moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Development and Validation of a Large Language Model-based Myopia Assistant System
Trial ID: NCT06607822
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-09-21
Completion date: 2024-10-26
Sponsor: The Hong Kong Polytechnic University
Conditions: Myopia; Large Language Model
Interventions: A patient-centered assistant system based on Large-Language Model (LLM)
Locations: The Hong Kong Polytechnic University
Countries: China
Enrollment: 70
Age range: 6 Years - 75 Years
Sex: ALL

Summary:
Myopia is a rapidly growing global health concern, and there is an urgent need for advanced tools that can facilitate personalized healthcare strategies. Artificial intelligence (AI)-based solutions, such as large language models, offer robust tools for ophthalmic healthcare. In this study, investigators aim to validate a patient-centered Large Language Model (LLM)-based Myopia Assistant System with the following key objectives: 1) evaluate the ability of the LLM models to generate high-level reports and help self-evaluation of myopia for patients in primary care; 2) evaluate its performance in answering evidence-based medicine-oriented questions and improving overall satisfaction within clinics for myopic patients.

Eligibility criteria:
Inclusion Criteria:

1. Outpatient participants aged 6 to 75.
2. Participants who undergo ophthalmic examinations for medical purposes.
3. Participants who can produce clear ophthalmic images in both eyes.
4. No prior experience in research involving digital medicine
5. Agree to participate in this study with written informed consent

Exclusion Criteria:

1. Participants who are reluctant to participate in this study
2. Participants who are unable to understand the study.
3. Participants who have recently undergone ocular surgery or those with severe ocular conditions that may affect the interpretation of imaging results related to myopia evaluation (e.g., severe vitreous hemorrhage, cataracts, corneal leukoma, etc.) will be excluded from the study.
4. Participants with poor quality of ophthalmic images, including blurriness, artifacts, underexposure, or overexposure.
5. Other unsuitable reasons determined by the evaluators.

Outcomes:
Satisfaction level
High Frequency Intraoral Ultrasound Probe for Early Diagnosis of Periodontal Diseases
Trial ID: NCT05809427
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-07-07
Completion date: 2024-07-05
Sponsor: University Hospital, Tours
Conditions: Periodontal Diseases
Interventions: Periodontal probing
Locations: University hospital of Tours
Countries: France
Enrollment: 39
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical investigation is evaluation the agreement between the periodontal pocket depth measurement obtained by periodontal probing (gold standard) and the measurement obtained by the ultrasound device

Eligibility criteria:
Patient Inclusion Criteria :

Age ≥ 18 years Affiliated to a social security scheme Free, informed and signed consent Ability for participant to comply with the requirements of the study

Exclusion Criteria (patient):

Surgical procedure performed in the area to be scanned Osteosynthesis material Under legal protection Pregnant women, breastfeeding Teeth Inclusion Criteria Minimum of 10 teeth One tooth from each sextant Presence of at least 1/3 of pathological sites (\>4mm and/or deep sites \>6mm)

Outcomes:
Periodontal pocket depth measurement by manual probing and measurement by ultrasound probe
Randomized Double-blind Controlled Exploratory Clinical Study to Evaluate the Efficacy and Safety of FB1006 in the Treatment of ALS Patients
Trial ID: NCT05923905
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: PHASE4
Start date: 2023-01-18
Completion date: 2027-09-30
Sponsor: Peking University Third Hospital
Conditions: Sporadic and Familial Amyotrophic Lateral Sclerosis
Interventions: FB1006; Placebo
Locations: Peking University Third Hospital
Countries: China
Enrollment: 64
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This is a randomized double-blind controlled exploratory clinical study to evaluate the efficacy and safety of FB1006 in the treatment of amyotrophic lateral sclerosis (ALS) patients.

Eligibility criteria:
Inclusion Criteria:

1. World Federation of Neurology modified El Ecorial criteria for diagnosis of patients with laboratory support probable, clinically probable, or definite sporadic and familial amyotrophic lateral sclerosis (ALS)
2. Age 18 to 80 years old
3. ALS duration no longer than 18 months(from day of onset)
4. Patient 's ALSFRS-R total scored ≥27，Each single item is scored at least 2（dyspnoea, orthopnea and respiratory insufficiency ≥3）
5. Forced vital capacity (FVC%) no less than 70% of predicted normal for gender, height and age
6. According to brain function AI analysis in accordance with depressive EEG characteristics
7. Women and men of childbearing potential should use medically acceptable contraception
8. Voluntarily participate, and sign an informed consent form

Exclusion Criteria:

1. Patients with dementia or severe neurological, psychiatric or systemic disease that is poorly controlled or may interfere with the conduct of the trial or the results of the trial
2. Pregnant women and lactating women
3. Suicide attempt or attempted suicide
4. Combined with other neurological diseases similar to ALS symptoms, or affecting the evaluation of drug efficacy, such as cervical spondylotic myelopathy, lumbar spondylosis, dementia, etc.
5. Patients with history of spinal surgery after ALS onset
6. ALT or AST \> 2 times ULN，creatinine clearance \< 60 mL/min/1.73m2 (MDRD)
7. Patients who are allergic to the investigational product
8. Having participated in other clinical studies within 3 months before randomization
9. Patients that the investigator considers unsuitable for participation in the study

Outcomes:
ROADS (Rasch-Built Overall Amyotrophic Lateral Sclerosis Disability Scale) Score
A Phase II Study to Evaluate Accuracy of ThermalytixTM in Detecting Breast Cancer
Trial ID: NCT05650086
Status: TERMINATED
Study type: OBSERVATIONAL
Start date: 2022-11-15
Completion date: 2023-06-30
Sponsor: University of Arizona
Conditions: Breast Cancer
Interventions: Thermalytix
Locations: University of Arizona Cancer Center
Countries: United States
Enrollment: 115
Age range: 21 Years - None
Sex: ALL

Summary:
This trial is to study a new breast imaging tool called Thermalytix™. ThermalytixTM is a new radiation-free, automated breast cancer screening technique that uses Artificial Intelligence (AI) over thermal images. Thermal images are heat signatures in our body. This new technique will capture heat signatures in the breast and analyze those images with AI software. This study will evaluate the performance of ThermalytixTM breast imaging against standard imaging modalities, such as mammography and ultrasound.

Eligibility criteria:
Inclusion Criteria:

* Be capable of understanding the investigational nature of the study and all pertinent aspects of the study
* Be capable of signing and providing written consent in accordance with institutional and federal guidelines
* Be willing and able to comply with scheduled visits, treatment plan, and follow up with research staff
* Age ≥ 21 years
* Cohort specific criteria
* Cohort 1: Be scheduled for biopsy
* Cohort 2: Had a normal screening mammogram within 3 months of study enrollment
* Cohort 3: Be scheduled for diagnostic mammogram
* Cohort 4: History of breast cancer, s/p lumpectomy and be scheduled for routine mammogram

Exclusion Criteria:

* Cohort specific criteria
* Cohort 1: Previous biopsies in the same or opposite breast within 3 months of study enrollment, prior history of breast cancer, prior history of breast surgeries including implants
* Cohort 2: prior history of breast surgeries including implants
* Cohort 3: Already underwent a biopsy in the same or opposite breast within 3 months of study enrollment, prior history of breast cancer, prior history of breast surgeries including implants
* Cohort 4: surgery \<1 year, h/o mastectomy with reconstruction
* Unable to complete study related procedures

Outcomes:
Sensitivity and specificity of ThermalytixTM in detecting breast cancer
Data Driven Behavior Intervention and Medical Outcome Evaluation of Patients' Comprehensive Monitoring
Trial ID: NCT04966754
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-08-01
Completion date: 2023-11-30
Sponsor: Peking Union Medical College Hospital
Collaborators: Peking University; Guangdong Provincial People's Hospital; Guangzhou Huyun Hospital Management Co., Ltd
Conditions: Metabolic Syndrome; Metabolic Disturbance; Metabolic Complication; Infection; Obesity; Diabetes
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
During the study, the resuscitation room, general wards and ICU were installed fixed or mobile acquisition devices in the resuscitation room, the emergency department of Peking Union Medical College Hospital, and the collection platform was set up. Patients with acute diseases (infection, diabetes complications, etc.) caused by metabolic syndrome (obesity, diabetes, etc.) were selected after informed consent. All medical intervention behaviors, relevant medical records and medical outcome records within the collection scope of the device platform were collected prospectively . And regular follow-up, guidance of patients with metabolic syndrome control, while collecting all the lifestyle characteristics of patients, some patients with metabolic cabin research, and observe the relevant medical outcomes. After that, all the collected data were coded, and the influence of all lifestyle and medical behavior interventions on patients' medical outcomes was studied by artificial intelligence method.

Eligibility criteria:
Inclusion Criteria:

* Diabetes and / or obesity (BMI\>=28).
* Emergency room / emergency ward / EICU;
* Be able to understand and agree the research protocol and sign the informed consent

Exclusion Criteria:

* Pregnant women / lactation period;
* The estimated survival time was less than 1 year;
* Long term follow-up is not guaranteed;
* Failure to comply with the protocol or procedure;
* The collection of medical behavior data can not complete the de privacy or label after de privacy;
* Other researchers think that it is not suitable for inclusion

Outcomes:
Rate of metabolic complications; Complication control; Rate of mortality
Artificial Intelligence in Radiation Oncology for CNS Tumors
Trial ID: NCT06036394
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-09-13
Completion date: 2028-09
Sponsor: Tata Memorial Centre
Collaborators: Bhabha Atomic Research Centre
Conditions: CNS Tumor
Interventions: The images (CT, MRI, PET) used for RT planning, mid-treatment imaging as part of IGRT or disease evaluation, and response assessment/ surveillance post-RT
Locations: Tata Memorial Hospital
Countries: India
Enrollment: 6000
Age range: 1 Year - None
Sex: ALL

Summary:
Radiotherapy involves the use of high-energy X-rays, which can be used to stop the growth of tumor cells. Radiotherapy constitutes an essential avenue in the treatment of brain tumors. The modern techniques of radiotherapy involve radiation planning techniques guided by computer algorithms aimed to deliver high doses of radiation to the areas of brain with tumors and limit the doses to surrounding normal structures. Artificial intelligence uses advanced analytical processes aided by computational analysis, which can be undertaken on the medical images, and radiation planning process. We plan to use artificial intelligence techniques to automatically delineate areas of the brain with tumor and other normal structures as identified from images. Also, we will use artificial intelligence on the radiation dose images and other images done for radiation treatment to classify tumors with good or bad prognoses, identify patients developing radiation complications, and detect responses after treatment.

Eligibility criteria:
Inclusion Criteria:

• Patients with CNS tumors treated with radiation in TMC between January 2010 and December 2022.

Exclusion Criteria:

* RT treatment outside TMC.
* Radiation planning not done in the treatment planning system (treated using clinical marking/ conventional simulator).

Outcomes:
Autosegmentation of organs at risk and target volumes.
Evaluation of Anthropometric and Ultrasonographic Measurements With Different Machine Learning Methods in Predicting Difficult Intubation: A Prospective Observational Study
Trial ID: NCT06904586
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-03-01
Completion date: 2025-01-31
Sponsor: Duzce University
Conditions: Difficult Endotracheal Intubation; Artificial Intelligence
Interventions: Thyromental distance; Neck circumference; Mouth opening distance; Distance from jawbone to hyoid bone with neck in neutral position; Distance from jawbone to hyoid bone with neck in extension; Distance between skin and trachea; Distance between skin and epiglottis; Distance between skin and anterior commissure of vocal cord:; Distance between skin and hyoid bone; Maximum Tongue Thickness
Locations: Duzce University
Countries: Turkey (Türkiye)
Enrollment: 329
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
The assessment and management of difficult airway is of critical importance. Unsuccessful airway management leads to serious mortality and morbidity. From the beginning of the pre-anesthesia examination, 3% to 13% of patients who are considered suitable for routine airway management may be difficult to intubate. Airway assessment issues include risk assessment and airway examination (bedside and forward) to estimate the risk of difficult airway or aspiration. Airway examination aims to determine the presence of upper airway pathologies or anatomical anomalies. Some physical characteristics are associated with difficult airways and unsuccessful intubation. Examples of these are; limited neck movement, snoring, short sternomental distance, neck circumference thickness, etc. Physical characteristics can be measured with a meter or more detailed upper airway ultrasonographic measurements. In this study, researchers aimed to evaluate the anthropometric and ultrasonographic measurement values of patients who underwent preoperative airway assessment and to see the predictability of difficult intubation with artificial intelligence-supported decision support programs.

Eligibility criteria:
Inclusion Criteria:

* Patients over 18 years of age
* Patients who will undergo general anesthesia

Exclusion Criteria:

* Pregnant women
* Those with congenital and/or acquired facial deformities
* Patients who have previously undergone upper neck airway surgery
* Patients with head and neck tumors
* Patients who will undergo thyroidectomy

Outcomes:
Support Vector Machine Algorithm Percentage of Accuracy in Predicted Difficult Intubations
Determining the Validity of ThinkSono Guidance for Ultrasound Image Acquisition and Remote Detection
Trial ID: NCT06652568
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-11-08
Completion date: 2025-12-08
Sponsor: ThinkSono, Ltd.
Collaborators: NYU Langone Health; Temple Health; Allegheny Health Network; University of Wisconsin, Madison; South Texas Veterans Health Care System
Conditions: Deep Vein Thrombosis; Deep Vein Thrombosis, Pulmonary Embolus
Interventions: ThinkSono System
Locations: NYU Langone Health; Temple Health; Allegheny Health Network; South Texas Veterans Health System; University of Wisconsin-Madison
Countries: United States; United States; United States; United States; United States
Enrollment: 634
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to confirm the safety and efficacy of ThinkSono Guidance, a software data collection and communication tool designed to collect ultrasound data to help detect blood clots in veins. The ThinkSono system is CE Mark approved in the European Union and in clinical use in Europe. Usually, when an ultrasound is conducted to detect blood clots in veins, a sonographer (trained technologist who conducts ultrasounds) and/or radiologist will conduct the procedure, including a compression ultrasound exam, and the scan may require a bulky cart and ultrasound equipment. ThinkSono Guidance is a mobile software application that enables other healthcare professionals such as nurses, non-radiologist physicians including general practitioners, and other allied healthcare professionals to perform the ultrasound at the point of care using guidance from the software app. This is a multi-site non-randomized, double-blinded, prospective cohort pivotal study.

Eligibility criteria:
Inclusion Criteria:

* The participant is willing to provide written informed consent to participate in this research.
* The participant is over the age of 18
* The participant has symptoms suggestive of a deep venous thrombosis (DVT)
* The diagnostic DVT algorithm indicates that an ultrasound is needed

Exclusion Criteria:

* Patient consent not given or retracted during the study.
* Local imaging specialists fail to scan the patient or fail to produce a conclusive imaging diagnosis.
* Incomplete ThinkSono Guidance scan due to logistical or other issues such as pain, lack of patient cooperation, barriers such as a cast or other physical limitations.
* Pregnant women
* Patients who undergo a local imaging scan and initiation of new treatment for DVT (e.g. lytic agents, therapeutic anticoagulation, or intervention after the local imaging scan) before a ThinkSono scan can be performed.

Outcomes:
Image Quality Adequacy; Triage Sensitivity; Triage Specificity; Prioritization Specificity
Perception of Health Promotion Messages
Trial ID: NCT07042789
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-24
Completion date: 2024-11-20
Sponsor: The University of Queensland
Conditions: Perception of Vaping
Interventions: Material source: AI generated ads vs existing ads by health agency; source labelling conditions: control condition, AI condition, health agency condition, combined condition
Locations: National Centre for Youth Substance Use Research
Countries: Australia
Enrollment: 763
Age range: 16 Years - 25 Years
Sex: ALL

Summary:
The specific aims of this study are:

1. To compare the perceived message effectiveness (PME) of AI-generated vaping prevention materials with existing traditionally generated (typically by a health authority) prevention materials in a sample of young people. This aim addresses the gap in understanding whether AI can match or exceed the effectiveness of traditional prevention materials in changing youth perceptions and intentions of vaping.
2. To examine the impact of ad labelling (AI-generated, AI-generated with health authority endorsement, and unlabelled) on the PME of vaping prevention messages. Given the novelty of AI in this context, understanding the influence of transparency about message origin and endorsements on message reception is critical.

Eligibility criteria:
Inclusion Criteria:

* Living in Australia
* Aged 16-25 years
* Able to read English
* Committed to reviewing each advertisement and providing honest, personal responses

Exclusion Criteria:

* Unwilling to commit to reviewing each advertisement honestly
* Unable to promise to provide genuine responses to survey questions

Outcomes:
Perceived Message Effectiveness (PME)
HYPER-AI-SCAN: HYPER-acute AI-assisted Sonographic Cerebral Hemorrhage Assessment Network
Trial ID: NCT07319013
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-03-14
Completion date: 2027-06
Sponsor: Hospital Universitari Vall d'Hebron Research Institute
Conditions: Stroke; Intracerebral Hemorrhage; Intracerebral Hemorrhage Basal Ganglia; Intracerebral Haemorrhage
Locations: Hospital Universitario Vall D'Hebron
Countries: Spain
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to evaluate whether transcranial Doppler ultrasound, combined with artificial intelligence (AI), can help identify intracerebral haemorrhage (ICH) in people with acute stroke (both men and women, adults of all ages) within 48 hours of symptom onset.

The main questions it aims to answer are:

Is it feasible to perform standardized protocol transcranial ultrasound in acute stroke patients? Can AI models trained on ultrasound images accurately distinguish haemorrhagic stroke ("ICH suspected") from non-haemorrhagic stroke? There is no comparison group, because all participants will undergo both CT (as standard care) and ultrasound (research imaging), and the AI models will compare their ultrasound-based predictions against CT-confirmed diagnoses.

Participants will:

undergo a non-invasive transcranial ultrasound scan after CT confirms the type of stroke allow researchers to collect coded ultrasound images for AI model training provide clinical and imaging information (already collected as part of routine care) to help evaluate factors related to diagnostic accuracy No treatments or changes to clinical care will be introduced as part of the study.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (age ≥18 years).
* Clinical diagnosis of acute stroke (ischemic or intracerebral hemorrhage).
* Able to undergo transtemporal transcranial ultrasound according to the standardized protocol (no clinical instability)
* Informed consent obtained from the patient or legally authorized representative, per local regulations.

Exclusion Criteria:

* Infratentorial hemorrhage (e.g., cerebellar or brainstem hemorrhage), due to limitations of transtemporal insonation.
* Isolated subarachnoid hemorrhage without parenchymal involvement.
* Hemodynamic instability or medical conditions requiring immediate life-saving intervention that preclude safe ultrasound recording.
* Known skull defects or prior craniectomy on the side required for contralateral insonation.
* Any condition that, in the opinion of the investigators, would interfere with protocol adherence or data accuracy.

Outcomes:
Feasibility of standardized transcranial ultrasound acquisition in acute stroke