moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Developing a Treatment Clustering System for Obstructive Sleep Apnea Using Polysomnographic Physiological Signals
Trial ID: NCT06512779
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-30
Completion date: 2026-05-01
Sponsor: National Cheng-Kung University Hospital
Conditions: Obstructive Sleep Apnea of Adult
Interventions: Control Group; Surgical Intervention; Oropharyngeal Training (face-to-face); Oropharyngeal Training (telerehabilitation); Continuous Positive Airway Pressure
Locations: National Cheng Kung University Hospital
Countries: Taiwan
Enrollment: 150
Age range: 20 Years - None
Sex: ALL

Summary:
Obstructive sleep apnea syndrome (OSA) is marked by repeated upper airway obstructions during sleep, affecting approximately 14% of men and 5% of women aged 30-70 years.

However, precise clinical prediction tools for selecting optimal treatment strategies are lacking. This study aims to develop an automated treatment clustering system using artificial intelligence to classify patients based on etiology into (i) anatomical factors, (ii) reduced muscle responsiveness, and (iii) other non-anatomical factors. This system will analyze physiological sleep assessments, such as electromyography (EMG) and pneumotachographs, from a retrospective polysomnography (PSG) database. Cross-validation will be conducted on new OSA patients undergoing various management strategies, including surgical intervention, CPAP therapy, and oropharyngeal training (delivered face-to-face or via telerehabilitation). This system aims to enhance clinicians' ability to predict treatment success rates and improve patient outcomes.

Eligibility criteria:
Inclusion Criteria:

* OSA patients
* Aged over 20 years

Exclusion Criteria:

* BMI≧ 32
* Central or mixed types of sleep apnea
* A history of malignancy or infection of the head and neck region and laryngeal trauma
* Craniofacial malformation
* Stroke
* Neuromuscular disease
* Severe cardiovascular disease
* Active psychiatric disease
* Structural abnormalities over the upper respiratory airway
* Performed any operation or treatment over the neck before
* Pregnancy

Outcomes:
Apnea-hypopnea -index
Efficacy of Artificial Intelligence-Generated Music Therapy in Reducing Dental Anxiety Among Adults Undergoing Impacted Third Molar Extraction: A Randomized Controlled Clinical Trial
Trial ID: NCT06998979
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-04
Completion date: 2025-02-20
Sponsor: University of Coimbra
Collaborators: University of Salamanca
Conditions: Tooth Extraction; Molar, Third
Interventions: Music therapy
Locations: Department of Maxillofacial Surgery at the University Hospital Complex of Salamanca
Countries: Spain
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
Music therapy is widely used in dentistry as it helps patients remain distracted during procedures, particularly during implant placement or third molar extraction. This therapy refers to the evidence-based clinical use of music to achieve therapeutic goals, administered by a licensed practitioner. Due to the high prevalence of complications associated with the eruption and development of wisdom teeth, there has been an increase in the rate of surgical extractions, which in turn elevates patients' dental anxiety. Music therapy may aid in the management of anxiety and thereby contribute to a reduction in pain perception. It is a favorable technique due to its low cost and minimal associated risks. In this context, and considering the widespread use of music as a therapeutic tool alongside the emergence of novel technologies-particularly Artificial Intelligence (AI)-this study aims to evaluate the efficacy of AI-generated music therapy in reducing dental anxiety during impacted third molar extraction procedures.

Eligibility criteria:
Inclusion Criteria:

* Patients over 18 years of age who provided informed consent
* Patients without severe or uncontrolled systemic diseases
* Patients capable of completing the dental anxiety questionnaires

Exclusion Criteria:

* Patients under 18 years of age
* Patients with severe or uncontrolled systemic diseases
* Patients using anxiolytic medications
* Patients unable to complete the dental anxiety questionnaires

Outcomes:
Corah's Dental Anxiety Scale; Modified Dental Anxiety Scale; Spielberger State-Trait Anxiety Inventory; Pain Intensity
Using Artificial Intelligence to Improve Radiologists' Performance in Radiology Report Writing: a Randomised Controlled Trial
Trial ID: NCT06907927
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-01
Completion date: 2025-04-30
Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Conditions: Healthy
Interventions: Control group; Experimental group
Locations: Union Hospital，Tongji Medical College，Huazhong University of Science and Technology
Countries: China
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
This is a randomised controlled trial to assess whether artificial intelligence software can improve radiologists' performance in radiology report writing and improve the quality of radiology reports.

Eligibility criteria:
Inclusion Criteria:

1. Radiologists who submit reports;
2. Clinical experience of less than 10 years and more than six months;
3. An average of more than 10 radiology reports are submitted per day;
4. Participants provided signed informed consent.

Exclusion criteria：

1. Radiologists who review reports;
2. Clinical experience of less than 6 months or more than 10 years;
3. An average of less than 10 radiology reports are submitted per day.）

Outcomes:
Error rates in radiology reports
Impact Of Artificial Intelligence-Based Prediction of Facial Changes in Bimaxillary Protrusion Cases on Patient Motivation and Satisfaction
Trial ID: NCT07066423
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-11-01
Completion date: 2025-11-01
Sponsor: Mansoura University
Conditions: Bimaxillary Protrusion
Interventions: Ai prediction photo of the patients; Fixed orthodontic brackets with premolars extraction
Locations: Mansoura university
Countries: Egypt
Enrollment: 45
Age range: 16 Years - 30 Years
Sex: ALL

Summary:
After developing an Artificial Intelligence model to predict the facial changes of patients with bimaxillary protrusion following orthodontic extraction of premolars and tooth retraction, participants will view a prediction in the form of a picture generated by the AI model, and their motivation will be measured using a questionnaire. Their satisfaction will be evaluated using another questionnaire after they complete their treatment. A comparison will then be made between the actual results and the AI-predicted results.

Eligibility criteria:
Inclusion Criteria:

1. Adult cases age range from 16 to 30 years old
2. Skeletal Bimaxillary protrusion with SNA and SNB increased
3. Dental bimaxillary protrusion with U1- SN and IMP a increased angles
4. Class I canine and molar angle classification
5. Free from any systemic disease
6. free from any dental surgery

Exclusion Criteria:

Clefts of the lip or palate and craniofacial syndromes; orthognathic patients will not be included because they do not represent the general adult orthodontic population

Outcomes:
Participants motivation for treatment
A Multicenter Study to Identify Determinants Associated With Black Carbon Concentration in Biological Samples and Predictors of Quality of Life in Different Subpopulations Distinguished by Age Group and Residence
Trial ID: NCT06591988
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2024-09-01
Completion date: 2025-10-01
Sponsor: Azienda Sanitaria Locale di Taranto
Conditions: Environment; Pollution Exposure; Mortality; Healthcare; Lifestyle Factors
Interventions: Lifestyle factors
Locations: Local Health Authority of Taranto
Countries: Italy
Enrollment: 608
Age range: None - None
Sex: ALL

Summary:
This study is part of the MISTRAL project, funded by the European Commission under the Horizon Europe Program, within the Work Programme 2021-2022 of Pillar II - Health Cluster on Environment and Health, which has the main objective of creating a cloud-based informatics platform for Health Impact Assessment analysis that, thanks to artificial intelligence, is able to simulate different clinical scenarios, economic and social, before and after the introduction of environmental risk mitigation policies, starting from data and evidence derived from case studies (several European cities) with different levels of exposure to environmental, health, geographical and socio-economic factors. The Project involves 11 partners distributed among Italy (Istituto Superiore di Sanità, Azienda Sanitaria Locale di Taranto, University of Bari, Politecnico di Bari, APS PLANET), Belgium (University of Hasselt), Poland (AGH-University of Technology and Science), Germany (Nuromedia), Spain (Ubitel), Greece (WINGS-ICT Solutions) and England (University of Oxford, University of Suffolk).

Specifically, the ZEPHYR study is a cross-sectional (cross-sectional), population-based, multicenter survey. The centers involved are the ASL of Taranto with the Taranto City Single District (Coordinating Recruitment Center), the University of Hasselt, Belgium (Recruitment Center Hasselt Hospital Campus), and the University of Krakow, Poland (Rybnik University Hospital as recruitment center).

Eligibility criteria:
Inclusion Criteria:

* Within the population of Taranto (southern Italy), healthy volunteer subjects will be recruited, and sampled from the following geographic areas of the city: the Tamburi district (an area presumably highly exposed to environmental pollutants), and the Talsano-Lama-San Vito district.

Exclusion Criteria:

* unhealthy subjects
* Taranto people residing outside the above areas

Outcomes:
QALY; DALY
Comparison of the Difference in Bone Age Readings Among Radiologists Using Artificial Intelligence
Trial ID: NCT07121283
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-01
Completion date: 2026-08-18
Sponsor: Cheng-Hsin General Hospital
Conditions: Bone Age
Interventions: True AI; Fake AI
Locations: Cheng Hsin General Hospital
Countries: Taiwan
Enrollment: 8
Age range: None - None
Sex: ALL

Summary:
AI-Assisted Bone Age Assessment

Eligibility criteria:
Inclusion Criteria:

* Exams read by radiologists who interpret pediatric skeletal age exams and verbally consent to participate

Exclusion Criteria:

* Exams containing more than one radiograph will not be included. No further exclusion criteria will be applied on the basis of image quality metrics or manufacturers

Outcomes:
Difference of Skeletal Age Estimate
Histopathology Images Based Survival Prediction of Patients With Primary Glioma Using Deep Learning or Machine Learning
Trial ID: NCT04215224
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 survival prediction for glioma patients
Locations: Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University
Countries: China
Enrollment: 3500
Age range: 1 Year - 90 Years
Sex: ALL

Summary:
This registry aims to collect clinical, molecular and histopathology imaging including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, 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 hstopathology imaging based algorithms that able to predict patients' survivals in the frame of 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 malfuctions

Outcomes:
AUC of survival prediction performance
Establishment and Application of an Artificial Intelligence Algorithm for Automatic Identification of Intraoperative Bleeding During Laparoscopic Pancreaticoduodenectomy
Trial ID: NCT06172062
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-07-20
Completion date: 2024-03-30
Sponsor: Xinrui Zhu，MD
Conditions: Laparoscopic Pancreaticoduodenectomy
Interventions: laparoscopic pancreaticoduodenectomy
Locations: West China Hospital of Sichuan University
Countries: China
Enrollment: 400
Age range: None - None
Sex: ALL

Summary:
Surgeons will label surgical videos of laparoscopic pancreaticoduodenectomy with bleeding information from multicenter. The labeled surgical video will be used as training material to train the algorithmic model so as to build an artificial intelligence algorithm that can automatically identify intraoperative bleeding in this surgery. Then, we will test the performance capability of the algorithm.

Eligibility criteria:
Inclusion Criteria:

1\. surgical videos that recorded complete laparoscopic pancreaticoduodenectomy including resection and reconstruction phases.

Exclusion Criteria:

1.surgical videos that recorded laparoscopic pancreaticoduodenectomy combined with resection and reconstruction of mesentericoportal vein.

Outcomes:
performance of artifical intelligence algorithm
A Study to Evaluate the Ability of Speech- and Language-based Digital Biomarkers to Detect and Characterise Prodromal and Preclinical Alzheimer's Disease in a Clinical Setting - AMYPRED-US PAST Extension Study
Trial ID: NCT04937959
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-01-22
Completion date: 2022-08-30
Sponsor: Novoic Limited
Conditions: Alzheimer Disease; Alzheimer's Disease (Incl Subtypes); Preclinical Alzheimer's Disease; Prodromal Alzheimer's Disease; Mild Cognitive Impairment; Normal Cognition
Locations: Syrentis Clinical Research
Countries: United States
Enrollment: 40
Age range: 50 Years - 85 Years
Sex: ALL

Summary:
The primary objective of the study is to evaluate whether a set of algorithms analysing acoustic and linguistic patterns of speech can detect amyloid-specific cognitive impairment in early stage Alzheimer's disease, based on archival spoken or written language samples, as measured by the area under the curve (AUC) of the receiver operating characteristic curve of the binary classifier distinguishing between amyloid positive and amyloid negative arms. Secondary objectives include (1) evaluating how many years before diagnosis of Mild Cognitive Impairment (MCI) such algorithms work, as measured on binary classifier performance of the classifiers trained to classify MCI vs cognitively normal (CN) arms using archival material from the following time bins before MCI diagnosis: 0-5 years, 5-10 years, 10-15 years, 15-20 years, 20-25 years; (2) evaluating at what age such algorithms can detect later amyloid positivity, as measured on binary classifier performance of the classifiers trained to classify amyloid positive vs amyloid negative arms using archival material from the following age bins: younger than 50, 50-55, 55-60, 65-70, 70-75 years old.

Eligibility criteria:
Inclusion Criteria:

* Subjects are fully eligible for and have completed the AMYPRED-US (Amyloid Prediction in early stage Alzheimer's disease from acoustic and linguistic patterns of speech) study.

(See https://clinicaltrials.gov/ct2/show/NCT04928976)

* Subject has access to audio or written recordings created by them that are available for collection.
* Subject consents to take part in PAST extension study.

Exclusion Criteria:

* Subject hasn't completed the full visit day in the AMYPRED-US study.

Outcomes:
The area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) arms.
A Single-center, Retrospective Study to Evaluate the Clinical Performance of Artificial Intelligence Medical Assisted Diagnostic Software (VeriSee DR) for Screening of Diabetic Retinopathy in Patients With Diabetes Mellitus
Trial ID: NCT04160988
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-12-16
Completion date: 2020-05-11
Sponsor: Acer Being Health Inc.
Collaborators: National Taiwan University Hospital
Conditions: Diabetic Retinopathy
Locations: National Taiwan University Hospital
Countries: Taiwan
Enrollment: 703
Age range: 20 Years - None
Sex: ALL

Summary:
This study is to evaluate the clinical performance of VeriSee DR for DR screening from color fundus photography images in patients with diabetes mellitus. The sensitivity and specificity of VeriSee DR's automated image analysis for screening the diabetes retinopathy will be determined.

Eligibility criteria:
Inclusion Criteria:

* Subjects enrolled in this study should meet all the following criteria.

  1. Subject with age ≥ 20 years old
  2. Subject with documented diagnosis of diabetes mellitus
  3. Subject with image taken by color fundus photography that meet the following requirement:

     * The resolution of image is 1024×1024 pixels or higher;
     * The angle view of image is 45 or 50 degree.
  4. Subject's image includes macula and optic nerve as judged by the ophthalmologist.

Exclusion Criteria:

* Subjects will be excluded if they meet any of the following criteria.

  1. The color fundus photography image previously used by VeriSee DR during the development process and pre-clinical test
  2. The macula, optic nerve, or other part in the image of color fundus photography is unclear to determine the disease condition as judged by the ophthalmologist.

Outcomes:
Sensitivity; Specificity
Breast Ultrasound Image Reviewed With Assistance of Deep Learning
Trial ID: NCT03706534
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-09-20
Completion date: 2020-01-31
Sponsor: Samsung Medison
Collaborators: University of Rochester
Conditions: Breast Cancer; Breast Lesions; Breast Mass
Interventions: Ultrasound Image review with CADe; Ultrasound Image review with CADx; Ultrasound Image manual review; Biopsy
Locations: University of Rochester
Countries: United States
Enrollment: 300
Age range: 19 Years - None
Sex: ALL

Summary:
This study evaluates a second review of ultrasound images of breast lesions using an interactive "deep learning" (or artificial intelligence) program developed by Samsung Medical Imaging, to see if this artificial intelligence will help the Radiologist make more accurate diagnoses.

Eligibility criteria:
1. Inclusion Criteria:

   * Adult females or males recommended for ultrasound-guided breast lesion biopsy or ultrasound follow-up with at least one suspicious lesion
   * Age \> 18 years
   * Able to provide informed consent
2. Exclusion Criteria:

   * Unable to read and understand English
   * Unable or unwilling to provide informed consent
   * A patient with current or previous diagnosis of breast cancer in the same quadrant
   * Unable or unwilling to undergo study procedures
3. Subject Characteristics

   1. Number of Subjects: 300 subjects from 300 separate breast lesions can be acquired. If a subject has more than 1 suspicious lesion, each may be chosen by the radiologist attending as suitable for "second review".
   2. Gender and Age of Subjects: Adult females or males aged 18 years or older who meet all of the inclusion criteria and none of the exclusion criteria will be considered for enrollment. Minors are excluded as breast cancer is very rare in this age group.
   3. Racial and Ethnic Origin: There are no enrollment exclusions based on economic status, race, or ethnicity. Based on local and United States census data, the expected ethnic distribution will be approximately 26 Hispanic (approx. 16%) and 134 non-Hispanic people. Furthermore, the expected racial distribution is expected to be approximately 126 White (approx. 79% of the whole study), 21 Black or African America (13%), 8 Asian (5%), and 5 of other categories (3%).
   4. Vulnerable Subjects: It is unlikely that any UR students or employees will be enrolled unless their primary physician refers them to UR Medicine Breast Imaging at Red Creek for breast ultrasound and a suspicious lesion is found. We do not expect any of these referrals to be from staffs who work directly with the PIs.

Outcomes:
Concordance rate
Artificial Intelligence + Care Coach Intervention for Persons Living With Dementia and Caregivers
Trial ID: NCT05352399
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-03-20
Completion date: 2024-06-30
Sponsor: Yale University
Collaborators: National Institute on Aging (NIA); Emergency Medicine Foundation
Conditions: Dementia; Caregiver; Emergency Department; Artificial Intelligence; Care Transition
Interventions: NeuViCare application
Locations: Yale New Haven Hospital
Countries: United States
Enrollment: 40
Age range: 65 Years - None
Sex: ALL

Summary:
The purpose of this research study is to develop and test an artificial intelligence intervention for emergency department (ED) discharge care transitions experienced by caregivers of older adults with cognitive impairment.

Eligibility criteria:
Inclusion Criteria:

* age ≥65 years with a diagnosis of dementia within the electronic health record OR new cognitive impairment identified during an ED visit
* be fluent in English or Spanish
* discharge after an ED visit
* possession of a smart device (either a phone, tablet, or computer) that has internet access to utilize and interact with NeuViCare AI services

Exclusion Criteria:

\- evidence of delirium, by the 4-AT screening tool

Outcomes:
Intervention Appropriateness Measure; Feasibility of Intervention Measure; Acceptability of Intervention Measure; System Usability Scale