moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Knowledge, Attitude, and Perceptions Towards Artificial Intelligence Among Dental Students of Faculty of Dentistry, Tanta University, Egypt
Trial ID: NCT06496152
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-08-01
Completion date: 2024-09-02
Sponsor: Tanta University
Conditions: Artificial Intelligence
Interventions: questionnaire
Enrollment: 371
Age range: 18 Years - 24 Years
Sex: ALL

Summary:
The aim of this study will be to assess knowledge, attitudes, and perceptions towards Artificial intelligence among dental students of Faculty of Dentistry, Tanta University

Eligibility criteria:
Inclusion Criteria:

* all dental students of Faculty of Dentistry, Tanta University

Exclusion Criteria:

* The postgraduate students

Outcomes:
The online form by questionnaire
The Development and Validation of Maternal and Fetal Electrocardiograms (ECG) Separation Algorithm Based on Artificial Intelligence Application
Trial ID: NCT07518550
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-12
Completion date: 2028-05-30
Sponsor: I.M. Sechenov First Moscow State Medical University
Conditions: Pregnancy Related
Interventions: Maternal and fetal electrocardiograms separation
Locations: V.F. Snegirev Clinic of Obstetrics and Gynecology of I.M. Sechenov First Moscow State Medical University
Countries: Russia
Enrollment: 350
Age range: 18 Years - 55 Years
Sex: FEMALE

Summary:
Effective monitoring of fetal heart activity during the second and third trimesters remains a vital challenge in perinatal medicine. This study proposes an adaptive algorithm for extracting the fetal electrocardiograms signal from abdominal ECG in pregnant women, considering the physiological characteristics of each trimester. Utilizing modern machine learning methods, independent component analysis, and data from wearable textile electrodes. The goal is to enhance the accuracy and reliability of automatic signal separation. A dataset of 300 recordings will be collected and analyzed. The resulting algorithm will enable rapid and precise detection of fetal heartbeats. To validate the algorithm, 50 patients will be recruited separately.

Eligibility criteria:
Inclusion Criteria:

* Age over 18 years
* Recordings obtained during the second or third trimester of pregnancy
* Recording duration of at least 5 minutes
* Singleton pregnancy
* Signed informed consent

Exclusion Criteria:

* Age under 18 years;
* Multiple pregnancy;
* Recent medical procedures or interventions that could affect the quality of electrocardiographic data;
* Severe maternal conditions (e.g., severe eclampsia, shock, severe organ failure, etc.);
* Severe fetal conditions (e.g., significant hypoxia, severe placental-fetal syndrome, and other life-threatening states).

Exclusion criteria:

1\. Patient's refusal to continue participation in the study.

Outcomes:
Correlation coefficient between automatically extracted fetal heart rates and reference. signals
Effectiveness of an Artificial Intelligence-Enabled Electric Toothbrush and Visual Pedagogy Materials on Oral Hygiene of Children With Autism Spectrum Disorder
Trial ID: NCT07160517
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-01
Completion date: 2027-02-10
Sponsor: University of Sao Paulo
Conditions: Autism Spectrum Disorder; Child; Oral Health; Artificial Intelligence
Interventions: Visual Pedagogy and Manual Toothbrushing
Locations: School of Dentistry of Ribeirão Preto - University of São Paulo (FORP-USP)
Countries: Brazil
Enrollment: 50
Age range: 5 Years - 13 Years
Sex: ALL

Summary:
This randomized clinical trial evaluates the effectiveness of an AI-enabled electric toothbrush and visual pedagogy materials in improving oral hygiene among children with autism spectrum disorder (ASD). The study compares plaque control, gingival health, and adherence between children using a manual toothbrush with visual pedagogy support and those using an AI-enabled electric toothbrush with app-based monitoring.

Eligibility criteria:
Inclusion Criteria:Children aged 5-13 years with a prior diagnosis of autism spectrum disorder (ASD) level 1 , 2 or 3 confirmed by a neuropediatrician.

Child must be accompanied by a primary caregiver (≥18 years old) responsible for daily oral hygiene.

Caregiver must have access to a smartphone or computer to interact with the digital platform.

Written informed consent (TCLE) from the caregiver and assent (TALE) from the child.

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Exclusion Criteria:Severe systemic medical conditions that contraindicate participation in clinical oral evaluations.

Children with advanced periodontal disease or other oral conditions requiring urgent dental treatment.

Caregivers who are unable or unwilling to use basic digital platforms (smartphone, tablet, or computer).

Families who decline participation at any stage of the study.

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Outcomes:
Change in Plaque Index (Simplified Oral Hygiene Index - Greene & Vermillion)
Detection of Periapical Lesions on Dental Panoramic Images Based on Artificial Intelligence Using Cone Beam Computed Tomography
Trial ID: NCT05888935
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-10-01
Completion date: 2024-12-01
Sponsor: Centre Hospitalier Régional Metz-Thionville
Conditions: Periapical Diseases
Locations: CHR Metz-Thionville/Hopital de Mercy
Countries: France
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
Dental periapical damages can have various reasons and is reflected by a radiolucent lesion on complementary imaging: angulated retro-alveolar (RA) radiographs, dental panoramic radiographs, and three-dimensional imaging such as computed tomography (CT) or cone-beam computed tomography (CBCT).

For the radiographic detection of these deep periodontal lesions, the dental panoramic represents a first approach commonly performed with relatively low radiation. The investigation can be followed by retroalveolar radiology imaging that are more localized and more precise. However, using these techniques, the detection rates of these lesions are low (20% and 36% respectively), it is necessary to use three-dimensional tomographic investigation to be more discriminating (69%). The gold standard imaging for detection of these lesions is CBCT followed by retroalveolar radiography (\~2x less sensitive than CBCT) and panoramic radiography (\~2x less sensitive than RA). Although not a full-thickness radiograph, the dental panoramic has the advantage of being more commonly performed while being less radiating than CBCT and giving a global view of the dental arches on a single image.

The detection of periapical lesions is done after a clinical assessment and a visual appreciation of the complementary examinations.

The aim of this project is to improve the detection of periapical lesions, by developing an algorithm able to identify them on a panoramic dental radiograph. This algorithm is based on a deep learning system trained with reference data including panoramic dental imaging and CBCT with an acquisition interval of less than 3 months. The model is based on a previous work, will improve the quality of the initial data (using CBCT), using innovative artificial intelligence algorithms (transfer learning).

Eligibility criteria:
Inclusion Criteria:

* Patients who have had CBCT and panoramic dental imaging with less than 3 months between the two examinations

Exclusion Criteria:

* Patients who refused to participe in the study.

Outcomes:
Artificial Intelligence software performance
Clinical Validation of Boneview for FDA Submission: Evaluation of the Ability of the Artificial Intelligence Software, Boneview, to Improve Physicians' and Radiologists' Performances in Detecting Fractures on Bone X-Rays Radiographs.
Trial ID: NCT04532580
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-09-01
Completion date: 2021-07-26
Sponsor: Gleamer
Conditions: Fracture
Interventions: Diagnosis of fracture on X-Ray Radiographs
Locations: Boston Imaging Core Lab
Countries: United States
Enrollment: 480
Age range: 21 Years - None
Sex: ALL

Summary:
The goal of this clinical validation is to assess comparative performances of physicians with and without the assistance of BoneView for the diagnosis of fractures on radiographs.

The performances are evaluated in terms of accuracy (primary endpoint with the pair specificity/sensitivity and the secondary endpoint PPV/NPV) and in terms of time needed for diagnosis (secondary endpoint with the measurement of time on each case).

Eligibility criteria:
Inclusion Criteria:

* Anonymized standard X-ray exams of extremity/pelvis/rib cage/dorsolumbar spine not containing or containing fracture(s).
* Adult patient, with minimum age of 21 years old.

Exclusion Criteria:

* Exams with one or more images including a body part not concerned by the intended use of BoneView (skull and cervical spine)
* Cases with one or more images being used for model design and development.
* Exams with one or more images being of poor quality (lossy), which prevent a proper interpretation by the readers.
* Cases not containing a medically relevant number of views for correct diagnosis.

Outcomes:
measurement of diagnosis performances via calculation of sensitivity, specificity, NPV, PPV
Clinical Research on Navigation and Quality Control System of Mediastinal Ultrasound Endoscopy Based on Deep Learning
Trial ID: NCT05792280
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-01-01
Completion date: 2025-04-10
Sponsor: The Third Xiangya Hospital of Central South University
Conditions: Mediastinum Disease
Interventions: AI system
Locations: The Third Xiangya Hospital of Central South University
Countries: China
Enrollment: 180
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The goal of this clinical trial is to develop and verify the auxiliary role of the artificial intelligence system in mediastinal ultrasound endoscopic scanning. The main questions it aims to answer are as follows: 1.The comparison of the image recognition accuracy between the artificial intelligence system and the ultrasound endoscopist; 2. Whether the artificial intelligence system can improve the integrity and efficiency of the mediastinum scanning for the ultrasound endoscopist. Participants will undergo mediastinal EUS with or without the assistance of the artificial intelligence system.

Eligibility criteria:
Inclusion Criteria:

* 1\. Age ≥18 years old, \<80 years old 2.Patients who need endoscopic ultrasonography; 3. Agree to participate in this study and sign the informed consent form.

Exclusion Criteria:

* Subjects who meet any of the following criteria cannot be selected for this trial:

First. The patient's physical condition does not meet the requirements of conventional endoscopic ultrasonography:

1. Poor physical condition, including hemoglobin ≤8.0g/dl, severe cardiopulmonary insufficiency, etc.
2. Anesthesia assessment failed
3. Pregnancy or breastfeeding
4. In the acute stage of chemical and corrosive injury, it is very easy to cause perforation
5. Recent acute coronary syndrome or clinically unstable ischemic heart attack
6. Heart disease patients with right-to-left shunt, patients with severe pulmonary hypertension (pulmonary artery pressure\> 90mmHg),patients with uncontrolled systemic hypertension and patients with adult respiratory distress syndrome.

Second. Disagree to participate in this study.

Third. There are other problems that do not meet the requirements of this research or that affect the results of the research:

1. Mediastinal lesions have previously undergone surgery or radiotherapy and chemotherapy;
2. Mental illness, drug addiction, inability to express themselves or other diseases that may affect follow-up.

Outcomes:
Accuracy; The completeness for standard station scanning
Creation of a Video/Image Library of Annotated Full-length Endoscopy Procedures for the Development of Artificial Intelligence-empowered Endoscopy Quality Reporting and Educational Modules
Trial ID: NCT06822816
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-06-10
Completion date: 2028-12-31
Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)
Conditions: Polyp of Colon; Artificial Intelligence (AI)
Locations: Centre Hospitalier de l'Université de Montréal
Countries: Canada
Enrollment: 10000
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to establish a video/image library dataset of complete endoscopy or partial colonoscopy procedures for patients with rectal cancer or inflammatory bowel disease (IBD). With this video/image library, the aims are:

* to develop and validate novel AI-empowered solutions to automatically detect and report endoscopy quality metrics
* to develop automated endoscopy reporting solutions, auditing, and educational tools for residents and fellows to enhance their endoscopy skills.

The hypothesis is that a heterogeneous video/image library will provide:

* comprehensive and robust source material to develop AI models
* real-time quality feedback at the end of an endoscopy procedure.

Eligibility criteria:
Inclusion Criteria:

* ≥ 18 y.o.
* indication of undergoing a screening, surveillance, diagnostic, or therapeutic upper (EGD) or lower (colonoscopy) endoscopy

Exclusion Criteria:

* Coagulopathy defined as an elevated INR ≥ 2.5
* Platelet count ≤ 50,000/mm3
* Emergency endoscopy
* Poor general health defined as the American Society of Anesthesiologists physical status class \>3

Outcomes:
EGD documented data; Colonoscopy documented data
Histopathology Images Based Prediction of Molecular Pathology in Glioma Using Deep Learning or Machine Learning
Trial ID: NCT04217044
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2017-01-01
Completion date: 2027-06-01
Sponsor: The First Affiliated Hospital of Zhengzhou University
Collaborators: Sun Yat-sen University
Conditions: Glioma
Interventions: Histopathology images based prediction of molecular pathology
Locations: Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University
Countries: China
Enrollment: 3000
Age range: 1 Year - 95 Years
Sex: ALL

Summary:
This registry aims to collect clinical, molecular and radiologic data including detailed clinical parameters, molecular pathology (1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations, etc) and images of HE slices in primary gliomas. By leveraging artificial intelligence, this registry will seek to construct and refine histopathology image based algorithms that are able to predict molecular pathology or subgroups of gliomas.

Eligibility criteria:
Inclusion Criteria:

* Patients must have radiologically and histologically confirmed diagnosis of primary glioma
* Life expectancy of greater than 3 months
* Must receive tumor resection
* Signed informed consent

Exclusion Criteria:

* No gliomas
* No sufficient amount of tumor tissues for detection of molecular pathology
* Patients who are pregnant or breast feeding
* Patients who are suffered from severe systematic malfunctions

Outcomes:
AUC of prediction performance
Deep Learning Algorithm for Detecting Obstructive Coronary Artery Disease Using Fundus Photographs
Trial ID: NCT06102226
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-07-01
Completion date: 2024-12-30
Sponsor: Yong Zeng
Conditions: Coronary Artery Disease; Artificial Heart Device User
Interventions: coronary artery imaging (coronary CTA or coronary angiography)
Locations: Yong Zeng
Countries: China
Enrollment: 7000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Artificial Intelligence, trained through model learning, can quickly perform medical image recognition and is widely used in early disease screening and assisted diagnosis. With the continuous optimization of deep learning, the application of AI has helped to discover some previously unknown associations with other systemic diseases. Artificial intelligence based on retinal fundus images can be used to detect anemia, hepatobiliary diseases, and chronic kidney disease, and to predict other systemic biomarkers. The above studies provide a theoretical basis for the application of artificial intelligence technology based on retinal fundus images to the diagnosis and prediction of cardiovascular diseases.

At present, there is still a lack of accurate, rapid, and easy-to-use diagnostic and therapeutic tools for predictive modeling of coronary heart disease risk and early screening tools in China and the world. Fundus image is gradually used as a tool for extensive screening of diseases due to its special connection with blood vessels throughout the body, as well as easy access, cheap and efficient. It is of great scientific and social significance to develop and validate a model for identification and prediction of coronary heart disease and its risk factors based on fundus images using AI deep learning algorithms, and to explore the value of AI fundus images in assisting coronary heart disease diagnosis and screening for a wide range of applications.

Eligibility criteria:
Inclusion Criteria:

Eligible participants were ≥ 18 years of age, with clinically suspected CAD, and were scheduled for coronary angiography.

Exclusion Criteria:

The exclusion criteria were as follows: (i) prior percutaneous coronary intervention (PCI); (ii) prior coronary artery bypass graft (CABG); (iii) other heart disease (e.g., congenital heart disease, valvular heart disease, or macrovascular disease); (iv) inability to have photographs taken; and (v) and a diagnosis of ST-segment elevation myocardial infarction (STEMI). Prior to the coronary angiography procedure, all eligible patients provided informed consent to participate in the study and to have their photographs used for research purposes.

Outcomes:
AUC
Data-driven Strategies for Substance Misuse Identification in Hospitalized Patients
Trial ID: NCT03833804
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-09-19
Completion date: 2024-09-19
Sponsor: University of Wisconsin, Madison
Collaborators: Rush University Medical Center; National Institute on Drug Abuse (NIDA)
Conditions: Substance Use; Substance Abuse; Substance-Related Disorders
Interventions: Processing of clinical notes in the EHR data collected during routine care
Locations: Rush University Medical Center
Countries: United States
Enrollment: 64996
Age range: 18 Years - 89 Years
Sex: ALL

Summary:
The investigators propose to develop an open-source, publicly available machine learning model that health systems could download and apply to their electronic health record data marts to screen for substance misuse in their patients. The investigators hypothesize that the natural language processing algorithm can provide a standardized and interoperable approach for an automated daily screen on all hospitalized patients and provide better implementation fidelity for screening, brief intervention, and referral to treatment.

Eligibility criteria:
Inclusion Criteria:

* Ages 18 years old to 89 years old
* Inpatient status during hospitalization
* Length of stay greater than 24 hours

Exclusion Criteria:

* Cannot participate in the usual care SBIRT intervention
* Death or obtunded during first 24 hours of admission
* Discharged against medical advice
* Transferred from another acute care hospital
* Transferred to another acute care hospital

Outcomes:
Proportion of Patients That Had a Universal Screen Positive and Received SBIRT (Screening, Brief Intervention, or Referral to Treatment)
The Effect of Education Provided With Artificial Intelligence-Based Materials on Mothers' Fever Management and Anxiety Levels
Trial ID: NCT07086547
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-01
Completion date: 2025-12-31
Sponsor: ISMIRA JULFAYEVA
Conditions: Fever; Pediatric Fever Management; Parental Anxiety
Interventions: AI-Based Fever Management Education
Locations: Erciyes University (ERÜ) Health Application and Research Center, Fevzi Mercan-Mustafa Eraslan Children's Hospital, Pediatric Emergency Unit
Countries: Turkey (Türkiye)
Enrollment: 80
Age range: 18 Years - 50 Years
Sex: FEMALE

Summary:
This study aims to evaluate the effect of an artificial intelligence (AI)-based educational program on mothers' knowledge and anxiety related to fever management in children. Fever is a common symptom in pediatric patients, especially under the age of three, causing repeated health concerns for parents. Despite the prevalence of fever, parents often lack sufficient knowledge and skills to manage it properly, which may lead to unnecessary anxiety and inappropriate care practices.

The study will be conducted as a pretest-posttest experimental design including 80 mothers of children who visit the Pediatric Emergency Clinic at Kayseri City Hospital. Participants will be assigned to either an intervention group receiving AI-supported fever management education or a control group receiving standard care.

Data will be collected using validated scales to assess parental fever management knowledge and anxiety levels before and after the intervention. The educational material was developed using ChatGPT 4.0, a large language model AI platform, and reviewed by pediatric experts.

The results of this study may improve fever management education and reduce anxiety among mothers, potentially leading to better health outcomes for children.

Eligibility criteria:
Inclusion Criteria:

* Mothers aged over 18 years,
* Mothers with children aged between 6 months and 5 years,
* No hearing problems,
* Agree to participate in the study,
* Presenting to the pediatric emergency department with a complaint of fever in their child,
* Child admitted to the emergency observation room for treatment,
* Able to speak and understand Turkish.

Exclusion Criteria:

* Illiterate mothers,
* Mothers whose children have had febrile seizures,
* Mothers whose children have comorbid diseases,
* Mothers whose children have symptoms requiring medical intervention other than fever.

Outcomes:
Change in Parental Fever Management Practices; Change in Parental Fever Management Practices