criteria:
Inclusion Criteria:

* Adolescents and adults \> 15 years
* With or without health conditions
* From all countries

Exclusion Criteria:

* Children \< 15 years

Outcomes:
Stress
The Intervention Effect of Generative Artificial Intelligence (Emohaa) and Individual Psychological Counseling on Emotional Distress in Young Adults: A Randomized Controlled Trial
Trial ID: NCT06992180
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-20
Completion date: 2025-09-10
Sponsor: Central University of Finance and Economics, China
Collaborators: Beijing Normal University; China Agricultural University; Qinghai University
Conditions: Depression, Anxiety
Interventions: Generative Artificial Intelligence Intervention; Individual Psychological Counseling
Enrollment: 222
Age range: 15 Years - 30 Years
Sex: ALL

Summary:
This randomized controlled trial aims to evaluate the effectiveness of an AI-based mental health chatbot, Emohaa, in reducing symptoms of depression, anxiety, insomnia, and academic burnout among young adults experiencing emotional distress. A total of 222 participants will be randomly assigned to one of three groups: (1) AI chatbot intervention, (2) traditional counseling, or (3) waitlist control. The study spans six time points from baseline through one-month follow-up. Primary and secondary outcomes will be measured via validated self-report questionnaires. The intervention is delivered entirely online through a WeChat-integrated platform, ensuring high accessibility and scalability.

Eligibility criteria:
Inclusion Criteria:

* Age ranges from 15 to 30
* Suffering from emotional distress (PHQ-9 \> 14 or GAD-7 \> 12)
* Ability to use the mobile phone to interact with AI
* Consent to participate in the study

Exclusion Criteria:

* High risk of self-injury and suicide
* Presence of psychotic symptoms (i.e., delusions)
* Serious substance use problems
* Other mental or physical illnesses requiring urgent medical assistance
* Received other psychological or pharmacological interventions in the last month.

Outcomes:
Depression; Anxiety
Integrative Mindfulness-Based Predictive Approach for Chronic Low Back Pain Treatment
Trial ID: NCT06106191
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-25
Completion date: 2029-12
Sponsor: Boston Medical Center
Collaborators: National Institute of Neurological Disorders and Stroke (NINDS); Worcester Polytechnic Institute
Conditions: Chronic Low-back Pain
Interventions: Mindfulness meditation; Perspectives on pain; Discussion
Locations: Boston Medical Center
Countries: United States
Enrollment: 350
Age range: 18 Years - None
Sex: ALL

Summary:
This study will identify unique signatures that people have which can cause pain by evaluating biological, psychological, and social markers using artificial intelligence. These markers can be used to accurately predict the response of diverse individuals with chronic low back pain (cLBP) to Mindfulness-Based Stress Reduction. This will help enhance clinician decision-making and the targeted treatment of chronic pain.

The overall objective is to use a unique machine learning (ML) approach to determine the biomarker signature of persons undergoing mindfulness based stress reduction (MBSR) treatment for their chronic low back pain (cLBP). This signature will facilitate clinical prediction and monitoring of patient response to MBSR treatment.

The design of the study is a single-arm clinical trial of the evidence-based MBSR program for patients with cLBP.

Eligibility criteria:
Inclusion Criteria:

* Chronic low back pain, which is pain that persists for at least 3-months and has resulted in pain on at least half the days in the past 6 months
* Willing and able to provide online or telephone informed consent
* Speak English as the intervention manual is currently written in English

Exclusion Criteria:

* Red flags- recent (past month) worsening of pain, unexplained fever, unexplained weight loss
* Pregnancy
* Metastatic cancer
* Not a patient at a participating clinic or persons not planning to continue as a patient at a participating clinic for 6 or more months

Outcomes:
Pain, Enjoyment, General activity (PEG) composite score
IIDEAS Intelligent-C Endoscopy Module for Real-time Detection of Colonic Lesions - a Prospective, Single Blinded, Non - Randomized, Single - Center Study
Trial ID: NCT05784935
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-03-20
Completion date: 2023-11-01
Sponsor: Asian Institute of Gastroenterology, India
Conditions: Artificial Intelligence; Colonic Polyp
Interventions: Real time AI detection model
Locations: AIG Hospitals
Countries: India
Enrollment: 380
Age range: 19 Years - 80 Years
Sex: ALL

Summary:
To conduct an single blinded, non-randomized, prospective, single center trial to validate the performance of a novel state-of-the-art Artificial Intelligence model (AI-Model) for colorectal lesion detection during routine diagnostic colonoscopy and to evaluate its feasibility in daily endoscopy. Consecutive patients referred for a screening, surveillance or diagnostic colonoscopy will be included

Eligibility criteria:
Inclusion Criteria:

1. Patients undergoing colonoscopy of age 18 to 80 years.
2. Patient who has read the informed consent form, has understood the relevant aspects of the study, and grants his/her authorization to participate by signing the informed consent form before the inclusion in the study and the performance of any procedure
3. Consecutive adult patients scheduled to undergo routine colonoscopy at AIG

Exclusion Criteria:

1. Females who are pregnant
2. Patients who are unsuitable for any other reason to participate in the study in the opinion of the investigator
3. Recent colonoscopy within past 12 month
4. History of inflammatory bowel disease
5. History of colorectal cancer
6. Previous bowel resection (apart from appendectomy)
7. Peutz-Jeghers syndrome, familial adenomatous polyposis or other polyposis syndromes
8. Bleeding tendency or severe comorbid illnesses for which polypectomy is considered unsafe.

Outcomes:
Performance of a novel state-of-the-art Artificial Intelligence model (AI-Model) for colorectal lesion detection during routine diagnostic colonoscopy
Acanthamoeba and Artificial Intelligence: Single-center Retrospective Observational Study
Trial ID: NCT06332703
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-05
Completion date: 2025-04
Sponsor: IRCCS Ospedale San Raffaele
Conditions: Acanthamoeba Keratitis; Artificial Intelligence
Enrollment: 151
Age range: 1 Year - 99 Years
Sex: ALL

Summary:
Acanthamoeba keratitis, caused by the pathogen Acanthamoeba spp, is recognized worldwide as a severe ocular infection that can pose potential risks to vision.

This observational retrospective and single-center study, of exploratory nature, aims to determine the possibility of identifying patterns that may be useful for future rapid diagnosis of Acanthamoeba keratitis from confocal images, leveraging the normality of corneal examination and the high specificity and sensitivity of computational models.

The data will be based on patients who have been confirmed positive through laboratory tests with proven effectiveness in detecting the infection.

The laboratory tests considered for the division of patients into their respective groups are bacterial examination, PCR examination, and culture examination.

Patients were divided into two groups, the first comprising patients positive for Acanthamoeba infection, while the second comprised patients negative for Acanthamoeba but positive for other pathogens. The study will last for 18 months.

The cohort under study includes 151 patients from the IRCCS San Raffaele Hospital who underwent the aforementioned examinations, of which 76 cases will be included in the group of patients positive for Acanthamoeba and 75 in the group of controls positive for other pathogens.

The confocal images of this cohort will be fed into artificial intelligence software. To evaluate the model, the test set will be used, and the AI model's ability will be assessed using the most commonly used metrics in the field of computer vision such as accuracy, specificity, sensitivity, and f1-score; culminating in a comprehensive evaluation of the model.

Eligibility criteria:
Inclusion Criteria:

* Performed corneal scraping and subsequent bacterioscopic exam, PCR and bacterial colture analysis between 2004 and 2023.
* Patients positivity to corneal infection.

Exclusion Criteria:

\- Patients negativity to aforementioned exams.

Outcomes:
Determination of the potential presence of significant patterns of Acanthamoeba infection in in-vivo confocal microscopy (IVCM) images.
Artificial Intelligence-based Prediction of Radio-cephalic Arteriovenous Fistula Maturation Using Preoperative Duplex Examination
Trial ID: NCT06600750
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-01-01
Completion date: 2023-12-31
Sponsor: Seoul National University Hospital
Conditions: Renal Insufficiency, Chronic; Arteriovenous Fistula; Artificial Intelligence (AI); Machine Learning
Interventions: Arteriovenous fistula
Locations: Seoul National University Hospital
Countries: South Korea
Enrollment: 494
Age range: None - None
Sex: ALL

Summary:
The goal of this observational study is to assess the efficacy of AI-driven models in analyzing comprehensive ultrasonographic variables across multiple forearm locations to predict successful AVF maturation. The main question it aims to answer is:

Can AI-driven models analyzing comprehensive ultrasonographic variables accurately predict the successful maturation of arteriovenous fistulas (AVFs)?

Participants who underwent radiocephalic arteriovenous fistula (AVF) creation had their preoperative ultrasonographic data analyzed using AI-driven models to predict successful AVF maturation over a four-year retrospective period.

Eligibility criteria:
Inclusion Criteria:

* patients who underwent RCAVF due to advanced chronic kidney disease from 2018 to 2022

Exclusion Criteria:

* Patients who did not have follow-up data available

Outcomes:
Maturation of the fistula
Development and Validation of an AI-Based Erythema Measurement System for Psoriasis Lesions Using Smartphone Imaging With Color Card Compared to Standard Color Calibration Device
Trial ID: NCT07494396
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-20
Completion date: 2027-04-20
Sponsor: Khon Kaen University
Conditions: Psoriasis
Interventions: No additional drugs(control Group)
Locations: Khon Kaen University
Countries: Thailand
Enrollment: 50
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
Psoriasis is a common chronic inflammatory skin disease. Disease severity is commonly assessed using the Psoriasis Area and Severity Index (PASI), in which erythema is graded subjectively on a 0-4 scale. This visual assessment is prone to significant inter- and intra-rater variability.

Although objective tools such as colorimeters provide accurate erythema measurement, their high cost limits routine clinical use. Smartphone imaging combined with artificial intelligence (AI) offers a practical alternative for objective assessment. However, variability in lighting conditions can affect image consistency. Incorporating a color calibration card enables accurate color normalization.

This study aims to develop and validate an AI-based system for measuring erythema in psoriatic lesions using smartphone images with a color card, compared against a standard colorimeter to assess validity and reliability.

Eligibility criteria:
Inclusion Criteria:

* Adults aged ≥18 years
* Diagnosed with psoriasis by a dermatologist
* Presence of at least five clearly photographable lesions
* Willing and able to take photographs using a smartphone with both a standard color calibration card and a study-specific color card at home weekly, and submit images via Line or email
* Willing to have clinical photographs taken at baseline and at a follow-up visit
* Provides written informed consent to participate in the study

Exclusion Criteria:

* Presence of skin infection or other lesions that may interfere with color assessment (e.g., tattoos)
* Use of self-tanning products or substances that alter skin color
* Inability to perform photography or comply with study procedures

Outcomes:
To evaluate the accuracy of erythema measurements of psoriatic lesions obtained from an AI-based erythema model (smartphone imaging with a color calibration card) compared with a standard color calibration device (gold standard).
HAAL: HeAlthy Ageing Eco-system for peopLe With Dementia
Trial ID: NCT06307197
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-02
Completion date: 2024-04-15
Sponsor: Istituto Nazionale di Ricovero e Cura per Anziani
Conditions: Dementia; Devices; Artificial Intelligence; Older People; Caregiver Burden
Interventions: HAAL platform
Locations: IRCCS INRCA Hospital
Countries: Italy
Enrollment: 10
Age range: 65 Years - None
Sex: ALL

Summary:
HAAL project aims to test several technological devices in order to improve the quality of life of older people with dementia and their informal and formal caregiver.

Eligibility criteria:
Inclusion Criteria:

* being the caregiver of a person with cognitive impairment or dementia;
* availability of time to participate;
* visit the assisted person at least two times at week or live with him/her.

Exclusion Criteria:

* Lack of written informed consent

Outcomes:
Care load of caregivers
A Non-Randomized Clinical Trial to Investigate the Efficacy of the Morley Medical Sepsis (MMS) Software Device in the Prediction and Early Detection of Sepsis
Trial ID: NCT04606862
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-10-31
Completion date: 2020-12-31
Sponsor: Morley Medical
Collaborators: Morley Research Consortium
Conditions: Sepsis
Interventions: Morley Medical Sepsis Software Device
Enrollment: 10000
Age range: 18 Years - None
Sex: ALL

Summary:
This is a pivotal medical device clinical trial evaluating the clinical outcomes in hospitalized patients monitored with the Morley Medical Sepsis Software Device. The device uses unique AI machine learning algorithms to analyze patient data in real time and generate clinical decision support sepsis risk predictions for clinicians.

Eligibility criteria:
Inclusion Criteria:

* Male or female patient 18 years of age or older
* Patient is admitted or had been admitted to a participating healthcare facility

Exclusion Criteria:

* Sepsis diagnosis present on admission
* Involvement in a clinical trial of another investigational product with similar purpose

Outcomes:
In-hospital sepsis prevalence; In-hospital sepsis related 30-day mortality
Mazankowski - Echo Go Discovery Protocol Retrospective LVEF/GLS Comparison
Trial ID: NCT04877899
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-10-08
Completion date: 2021-11-30
Sponsor: Ultromics Ltd
Collaborators: Mazankowski Alberta Heart Institute
Conditions: Cardiotoxicity; Cancer
Interventions: EchoGo
Locations: Mazankowski Alberta Heart Institute
Countries: Canada
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to compare conventionally acquired Left Ventricle Ejection Fraction (LVEF) and Global Longitudinal Strain (GLS) data to Artificial Intelligence (AI) driven automated processing of 2 dimensional contrast and 2 dimensional non-contrast resting transthoracic echocardiograms for application in the assessment of patients undergoing chemotherapy with cardiotoxic drugs. This is a single-centre retrospective study which utilizes echocardiographic DICOM image and meta-data datasets received from a Canadian site. Data processed using the AI driven automated processing will be compared to conventionally acquired LVEF and GLS measurements and results will be analysed to determine accuracy and precision.

Eligibility criteria:
Inclusion Criteria:

* Normal EF and no regional wall motion abnormalities prior to starting chemotherapy treatment.
* Follow-up EF measurements available for at least 1 year during the treatment period.
* Follow-up EF measurements for at least 1 year during the treatment period.

Exclusion Criteria:

* Age \< 18 years
* Inadequate image quality (as determined by the Ultromics Operators Quality Control process)

Outcomes:
Compare the performance of automated EF and GLS measurements in resting transthoracic echocardiograms against conventional measurement acquisition.; Compare the performance of automated EF and GLS measurements in resting transthoracic echocardiograms against conventional measurement acquisition.; Compare the performance of automated EF and GLS measurements in resting transthoracic echocardiograms with and without the application of contrast agents.; Compare the performance of automated EF and GLS measurements in resting transthoracic echocardiograms with and without the application of contrast agents.
Studio Del Milieu Steroideo in Pazienti Obesi
Trial ID: NCT06341699
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-05-16
Completion date: 2026-06-30
Sponsor: Istituto Auxologico Italiano
Collaborators: Ministry of Health, Italy
Conditions: Obesity, Morbid
Locations: Istituto Auxlogico Italiano, Hospital San Giuseppe, Piancavallo
Countries: Italy
Enrollment: 180
Age range: 18 Years - 80 Years
Sex: MALE

Summary:
The aim of this observational study is to evaluate the actual secretion of cortisol and testosterone in obese male subjects, together with the receptor sensitivity to these hormones in order to understand whether the hormonal milieu deriving from these parameters is associated with alterations in bone metabolism, lipoprotein concentration and function, and/or the severity and complications of obesity.

It will be also investigated if this hormonal milieu is a predictive factor for cardiovascular disease in obese patients.

Eligible subjects are male patients (age 18-80 years) with severe obesity and no other known causes of hypercortisolism or hypogonadism.

Questionnaires for the evaluation of mood and symptoms will be collected upon enrollment, patient's anamnestic and clinical data relating to disease complications, BMI, previous blood tests, cortisol suppression with dexamethasone 1 mg will be collected, and blood sample will be sent to and analyzed at a centralized laboratory for the study of Steroid hormones, bone metabolism, lipoprotein function, genetics and receptor analysis.

Eligibility criteria:
Inclusion Criteria:

* morbid obesity

Exclusion Criteria:

* patients suffering from severe systemic pathologies, fever, chronic inflammatory states known to cause PCR \>10 mg/dL
* use of corticosteroids at the time of enrollment
* the patient's poor understanding of spoken and written Italian
* patients suffering from primary pathologies of the testicle, known at enrollment
* patients suffering from hypothalamic-pituitary or adrenal pathologies already known at the time of enrollment

Outcomes:
Association of androgen milieu with the severity of obesity; Association of cortisol milieu with the severity of obesity; Algorithm fo CV risk in obesity
A Decision Support System Based on Classification Algorithms for the Diagnosis of Periodontal Disease
Trial ID: NCT06071338
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-10-01
Completion date: 2024-12-30
Sponsor: Ministry of Health, Saudi Arabia
Conditions: Periodontal Diseases
Interventions: Clinical Periodontal Examination
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
The study purposes For periodontal applications, such as diagnosing gingivitis and periodontal disease, artificial intelligence (AI) models have been developed; however, their accuracy and technological maturity are to be evolved. The applications of such technologies in the field of periodontics are walking baby steps worldwide. The Kingdom of Saudi Arabia is moving fast in technology adoption and implementation in different sectors. However, the healthcare sector, especially clinical-related, needs original research applied to Saudi subjects. The literature in the field of machine learning applications in dentistry is limited. Although AI models for periodontology applications are still being developed, they could serve as potent diagnostic instruments. The current study was planned to add to the current gap in the literature.

Eligibility criteria:
Inclusion Criteria:

* age 18 or more
* no periodontal treatment has been done at least 6 months prior to the enrollment
* seeking periodontal treatment

Exclusion Criteria:

* refuse to volunteer in the study

Outcomes:
Age; Gender; Marital statue; Education; Physical Activities; Bleeding on Probing; Gingival Index; Plaque Index; Periodontal Probing Depth; Recession Type; Clinical Attachment Loss; Radiographic Bone Loss; Tooth Mobility; Missing Teeth; Furcation Involvement; Diabetes; Smoking; Body Mass Index; Cardiovascular Disease
Multicenter Prospectively Collected Registry of Images and Videos of Colonic Lesions to Develop Artificial Intelligence to Predict Submucosal Invasion
Trial ID: NCT07401264
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-04-07
Completion date: 2030-12
Sponsor: Universitaire Ziekenhuizen KU Leuven
Conditions: Colonic Lesions
Locations: UZ Leuven
Countries: Belgium
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
It concerns a study in which we wish to collect images and corresponding histological information (microscopic characteristics) in a prospective manner in various European and non-European centers (multicentric). This means that the collection of images is new and happens in the context of a procedure that has not yet been carried out. The relevant procedure will involve a standard colonoscopy during which we wish to collect digital images and videos of large colorectal polyps (colorectal lesions). These images will later be used for the development of a new computer software (artificial intelligence) that is able to predict the histology (microscopic characteristics) and extent of invasion in the intestinal wall (submucosal invasion) all by itself. The latter is one of the goals in the European project ECOPOP in which the sponsor is involved.

Eligibility criteria:
Inclusion Criteria:

* Referral for standard or therapeutic colonoscopy
* Digital video material of standard colonoscopy containing at least one colorectal polyp (≥ 10mm) and/or with suspicion of malignancy/submucosal invasion
* Digital images of colorectal polyps ≥ 10mm made during standard colonoscopy and/or with suspicion of malignancy/submucosal invasion Page 11 of 21
* Videos and/or images can be made in white light or any virtual of dye-based enhancement technique
* Colonoscopies performed after IRB approval for this particular study.
* Material collected in adult patients of all sex or race, including pregnant women.

Exclusion Criteria:

* Any contraindication to undergo a standard colonoscopy
* Any uncontrolled coagulopathy or bleeding disorder
* Colonoscopy videos not containing any or colorectal polyps \< 10mm
* Colonoscopy videos or images of low quality due to unstable imaging, stool remnants, bowel or patient movements or blurry vision
* Colonoscopy videos with visualization of the polyp less than 5 seconds due to unstable positioning, passing stool remnants, bowel or patient movements or blurry visions

Outcomes:
registry of images of colonic lesions