moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Comparison of the SUNRISE© Device With Ventilatory Polygraphy for the Diagnosis of Obstructive Sleep Apnea Hypopnea Syndrome: Collection of Preliminary Data
Trial ID: NCT06460441
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-06-19
Completion date: 2025-02-28
Sponsor: Groupe Hospitalier de la Region de Mulhouse et Sud Alsace
Collaborators: University Hospital, Strasbourg, France
Conditions: Obstructive Sleep Apnea Hypopnea Syndrome
Interventions: SUNRISE© device
Locations: Hôpitaux Universitaires de Strasbourg
Countries: France
Enrollment: 37
Age range: 18 Years - None
Sex: ALL

Summary:
Additional data on screening for obstructive sleep apnea/hypopnea syndrome (OSAHS) in patients with co-morbidities, such as diabetics and those with chronic respiratory diseases would enable current recommendations to evolve, with a view to promoting the widespread use of simplified techniques for OSAHS screening, particularly in patients with cardiorespiratory co-morbidities.

The primary objective of this observational multicentric pilot study is to compare the performance of the Sunrise® medical device with that of ventilatory polygraphy as part of routine screening for OSAHS in diabetic patients followed up in the diabetology department of the Strasbourg University Hospital, and in patients followed up in the pneumology consultation department of Mulhouse Hospital (GHRMSA).

Eligibility criteria:
Inclusion Criteria:

* Patient aged 18 or over;
* Patient with a smartphone (Android or iOS) with Bluetooth and Internet connection and able to use the mobile application associated with the Sunrise© medical device;
* Patient having a scheduled exploration by ventilatory polygraphy;
* Non-opposition to participation in the research.

Exclusion Criteria:

* Patient suffering from a psychiatric pathology or with a history of behavioral disorders;
* Patient already treated for OSAHS;
* Patient refusing to cut his beard (if too abundant to wear the Sunrise© device);
* Patient suffering from a pathology affecting the rotation of the condyle in the temporomandibular joint;
* Patient deprived of liberty by judicial or administrative decision;
* Patients under court protection or legal guardianship.

Outcomes:
Apnea-hypopnea index (AHI)
The Significance of Artificial Intelligence-Guided Psychotherapy of Depression and Anxiety Disorders
Trial ID: NCT07576686
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-07
Completion date: 2026-06-10
Sponsor: Aleksandra Stojanovic
Conditions: Depression - Major Depressive Disorder; Anxiety Disorders
Interventions: AI-Guided Cognitive Behavioral Therapy; Cognitive Behavioral Therapy (CBT)
Locations: University Clinical Center Niš
Countries: Serbia
Enrollment: 120
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to evaluate the effectiveness of an artificial intelligence (AI)-guided cognitive behavioral therapy (CBT) program for the treatment of mild depression and anxiety disorders in adults.

Depression and anxiety disorders are among the most common mental health conditions worldwide and are associated with significant individual and societal burden. Despite the availability of effective treatments, access to psychotherapy remains limited due to insufficient resources and long waiting times. Digital mental health interventions, particularly those supported by artificial intelligence, have the potential to increase accessibility and scalability of evidence-based treatments.

In this controlled clinical trial, participants diagnosed with mild depressive disorder and/or anxiety disorders will be assigned to either an experimental group receiving AI-guided CBT or a control group receiving standard psychiatric care. The intervention will be delivered through a digital platform and will consist of structured weekly sessions over a 10-week period.

The primary objective of the study is to assess changes in symptoms of depression and anxiety. Secondary outcomes include perceived stress, social support, digital therapeutic alliance, and overall clinical improvement.

The findings of this study are expected to contribute to the understanding of the role of AI in psychotherapy and its potential to improve access to mental health care.

Eligibility criteria:
Inclusion Criteria:

Age 18 years or older Diagnosis of mild depressive disorder and/or anxiety disorder confirmed by a structured clinical interview (MINI) Ability to understand and provide informed consent Access to a device capable of using the digital platform (computer, tablet, or smartphone) Willingness to participate in weekly sessions over a 10-week period

Exclusion Criteria:

Moderate to severe depressive disorder Current or past diagnosis of psychotic disorder Bipolar disorder Substance use disorder or dependence Primary diagnosis of antisocial personality disorder Severe cognitive impairment or inability to use digital tools Acute suicidal risk requiring immediate intervention Concurrent participation in another structured psychotherapy program-

Outcomes:
Change in depressive symptom severity; Change in anxiety symptom severity
Research on the Application of Artificial Intelligence Ultrasonic Recognition
Trial ID: NCT05207254
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-12-18
Completion date: 2025-12-30
Sponsor: Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
Conditions: Difficult Airways
Interventions: Ultrasonic test
Locations: Shanghai 9Th Hospital
Countries: China
Enrollment: 4000
Age range: 18 Years - None
Sex: ALL

Summary:
Although there is no related research on the evaluation of difficult airways by ultrasound features based on artificial intelligence, the investigators guess that the evaluation of ultrasound features based on artificial intelligence can make further breakthroughs in difficult airway early warning systems. Therefore, this project intends to use AI technology to extract and analyze the ultrasound features of the subjects, evaluate the correlation between the ultrasound features of the subjects and the occurrence of difficult airways, and construct possible diagnostic models to evaluate AI ultrasound feature recognition in the prediction of difficult airways. The effect and application value of this method are expected to be more intelligent and accurate for early warning of difficult airways in clinical anesthesia.

Eligibility criteria:
Inclusion Criteria:

* ASA classification is 1-3
* Patients who intend to undergo tracheal intubation under general anesthesia
* Age ≥ 18 years old

Exclusion Criteria:

* Patients with speech communication and cooperation barriers;
* Patients with open head and neck trauma
* Patients with cervical spine fractures or cervical spine diseases;
* Emergency surgery;
* Patients who are allergic to related drugs.

Outcomes:
Diagnosed as a difficult airway
A Prospective Observational Study of Artificial Intelligence Morphometric Evaluation of Vertebral Fractures
Trial ID: NCT06449742
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-03-01
Completion date: 2027-03-01
Sponsor: IRCCS San Raffaele
Conditions: Vertebral Fracture; Osteoporosis
Interventions: AI software and automatic 3D reconstruction
Locations: IRCCS Ospedale San Raffaele
Countries: Italy
Enrollment: 250
Age range: 50 Years - None
Sex: MALE

Summary:
The study will be conducted as a monocentric observational prospective study design wants to evaluate the prevalence of vertebral fractures in the cohort of patients that perform a chest-abdomen CT for medical indication other than osteometabolic pathologies.

Eligibility criteria:
Inclusion Criteria:

1. Male subjects
2. Age ≥ 50 years
3. Clinical and medical history data available at abdomen-chest CT evaluation
4. Signature of informed consent to the study

Exclusion Criteria:

1. Hospitalized patients
2. Patients known to have osteo-metabolic diseases.
3. with primary and/or acquired immunodeficiency states, and/or severe impairment of general clinical condition (e.g. metastatic neoplasms; immunosuppressive therapies; worsening/reacute/compensated chronic diseases; moderate-severe renal failure)
4. Patients unable to understand and sign the Informed Consent.

Outcomes:
Number of fractures detected with CT scans trough AI software and 3D reconstruction
Predictive Risk Algorithm for Development of Right Ventricular Pacing Induced Cardiomyopathy - a Step Towards Personalized Pacemaker Lead Deployment
Trial ID: NCT06449079
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-07-30
Completion date: 2026-10-30
Sponsor: Guy's and St Thomas' NHS Foundation Trust
Collaborators: Imperial College Healthcare NHS Trust; King's College Hospital NHS Trust
Conditions: Heart Failure; Pacemaker-Induced Cardiomyopathy; Pacemaker Complication
Interventions: Machine learning
Locations: Guys' and St Thomas' Hospital NHS Trust; Kings' College London Healthcare Trust; Imperial College London Healthcare Trust
Countries: United Kingdom; United Kingdom; United Kingdom
Enrollment: 10000
Age range: 18 Years - None
Sex: ALL

Summary:
Development of pacing induced cardiomyopathy (PICM) is correlated to a high morbidity as signified by an increase in heart failure admissions and mortality. At present a lack of data leads to a failure to identify patients who are at risk of PICM and would benefit from pre-selection to physiological pacing. In the light of the foregoing, there is an urgent need for novel non-invasive detection techniques which would aid risk stratification, offer a better understanding of the prevalence and incidence of PICM in individuals with pacing devices and the contribution of additional risk factors.

Eligibility criteria:
Inclusion Criteria:

* All patients who received a pacing device (VVI, DDD, ICD, leadless pacemaker) from the GSTT/RBH/KCH/ICH database in the last 10 years (from 01/01/2014)
* All patients who are \>18 years old.
* Male and Female

Exclusion Criteria:

* Patients who did not receive a pacing device (VVI, DDD, ICD, leadless pacemaker)
* All patients \<18 years old
* Patients with congenital heart disease
* Patients who have received artificial heart valves or underwent cardiac bypass surgery
* Patients who did not have an echocardiogram after receiving a pacing device

Outcomes:
Primary aim
Relationship Between Muscle and Fat Mass Using Deep-learning Approach in Preoperative Computed Tomography and a Neuromuscular Blocking Agent Dose Required for Maintaining Deep Neuromuscular Blockade in Patients Undergoing Gynecological Surgery
Trial ID: NCT07478120
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-01-01
Completion date: 2026-03-01
Sponsor: Seoul National University Bundang Hospital
Conditions: Neuromuscular Blockade; Neuromuscular Blockade Monitoring
Interventions: Neuromuscular block
Locations: Seoul National University Bundang Hospital
Countries: South Korea
Enrollment: 31
Age range: 19 Years - 85 Years
Sex: ALL

Summary:
Deep neuromuscular blockade has benefits in various surgical procedures and reduces postoperative pain. While neuromuscular blockade dosages are often based on the patient's actual body weight, body composition can vary significantly depending on age, gender, and individual exercise levels. Therefore, uniformly estimating neuromuscular blockade dosages based on actual body weight can result in under- or over-administered neuromuscular blocking agents. Calculating neuromuscular blockade dosage based on actual body weight can be inaccurate, but it remains due to the difficulty of accurately quantifying human muscle and fat mass. Recent advances in image analysis techniques utilizing artificial intelligence models have led to the development of methods for quantifying muscle and fat mass from computed tomography (CT). Theoretically, a higher neuromuscular blockade dosage should be required as the muscle mass increases. This study aims to analyze the relationship between neuromuscular blocking agent dosage, onset time, profound blockade time, and recovery time, and muscle mass and fat mass calculated from preoperative CT scans.

Eligibility criteria:
Inclusion Criteria:

* American Society of Anesthesiologists physical class (ASA class) 1-3
* Patients scheduled for elective gynecologic surgery under general anesthesia
* Patients undergoing abdominal computed tomography for routine preoperative preparation

Exclusion Criteria:

* Pregnant women
* Decreased renal function (GFR\<60mL/min/1.73m2).
* Patients taking drugs that affect deep neuromuscular blockade
* Patients with a history of neuromuscular and muscle disorders

Outcomes:
muscle and fat mass
Artificial Intelligence vs Physicians for Breast Cancer Patients' Information : a Blind, Randomized, Controlled Study
Trial ID: NCT03556813
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-11-01
Completion date: 2019-01-20
Sponsor: Wefight
Conditions: Breast Cancer
Interventions: survey
Locations: Wefight
Countries: France
Enrollment: 150
Age range: 18 Years - None
Sex: FEMALE

Summary:
Chatbots (contraction of "chat" and "robot") are a computer software program that use statistical learning and aim to simulate a conversation by text or voice message. The use of these chatbots was evaluated as part of the increase in therapeutic adherence and information on well-being and mental health.

In clinical studies, chatbots are useful in gathering information through questionnaires directly submitted during conversations or through keyword analyzes. There is no longer any delay between the moment the patient notices a side effect, for example, and the moment when the patient announces it during a consultation. The responses given by the chatbot do not constitute medical advice and only provide information on the treatments envisaged or in progress in the context of the management of breast cancer.

The main objective of the study is to show that the chatbot Vik's answers to the common questions asked by breast cancer patients about their therapeutic management are at least as satisfactory as answers given by a committee of multidisciplinary doctors

Eligibility criteria:
Inclusion Criteria:

* over 18 years
* paitents with breast cancer

Exclusion Criteria:

* Patient unable to formulate his opposition
* Patient with cancer other than breast cancer

Outcomes:
Quality of Patient information
A Randomized Controlled Trial Evaluating an Innovative HIV Self-testing Service With Online Real-time Instruction, Pre-test and Post-test Counselling Provided by a Fully-automated Artificial Intelligence Chatbot (HIVST-Chatbot)
Trial ID: NCT05796622
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-16
Completion date: 2024-02-28
Sponsor: Chinese University of Hong Kong
Conditions: Behaviors, Health
Interventions: HIVST-Chatbot (HIV self-testing services with counseling provided by a Chatbot); HIVST-OIC (HIV self-testing services with counseling provided by administrators)
Locations: Centre for Health Behaviours Research, the Chinese University of Hong Kong
Countries: Hong Kong
Enrollment: 564
Age range: 18 Years - None
Sex: MALE

Summary:
A parallel-group non-inferiority randomized controlled trial will then be conducted to evaluate the performance of HIVST-Chatbot. A total of 528 Hong Kong Chinese speaking men who have sex with men (MSM) aged ≥18 years who have access to live-chat applications are recruited from multiple sources. Those who are diagnosed as HIV positive are excluded. Participants are 1:1 randomized into the intervention group (n=264) or the control group (n=264). The research team will implement the HIVST-Chatbot (HIVST services with real-time counselling provided by a fully-automated artificial intelligence Chatbot) in the intervention group and HIVST-OIC (HIVST with real-time counselling provided by trained administrators) in the control group. Participants completed two telephone surveys at baseline and six months afterwards.

Eligibility criteria:
Inclusion Criteria:

* Hong Kong Chinese-speaking men aged at least 18 years old
* Having had anal intercourse with at least one man in the past six months
* Willing to leave contact information and be followed up at Month 6
* Having access to the online live-chat application (WhatsApp)
* Not intending to leave Hong Kong for one month consecutively within the next six months

Exclusion Criteria:

* Self-reported as HIV positive

Outcomes:
Uptake of HIV self-testing; Proportion of HIV self-testing users receiving counselling along with testing
A Prospective Clinical Study on the Real-world Diagnostic Effectiveness of Artificial Intelligence Algorithm in Diabetic Retinopathy Screening
Trial ID: NCT03911323
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2018-10-01
Completion date: 2020-10-01
Sponsor: Shenzhen Second People's Hospital
Conditions: Diabetic Retinopathy
Interventions: diabetic retinopathy
Locations: Shenzhen second peoples's hospital
Countries: China
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Recently, artificial intelligence algorithm has made great progress in the prediction of diabetic retinopathy based on fundus images，showing very high sensitivity and specificity. However，the real-world diagnosis effectiveness of deep learning model is still unclear.

This study is designed to evaluate the clinical efficacy of such an algorithm in detecting referable diabetic retinopathy.

Eligibility criteria:
Inclusion Criteria:

1. Subject must understand the study, participate voluntarily, and has signed informed consent
2. Age 18 or older, no limitations on gender identity
3. Patients with type 1 or type 2 diabetes.

Exclusion Criteria:

1. Subjects diagnosed with eye diseases other than diabetic retinopathy
2. Subjects diagnosed with macular edema, severe non-proliferative retinopathy, proliferative retinopathy, radioactive retinopathy or retinal vein obstruction.
3. Pregnant woman, subjects with mydriatic allergy, unclear refractive medium, family history of glaucoma, or diagnosed as narrow angle
4. Subjects with a history of laser therapy, retinal surgery or anti-vascular endothelial growth factor injection
5. Subjects currently participating in another ophthalmic research, receiving ophthalmic research products.
6. Subject who is photo-sensitivity or taking medication that causes photosensitivity
7. Subjects received photodynamic therapy recently

Outcomes:
Sensitivity and specificity
Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUH)
Trial ID: NCT05032651
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-03-19
Completion date: 2023-12-23
Sponsor: Kaohsiung Medical University Chung-Ho Memorial Hospital
Collaborators: National Sun Yat-sen University
Conditions: End Stage Renal Disease on Dialysis
Interventions: novel artificial intelligence assisted platform
Locations: Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUH)
Countries: Taiwan
Enrollment: 130
Age range: 20 Years - None
Sex: ALL

Summary:
It has been known well for a long time that End stage renal disease (ESRD) patients usually need ESA to maintain their hemoglobin (Hb) to improve both mortality as well as quality of life. Later, several large-scale RCTs showing normalization of Hb in this population increased the risk of thromboembolic event, which leaded current guidelines to recommend the therapeutic goal for anemia in ESRD should be within a range (usually between 10-12 gm/dl) rather than above or below a certain level (or value). In addition to ESA dose, many factors contribute to the severity of anemia in this population, such as iron status, chronic blood loss, adequacy of dialysis, chronic inflammation, renal wasting, et al. To put all these factors together, maintaining the Hb levels within the target level is a challenge to physician. It was reported that only one third of Hb within the target at any given time. Our data showed, for those under maintenance hemodialysis and without any blood transfusion in observation period, near two third of patients' Hb level were within the target range.

Eligibility criteria:
Inclusion Criteria:

1. signed informed consent
2. Age older than 20 year old.
3. End stage renal disease under regular hemodialysis with three times per week and duration of each session at least 4 hours
4. Having at least consecutive 6 months data of both Hb and biochemical study during dialysis before enrollment and at least one Hb level within the range of 11-12 g/dl
5. Having at least one prescription of ESA supplement to keep Hb within the range of 10-12 g/dl in the past 6 months before the enrollment
6. Having received ESA of the same brand at least 6 months before the enrollment

Exclusion Criteria:

1. Ever receiving blood transfusion in the past 12 months
2. Active bleeding with blood loss more than 250cc in 3 months before the enrollment
3. Active infection or malignancy
4. Study subject can not follow with the study protocol

End of Study

The eligible subject will not be allowed to continue the study once informed consent is withdrawn or event happening meets the following criteria:

Outcomes:
hemoglobin (Hb) levels
Development and Fine-tuning of a Medical-grade Wearable Device with Interpretable Artificial Intelligence System for the Obstructive Sleep Apnea-hypopnea Syndrome (OSAHS) Screening
Trial ID: NCT06474351
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-06-01
Completion date: 2023-12-31
Sponsor: PranaQ Pte. Ltd.
Conditions: Obstructive Sleep Apnea
Locations: Taipei Veterans General Hospital
Countries: Taiwan
Enrollment: 240
Age range: 20 Years - None
Sex: ALL

Summary:
The clinical study aims to develop and fine-tune the TipTraQ system, created by PranaQ, for home sleep apnea screening. The system comprises a wearable device and a cloud-based AI for estimating Total Sleep Time (TST) and Apnea-Hypopnea Index (AHI). This open-label, non-randomized study will recruit 240 subjects aged 20 and older from the Veteran General Hospital, Taipei.

Eligibility criteria:
Inclusion Criteria:

Subjects from the age of 20 and older that have an indication for an in-lab PSG study. The subject should be able to understand the study and sign the informed consent before being enrolled in the study. During the study period, the subject can withdraw from the study any time.

Exclusion Criteria:

Subjects with the following medical conditions will be excluded from this study:

1. Heart transplant
2. Heart failure, New Youk Heart Association (NYHA) classification III or IV
3. Chronic opioid medication user Devastating Severe strokes, with the modified Rankin score (mRS)≥4
4. Tracheostomy
5. Incapable of comprehending and signing the informed consent and questionnaires, including but not limited to subjects with severe Alzheimer's, unconscious by head trauma, or of someone with limited mental capacity
6. Cannot correctly follow the order to use the TipTraQ device

Outcomes:
Apnea Hypopnea Index(AHI); Oxygen Desaturation Index(ODI); Total Sleep Time(TST)
Application of Artificial Intelligence to Construct Whole-person Precise Smart Medicine and Precise Health Care Program in Patients With Myasthenia Gravis
Trial ID: NCT06879210
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-19
Completion date: 2026-07-19
Sponsor: Yi Ting Yeh
Conditions: Myasthenia Gravis
Interventions: Mobile device.(phone)
Locations: Yi Ting Yeh
Countries: Taiwan
Enrollment: 10
Age range: 20 Years - None
Sex: ALL

Summary:
This study aims to propose an intelligent health robot that utilizes remote intelligent robots to assist patients with disease-related issues, enabling them to address their problems promptly.

Eligibility criteria:
Inclusion Criteria:

1. Patients diagnosed with myasthenia gravis by a physician.
2. Age 20 years and above.
3. Able to use a smartphone.

Exclusion Criteria:

1. Individuals not diagnosed with myasthenia gravis.
2. Individuals who do not use smartphones or tablets.
3. Individuals who withdraw from the group midway.
4. Individuals unwilling to participate in interviews.

Outcomes:
Can the use of a mobile device [Disease Assistance Consultation] enhance the understanding of disease care and increase knowledge related to diseases in traditional Chinese medicine?