moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Nursing Students&Amp;#39; Readiness for Medical Artificial Intelligence and Artificial Intelligence Anxiety: A Mixed Method Study
Trial ID: NCT06623448
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-07-01
Completion date: 2024-09-01
Sponsor: Necmettin Erbakan University
Conditions: Nursing Students
Locations: Necmettin Erbakan University
Countries: Turkey (Türkiye)
Enrollment: 358
Age range: None - None
Sex: ALL

Summary:
The aim of this observational study is to examine nursing students\&#39; readiness for medical AI and their anxiety about AI using a mixed method.

The main question it aims to answer is:

Nursing students\&#39;;

1. What is their level of awareness of medical artificial intelligence?
2. What is their level of artificial intelligence anxiety?
3. Is there a relationship between artificial intelligence awareness and anxiety levels?
4. What are the opinions of those who have artificial intelligence anxiety about artificial intelligence?

In the 1st stage, nursing students answered the related survey questions online. In the 2nd stage, qualitative interviews were conducted with those with high AI anxiety scores and evaluated.

Eligibility criteria:
Inclusion Criteria:

To be a student of Necmettin Erbakan University Faculty of Nursing To be a student of Necmettin Erbakan University Kamil Akkanat Faculty of Health Sciences, Department of Nursing

Exclusion Criteria:

Students who did not consent to participate were excluded

Outcomes:
Artificial intelligence awareness level and artificial intelligence anxiety leve
Use of Natural Language Processing (NLP) and Machine Learning (ML) for the Identification of Patients With Crohn's Disease (CD) and Complex Perianal Fistulas (CPF) and Their Characterization in Terms of Clinical and Demographic Characteristics. A Multicentre, Retrospective, NLP Based Study
Trial ID: NCT04844593
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-03-08
Completion date: 2024-04-29
Sponsor: Takeda
Conditions: Crohn Disease; Rectal Fistula
Locations: Hospital Universitario Son Espases; Hospital del Mar; Hospital Universitario Fundacion Alcorcon
Countries: Spain; Spain; Spain
Enrollment: 32
Age range: 18 Years - None
Sex: ALL

Summary:
Natural Language Processing and machine learning are examples of artificial intelligence tools. This study will check if these tools correctly identify people with Crohn's disease with complex perianal fistulas from their medical records.

Eligibility criteria:
Inclusion Criteria:

1\. CD participant diagnosed or not with CPF between January 1st 2015 and December 31st 2021.

Exclusion Criteria:

Not applicable.

Outcomes:
Percentage of Participants With CD and CPF Accurately Identified With the use of NLP and Medical Language (MEL)
Clinical Validation of an Artificial Intelligence Tool to Predict Inversion Time
Trial ID: NCT06855238
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-11
Completion date: 2024-12-20
Sponsor: Istituto Auxologico Italiano
Conditions: Cardiovascular Diseases
Interventions: THAITI software
Locations: Istituto Auxologico Italiano IRCCS
Countries: Italy
Enrollment: 60
Age range: None - None
Sex: ALL

Summary:
Introduction: Inversion-recovery (IR) magnetic resonance (MR) sequences are commonly used to perform late-gadolinium enhancement (LGE) imaging during cardiac magnetic resonance (CMR) scans. Inversion Time (TI), i.e. the time between the 180° inverting pulse and the 90°-pulse, must be manually input to obtain optimal myocardium nulling. Determinants of this value are patient's, sequence, and contrast characteristics, and the time after contrast injection. The identification of the correct TI is pivotal to quality images. The determination of TI is mostly based on experience, and it can be challenging in some diseases and for less experienced operators.

Aim of this study is to test in a clinical setting an Artificial Intelligence (AI) tool, which we developed to automatically predict TI in CMR post-contrast IR LGE sequences, named "THAITI".

THAITI performance will be evaluated in terms of 1) quality of images obtained using the AI-predicted TI with a 4-point Likert scale; 2) quality of images obtained using the AI-predicted TI in terms of Contrast-Enhancement ratio, i.e. the signal intensity of enhanced/remote myocardium in CMR-LGE images; 3) numbers of images that need to be reacquired; 4) average time duration of CMR-LGE imaging.

Eligibility criteria:
Inclusion Criteria:

* Patients in whom CMR-LGE is performed for a clinical reason
* Mixed cardiac conditions (including cardiomyopathies, ischemic heart disease, normal scans, focal and diffuse myocardial pathological processes)
* Both sexes
* Any age
* Availability of serum creatinine, measured within one month prior to CMR
* Provision of the written informed consent

Exclusion Criteria:

* Non-contrast CMR
* First-pass perfusion stress-CMR
* Absolute contraindication to CMR
* Inadequate overall image quality

Outcomes:
Images quality proportion
Risk Prediction of Severe Radiation-induced Oral Mucositis in Locally Advanced Nasopharyngeal Carcinoma
Trial ID: NCT05858385
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-09-22
Completion date: 2024-12-31
Sponsor: Affiliated Cancer Hospital & Institute of Guangzhou Medical University
Conditions: Nasopharyngeal Carcinoma by AJCC V8 Stage; Radiation Induced Mucositis
Interventions: Intervention based on key factors identified by the severe RIOM prediction model
Locations: Yu Zeng
Countries: China
Enrollment: 700
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
Exploring effective risk prediction models for severe Radiation-Induced Oral Mucositis (RIOM/RTOM), providing a research basis for mitigating oral radiation toxicity, and effectively improving the sensitivity of dentists in predicting the risk of severe RIOM in locally advanced nasopharyngeal carcinoma patients.Based on precise radiotherapy, it is proposed to extract OAR using the contour of local oral areas. Explore more accurate RIOM dose-response relationships.Exploring a new type of fusion classifier, by complementing the information between each base classifier, helps to maximize the utilization of the information contained in different factors to build a more objective, reliable, and efficient multi criteria decision-making based risk prediction model for severe RIOM. It use predictive models to identify key risk factors for severe RIOM and further validate the effectiveness of this risk factor in reducing the risk of severe RIOM on risk factors for severe RIOM identified by the predictive mode.

Eligibility criteria:
Inclusion Criteria:

* Confirmed as nasopharyngeal carcinoma through pathological tissue biopsy, AJCC 8th edition bed staging is I-Iva stage, with no distant metastasis
* First time receiving radical radiation therapy and receiving RTOM observation and treatment throughout the entire process in the Department of Stomatology
* Complete information on anti-tumor treatment materials
* No oral mucosal diseases that have not been effectively controlled in the past or still require long-term medication treatment
* Other diseases that do not affect the treatment of nasopharyngeal carcinoma

Exclusion Criteria:

* Failure to complete radiotherapy, or material release time delayed by more than two weeks compared to plan
* There are other diseases that affect the examination and treatment of oral mucosal inflammation, such as restricted mouth opening, Schegren's syndrome, etc
* Expected survival time less than 6 months
* Cases where patients withdraw from clinical trials at any time and for any reason

Outcomes:
Incidence of severe RIOM
Breast Point-of-Care Ultrasound Examination Trial
Trial ID: NCT06932133
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-07
Completion date: 2027-02-12
Sponsor: Region Skane
Conditions: Breast Cancer
Interventions: Point-of-care ultrasound
Locations: Unilabs Mammography Unit, Skane University Hospital
Countries: Sweden
Enrollment: 600
Age range: 18 Years - None
Sex: FEMALE

Summary:
The high cost of diagnostic equipment, limited expertise, and inadequate infrastructure are major barriers to early breast cancer diagnosis in low- and middle-income countries. Point-of-care ultrasound (POCUS) offers a relatively low-cost, portable solution that, when combined with artificial intelligence (AI)-driven image analysis, has the potential to significantly expand access to breast assessment in these settings. The purpose of this study is to evaluate the performance of POCUS for women with focal breast symptoms and to assess the performance of AI to analyze POCUS images. The study will be divided in two parts: a prospective interventional study and a retrospective multicase multireader study.

Eligibility criteria:
Inclusion Criteria:

* Women (≥18 years of age) referred to diagnostic imaging with a suspicion on malignancy

Exclusion Criteria:

* Individuals unable to comprehend the study information due to language barriers or cognitive impairments.

Outcomes:
The area under the receiver operating characteristic curve (AUC) for the intervention, compared to that of the comparator
Validation of the Utility of an Artificial System for the Large-scale Screening of Scoliosis Using Back Images
Trial ID: NCT03773458
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-06-01
Completion date: 2018-07-30
Sponsor: Sun Yat-sen University
Conditions: Orthopedic Disorder of Spine; Artificial Intelligence; Scoliosis
Interventions: An artificial system for the screening of scoliosis
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen University
Countries: China
Enrollment: 500
Age range: 10 Years - 22 Years

Summary:
Traditional school scoliosis screening approaches remains debatable due to unnecessary referal and excessive cost. Deep learning algorithms have proven to be powerful tools for the detection of multiple diseases; however, the application of such methods in scoliosis screening requires further assessment and validation. Here, the investigators develop an artificial system for the automated screening of scoliosis using disrobed back images, and conduct clinical trial to validate if the diagnostic system can offsetting the shortcomings of human doctors.

Eligibility criteria:
Inclusion Criteria:

* 1.Patients included both pretreatment back photos and whole spine (C7-S1) standing X-ray or ultrasound images (for healthy population); 2. All the documents are clear to be recognized by naked eyes; 3. Back photos and are taken at the same time (not \>1month); 4.Patients were consider as idiopathic scoliosis according to clinical photos.

Exclusion Criteria:

* 1\. Patients were considered as non-idiopathic scoliosis for obvious abnormal features of trunck,such as Cafe-au-Lait spots for neurofibromatosis, Spider finger, Abnormal hair spot of back, pelvic tilt, lower limb discrepancy and so on; 2.The taken time between back photo and X-ray or ultrasound was more than 1month; 3.The clinical photos and images were not clear; 4. The X-ray film or ultrasound images not including whole spine (C7-S1).

Outcomes:
The proportion of accurate, mistaken and miss detection of the intelligent visual acuity diagnostic system.
Accuracy Of Dental Caries Detection From Intraoral Images Using Different Artificial Intelligence Models Versus Conventional Visual Examination Among A Group Of Children: A Diagnostic Accuracy Study
Trial ID: NCT06749743
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-04-30
Completion date: 2025-12-30
Sponsor: Cairo University
Conditions: Dental Caries (Diagnosis); Artifical Intelligence; Intraoral Images
Interventions: FASTER RCNN
Locations: Cairo university
Countries: Egypt
Enrollment: 398
Age range: 4 Years - 12 Years
Sex: ALL

Summary:
The goal of this observational study is to evaluate the diagnostic accuracy of different deep learning models in detecting dental caries from intra oral images taken by a professional intra oral camera in children. The main question it aims to answer is:

What is the diagnostic accuracy of different deep learning models in detecting dental caries from intra oral images taken by a professional intra oral camera in children compared to the conventional clinical visual examination?

Eligibility criteria:
Inclusion Criteria:

* Child dentition having at least one decayed tooth.

Exclusion Criteria:

* Child dentition with developmental enamel defects.
* Children with any systemic medical condition.
* Parent / child refuse to participate in the study.
* Uncooperative child.

Outcomes:
Accuracy Of Dental Caries Detection From Intraoral Images Using Different Artificial Intelligence Models Versus Conventional Visual Examination Among A Group Of Children: A Diagnostic Accuracy Study
Application of Artificial Intelligence in Early Detection of Eye Complications in Diabetics: A Randomized Clustered Trial in Hail, Saudi Arabia
Trial ID: NCT05655117
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01
Completion date: 2023-07
Sponsor: The New Model of Care, Hail Health Cluster
Collaborators: Health Holding Company, Hail Health Cluster
Conditions: Artificial Intelegence; Diabetic Retinopathy Associated With Type 2 Diabetes Mellitus; Macular Edema Due to Type 2 Diabetes Mellitus
Interventions: AI based eye screening
Enrollment: 440
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
The goal of this pragmatic trial is to test the benefit of using artificial intelligence-based eye screening i.e, a fundus camera device in the early detection of eye complications in diabetics. The main questions it aims to answer are:

To what extent does the application of artificial intelligence-based eye care at primary care clinics work well in achieving early detection of eye complications such as macular oedema? To what extent does the application of artificial intelligence-based eye care at primary care clinics work well in achieving early detection of eye complications such as retinopathy? Participants will be asked to participate in the screening for eye complications at primary care centres, and a fundus camera will be used for screening.

Researchers will compare the proportion of detected cases with early signs of eye complication among those using artificial intelligence-based eye screening i.e., fundus camera, to the proportion of detected cases among those using routine eye care clinics at the primary care centre.

Early detection of eye complications in diabetics prevents the risk of blindness.

Eligibility criteria:
Inclusion Criteria:

* Diabetic patients aged 18-90

Exclusion Criteria:

* Severely ill patient or patient with cancer

Outcomes:
The detection rate of diabetic retinopathy in the intervention group vs. control group; The detection rate of macular oedema in the intervention group vs. control group.
One-stop Evaluation of Hypertrophic Cardiomyopathy in Cardiac Magnetic Resonance
Trial ID: NCT07336290
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2010-01-01
Completion date: 2030-12-30
Sponsor: Minjie Lu
Conditions: Hypertrophic Cardiomyopathy (HCM)
Enrollment: 10000
Age range: None - None
Sex: ALL

Summary:
This is a multi-center, observational study aiming to establish a precision diagnosis scheme and multi-dimensional risk prediction models for Hypertrophic Cardiomyopathy (HCM). The study plans to enroll 10000 adult HCM patients. It will integrate genetic testing, advanced cardiac imaging (echocardiography and cardiac magnetic resonance), and biomarker analysis to develop early diagnosis, differential diagnosis, and new risk prediction models for sudden cardiac death (SCD) and heart failure in HCM patients. Participants will undergo baseline assessments and be followed up every six months for up to 3 years to track clinical outcomes. This study is non-interventional and does not involve any investigational drugs or devices.

Eligibility criteria:
Inclusion Criteria:

A maximal left ventricular wall thickness of ≥ 12 mm in any segment at end-diastole, as measured by echocardiography or Cardiac Magnetic Resonance (CMR)

Exclusion Criteria:

* Presence of any contraindication to CMR examination.
* Unwillingness to participate in the study or to provide informed consent.

Outcomes:
Number of participants with sudden cardiac death
Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence
Trial ID: NCT07328997
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-05-31
Completion date: 2025-11-30
Sponsor: Shanghai Zhongshan Hospital
Conditions: ARDS (Acute Respiratory Distress Syndrome); AI (Artificial Intelligence)
Interventions: CT scan
Locations: Department of critical care medicine, Zhongshan Hospital, Fudan University
Countries: China
Enrollment: 400
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
By using multi-center chest CT data, an intelligent assessment model for the severity of ARDS was constructed. Based on CT quantitative features and clinical characteristics, a prediction model for short-term critical events (such as mechanical ventilation decisions, prone position strategies, death, ECMO use, etc.) was established. The disease was staged and quantified, and a diagnosis and risk stratification model for ARDS was developed to assist in guiding the diagnosis and treatment strategies for ARDS.

Eligibility criteria:
Inclusion Criteria:

* Meets the diagnostic criteria for ARDS
* Be admitted to the intensive care unit
* There are chest CT images

Exclusion Criteria:

* Age less than 18 years old
* Missing medical records
* No chest CT images

Outcomes:
Accuracy of ARDS severity classification; Treatment plan matching rate between model-recommended and actual clinical management.; Accuracy of 28-day in-hospital mortality prediction.
Navigation and Artificial Intelligence Technology for Indigenous Virtual Education on Smoking Cessation (NAITIVE)
Trial ID: NCT06697496
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06-01
Completion date: 2030-10-31
Sponsor: Fred Hutchinson Cancer Center
Collaborators: National Institute on Minority Health and Health Disparities (NIMHD)
Conditions: Cigarette Smoking-Related Carcinoma
Interventions: QuitBot Smoking Cessation Chatbot Program; QuitBot Smoking Cessation Text Messaging Program; Survey Administration; Interview; Carbon Monoxide Measurement
Locations: Fred Hutch/University of Washington Cancer Consortium
Countries: United States
Enrollment: 772
Age range: 18 Years - None
Sex: ALL

Summary:
This clinical trial develops a chatbot smartphone application (app), QuitBot, and text messaging to help American Indians (AI) and Alaska Natives (AN) to quit smoking commercial tobacco (smoking cessation), and evaluates two remote smoking cessation programs to see how well they work for helping AI/AN people quit smoking commercial tobacco. AI/AN populations have one of the highest rates of commercial cigarette smoking of any racial and ethnic group in the United States (US). They also have a higher rate of developing smoking-related cancer but are less likely to quit smoking. The two programs are designed to provide personalized support in setting a smoking cessation goal, tasks to reach the smoking cessation goal, and motivation to remain smoke-free. This may help to keep participants engaged and support them in their quit efforts, and may improve smoking cessation among AI and AN.

Eligibility criteria:
Inclusion Criteria:

* Self-identifying as American Indian or Alaska Native, either alone or in combination with other races
* Age 18 and older
* Smoking combustible cigarettes daily in the past year
* Interest in quitting smoking
* Interest in learning skills to quit smoking
* Willing to be randomly assigned
* Have daily access to their own Android or iPhone smartphone
* Ability to download a smartphone app
* Ability to read English
* Not currently (i.e., within past 30 days) using other smoking cessation interventions
* No prior participation in our studies
* No prior use of SFT
* No household or family member participating
* US residency for the next twelve months
* Willingness to complete follow-up assessments at the 3-, 6-, and 12-month follow-ups
* Providing email, phone number(s), and mailing address

Exclusion Criteria:

* The reverse of the inclusion criteria
* Pregnant or breastfeeding
* Use of other tobacco products (e.g., ceremonial use of tobacco, e-cigarettes) will be assessed but is not an exclusion criterion, as it would limit the study's generalizability

Outcomes:
30-day point prevalence abstinence (PPA)
Playing and Singing for the Recovering Brain: Efficacy of Enriched Social-Motivational Musical Interventions in Stroke Rehabilitation
Trial ID: NCT04507542
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-09-01
Completion date: 2022-07-01
Sponsor: University of Barcelona
Collaborators: Artificial Intelligence Research Institute, Spanish National Research Council; University of Helsinki
Conditions: Stroke
Interventions: Enriched Music-Supported Therapy; Graded Repetitive Arm Supplementary Program
Locations: Bellvitge Biomedical Research Institute
Countries: Spain
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
Music-Supported Therapy (MST) is a rehabilitation technique to improve the upper extremity motor function of stroke patients through playing musical instruments. A modified version of the MST protocol has been created (hereafter, referred as enriched MST, eMST) to include (i) a home-based self-training program using an app for electronic tablets and (ii) weekly group sessions of musical playing strengthening the motivational and emotional components of music playing. A randomised controlled trial will be conducted to test the effectiveness of this enriched MST (eMST) protocol in improving motor functions, cognition, emotional well-being and quality of life when compared to a program of home-based exercises utilizing the Graded Repetitive Arm Supplementary Program (GRASP). Sixty stroke patients will be recruited and randomly allocated to an eMST group (n=30) or a control GRASP intervention group (n=30). Patients will be evaluated before and after a 10-week intervention, as well as at 3-month follow-up. The primary outcome of the study is the functionality of the paretic upper limb measured with the Action Research Arm Test. Secondary outcomes include other motor and cognitive functions, emotional well-being and quality of life measures as well as self-regulation and self-efficacy outcomes. We hypothesize that patients treated with eMST will show larger improvements in their motor and cognitive functions, emotional well-being and quality of life than patients treated with a home-based GRASP intervention.

Eligibility criteria:
Inclusion criteria:

* Presence of mild-to-moderate paresis of the upper extremity after a stroke (having a score between 1 and 4 in the Medical Research Council Scale for Muscle Strength at the distal muscles of the upper extremity);
* More than 6 months post-stroke;
* Completion of formal rehabilitation programs.

Exclusion criteria:

* Major language or cognitive deficits affecting comprehension (Mini-Mental State Examination \< 24);
* Neurological or psychiatric co-morbidity;
* Other musculoskeletal condition affecting upper extremity motor function (e.g. fracture or arthritis).

Outcomes:
Change in Action Research Arm Test; Change in Action Research Arm Test