moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Diagnostic Accuracy of Oral Images, Orthopantomographs (OPGs) and Self-Reported Questionnaires vs. Clinical Assessment for Detecting Periodontal Health and Disease: a Multi-center Diagnostic Study
Trial ID: NCT07164573
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-11-13
Completion date: 2028-11-13
Sponsor: Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
Collaborators: University of Chieti; Department of Medical Sciences, University of Torino; King's College London; University of Roma La Sapienza
Conditions: Periodontal Diseases; Gingivitis; Periodontitis
Locations: Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine
Countries: China
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
This is a multi-center, cross-sectional diagnostic study aimed at evaluating the accuracy of various non-invasive methods-including self-reported questionnaires, intra-oral photographs, smartphone images, intraoral scans (IOS), and orthopantomographs (OPGs)-in detecting periodontal health and disease, compared to clinical periodontal examination as the gold standard. The study will enroll 2,000 subjects across five centers, representing the full spectrum of periodontal conditions (health, gingivitis, and periodontitis stages I-IV). Participants will undergo a standardized clinical examination, radiographic imaging, and complete validated questionnaires. Machine learning models (e.g., HC-Net+ for OPGs and DLM for oral image) will be used to analyze images and integrate data domains. The primary outcome is the diagnostic accuracy (sensitivity, specificity, AUROC) of each method alone and in combination for classifying periodontal status. The study aims to validate and refine AI-based tools for scalable, efficient periodontal screening in clinical and community settings.

Eligibility criteria:
Inclusion Criteria:

1. Adult patients aged 18 years or older.
2. Seeking dental care at one of the participating study centers.
3. Ability to understand and willingness to provide written informed consent.

Exclusion Criteria:

1. Edentulous patients (complete tooth loss).
2. Pregnancy or lactation.
3. History of periodontal therapy (other than supragingival prophylaxis/cleaning) within the past 12 months.
4. Use of antibiotic medication within the 3 months prior to enrollment.

Outcomes:
Diagnostic accuracy for detecting periodontitis (Stage II-IV) as determined by the Area Under the Receiver Operating Characteristic Curve (AUROC) of each index test against the clinical reference standard
Pulsed ElectroMagnetic Field Therapy With Square Wave Forms and Artificial Intelligence Within the Software Interface for Individualised, Bespoke Bio-Electromagnetic Therapy to Treat Symptoms of Chronic Lyme Disease Remaining After Antibiotics Treatment
Trial ID: NCT04577053
Status: TERMINATED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-10-07
Completion date: 2021-09-28
Sponsor: Kasey Phifer
Conditions: Lyme Disease; Lyme Neuroborreliosis; Lyme Arthritis; Unknown Origin Fever
Interventions: Scientific Consciousness Interface Operations (SCIO) Class II FDA approved medical device
Locations: The Bristol Centre for Biofeedback; The National Centre for Electromagnetic Therapies CIC
Countries: United Kingdom; United Kingdom
Enrollment: 20
Age range: 14 Years - None
Sex: ALL

Summary:
Overview

PEMF Therapy for relief or reduction of lingering symptoms after antibiotic treatment of Lyme disease of participants in the UK.

Symptoms monitored:

Muscle ache, myalgia, muscle pain that is acutely located and/or 'wandering' (different location on different days) Aching joints Headache Fatigue, general tiredness, loss of energy, general exhaustion Mild, recurrent fever and/or chills occurring regularly Lack of oxygen in blood, feelings of 'air hunger', too high carbon dioxide levels in blood

Eligibility criteria:
Inclusion Criteria:

* Diagnosis from GP confirming Lyme disease and/or
* Blood test confirming Lyme disease and/or
* Symptoms that correlate to Lyme disease without diagnosis from GP. This is called "self diagnosis" and because 80% of the trial is focused on treating symptoms and not the spirochetes, bacteria or small cysts of Lyme disease, the results are expected to be largely the same

Exclusion Criteria:

* Persons currently undergoing antibiotic treatment for Lyme or other conditions
* Persons who were prescribed antibiotics but did not or could not take them

Outcomes:
Overall Energy Levels; Myalgia; Joint stiffness; Depression; Fever/Chills
Duke Health Listens Survey on Generative Artificial Intelligence (AI) for MyChart Messaging
Trial ID: NCT06108037
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-31
Completion date: 2023-12-11
Sponsor: Duke University
Conditions: Communication
Interventions: Generative AI for electronic communication and disclosure
Locations: Duke University Health System
Countries: United States
Enrollment: 1454
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to understand how patients feel about the use of computer programs to create responses when they send electronic messages to their doctors.

Eligibility criteria:
Inclusion Criteria:

* Member of the Duke Health Listens patient advocacy community

Exclusion Criteria:

* Age \< 18

Outcomes:
Patient satisfaction, as measured by survey; Patient attitudes towards utility, as measured by survey; Patient empathy, as measured by survey
Longterm Effectiveness of Artificial Intelligence-assisted Colonoscopy on Adenoma Recurrence - a Prospective Longitudinal Follow-up of Randomized Controlled Trial
Trial ID: NCT06251700
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-01-24
Completion date: 2027-04-30
Sponsor: Chinese University of Hong Kong
Conditions: Screening Colonscopy
Interventions: ENDO-AID CADe
Locations: Prince of Wales Hospital
Countries: Hong Kong
Enrollment: 775
Age range: 18 Years - None
Sex: ALL

Summary:
We hypothesize that AI-assisted colonoscopy can reduce post-colonoscopy neoplasia incidence after 3 years, over standard colonoscopy. Moreover, this protective effect may allow surveillance intervals to be lengthened, by modifying long-term outcome of high-risk subgroup.

Eligibility criteria:
Inclusion Criteria:

Subjects are eligible if:

(i) They underwent randomization to receive colonoscopy with/without CADe in ENDOAID-TRAIN study \[NCT04838951\]; (ii) They are fit and willing to undergo surveillance colonoscopy at year 3; (iii) Written informed consent obtained.

Exclusion Criteria:

Subjects will be excluded from the study if they have any of the followings:

(i) Incomplete colonoscopy during index procedure; (ii) Known residual colorectal neoplasia not removed (except hyperplastic polyps); (iii) Underwent unscheduled interval colonoscopy before year 3; (iv) Contraindications to surveillance colonoscopy at year 3; (v) Advanced comorbid (American Society of Anesthesiologists grade 4 or above); (vi) History of CRC, hereditary polyposis syndrome or inflammatory bowel disease; (vii) History of colectomy at any time point.

Outcomes:
Post-colonoscopy adenomas at year 3
Application of Artificial Intelligence for Early Diagnosis of Gastric Cancer During Optical Enhancement Magnifying Endoscopy
Trial ID: NCT04563416
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-07-10
Completion date: 2020-12-30
Sponsor: Shandong University
Conditions: Artificial Intelligence; Optical Enhancement Endoscopy; Magnifying Endoscopy
Locations: Department of Gastroenterology, Qilu Hospital, Shandong University
Countries: China
Enrollment: 80
Age range: 18 Years - None
Sex: ALL

Summary:
Previous prospective randomized controlled study demonstrated higher accuracy rate of diagnosing early gastric cancers by Magnifying image-enhanced endoscopy than conventional white-light endoscopy. Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner. we developed a new computer-aided system to assist endoscopists in identifying early gastric cancers in magnifying optical enhancement images.

Eligibility criteria:
Inclusion Criteria:

* patients receive optical magnifying OE endoscopy examination

Exclusion Criteria:

* Patients with advanced cancer, lymphoma,active stage of ulcer and artificial ulcer after ESD were excluded.

Outcomes:
the diagnosis efficiency of the computer-assist diagnosis tool
Randomized Controlled Trial of Artificial Intelligence-Driven Virtual Reality Rehabilitation for Upper Limb Recovery in Acute and Subacute Stroke Patients
Trial ID: NCT07456306
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-01
Completion date: 2026-08-01
Sponsor: Complejo Asistencial Universitario de León Urgencias
Collaborators: University of Salamanca
Conditions: Stroke; Rehabilitation
Interventions: AI-driven VR rehabilitation therapy; Standard rehabilitation
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
Background: Upper limb motor deficits are common after stroke, and early rehabilitation is crucial for functional recovery. Virtual reality (VR) combined with artificial intelligence (AI) has shown potential to enhance motor learning, but evidence from randomized controlled trials in acute and subacute hospitalized patients is limited.

Objective: To evaluate whether AI-driven VR rehabilitation in addition to standard care improves upper limb motor recovery compared with standard rehabilitation alone in acute and subacute stroke patients.

Design: Prospective, assessor-blinded, randomized controlled trial with stratified 1:1 allocation by baseline Fulg Meyer Assessment Score.S Participants: Adults (≥18 years) with confirmed acute or subacute stroke, admitted to the hospital, with upper limb motor déficits.

Intervention: Experimental group will receive standard rehabilitation plus AI-driven VR therapy during hospitalization. Control group will receive standard rehabilitation alone.

Primary Outcome: Change in FM-UE score from baseline to hospital discharge. Secondary Outcomes: VR-derived digital biomarkers, therapy adherence, NIHSS, FM-UE, and Modified Ashworth Scale at each session, and the following baseline and treatment variables: etiology, Modified Rankin Scale (mRS), handedness, stroke lateralization, aphasia, acute reperfusion, reperfusion treatment, days from stroke to start of rehabilitation, adverse events, patient satisfaction, and date of discharge. FM-UE at 1 month post-intervention.

Randomization and Blinding: Patients will be randomized in a 1:1 ratio to either group, stratified by baseline FM-UE (≤30 vs \>30) to ensure balance between groups for stroke severity. Within each stratum, block randomization will be used. Outcome assessors will be blinded.

Sample Size: 50 patients (25 per group).

Ethics and Registration: Approved by hospital ethics committee. Significance: This trial will provide evidence on the effectiveness of AI-driven VR therapy for early upper limb rehabilitation in hospitalized stroke patients.

Eligibility criteria:
Inclusion Criteria:

* Adults ≥18 years
* Confirmed acute or subacute stroke (ischemic or hemorrhagic)
* Upper limb motor deficits
* Admitted to hospital
* Favourable assessment by the rehabilitation service to begin treatment
* Signing of informes consent

Exclusion Criteria:

* Moderate to severe cognitive impairment.
* Severe global aphasia.
* Language barrier.
* Visual impairment.
* Previous stroke with significant sequelae in upper limb.
* Contraindication for RHB therapy.

Outcomes:
Motor function
A Pilot Study to Evaluate Anaesthetists' Ultrasound Scanning & Interpretation Performance for Ultrasound-guided Regional Anaesthesia - With & Without the Assistance of an Artificial Intelligence Device ScanNav Anatomy Peripheral Nerve Block
Trial ID: NCT05156099
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-12-06
Completion date: 2022-12-08
Sponsor: IntelligentUltrasound Limited
Conditions: Ultrasound Imaging of Anatomical Structures
Interventions: Ultrasound Scanning; Ultrasound Scanning - 2 months later
Locations: Intelligent Ultrasound Limited
Countries: United Kingdom
Enrollment: 21
Age range: 18 Years - None
Sex: ALL

Summary:
This is a single-centre, randomised, interventional prospective cohort study to be undertaken at offices of Intelligent Ultrasound Limited in Cardiff, United Kingdom.

Eligibility criteria:
Inclusion Criteria:

1. Male or female, at least 18 years of age
2. Able to comprehend and sign the Informed Consent prior to enrolment in the study
3. Available to travel and attend the study day in person in Cardiff, UK
4. Medical professional (currently practicing anaesthetist or similar clinician) who is qualified to perform UGRA
5. Do not self-identify as an expert in the field of UGRA:

   1. Has not completed advanced training or hold a qualification (e.g., EDRA, MSc, MD, PhD) related to UGRA
   2. Does not regularly deliver anaesthesia for 'awake' surgery (excluding central neuraxial techniques)
   3. Does not teach on formal RA courses

Exclusion Criteria:

1. Aged \<18 years of age
2. Unwilling or unable to provide informed consent
3. Expert in UGRA (see definition above)

Outcomes:
Correct identification of appropriate block site (at initial assessment and 2 months later)
Artificial Intelligence Diagnosis of Different Histopathological Growth Patterns of Colorectal Cancer Liver Metastasis
Trial ID: NCT07088393
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-09
Completion date: 2025-12-31
Sponsor: Sun Yat-sen University
Conditions: Liver Metastases of Colorectal Cancer
Locations: Sixth Affiliated Hospital, Sun Yat-sen University
Countries: China
Enrollment: 437
Age range: None - None
Sex: ALL

Summary:
This study selected cases of colorectal cancer liver metastasis patients who underwent liver metastasis tumor resection, retrieved the pathological HE sections of the metastatic lesions, and constructed a predictive model. AI software was applied to delineate different types of regions, achieving full automation of HGP prediction and constructing a predictive model. Statistical analysis was conducted on the classification of histopathological growth patterns (HGP) of liver metastasis and the survival prognosis of patients, and the differences in prognosis among different HGP classification methods were compared. This provides a new method for judging prognosis and treatment for clinical treatment of colorectal cancer liver metastasis patients.

Eligibility criteria:
Inclusion Criteria:

* Patients with colorectal cancer liver metastases who underwent resection of liver metastases;
* Confirmed by a pathologist as having liver metastases from colorectal cancer;

Exclusion Criteria:

* Cases of colorectal cancer liver metastasis that cannot be classified by histopathology.

Outcomes:
The accuracy rate of the predictive model for HGP classification
Development of Dysphagia Evaluation Via Video Analysis Based on Deep Learning Method in Neonates and Infants and Correlation Between the Evaluation and the Development
Trial ID: NCT05204966
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-08-02
Completion date: 2024-12-31
Sponsor: Asan Medical Center
Conditions: Dysphagia of Newborn
Interventions: AI analysis
Locations: Asan medical Center
Countries: South Korea
Enrollment: 50
Age range: None - 6 Months
Sex: ALL

Summary:
The purpose of this study is to develop an evaluation of dysphagia through deep learning-based video analysis in newborns and infants, and to report the correlation with future development.

Eligibility criteria:
Inclusion Criteria:

* Newborns and infants under 6 months of correctional age
* Clinically suspected to have dysphagia
* In case parental consent is obtained

Exclusion Criteria:

* In case of cardiopulmonary instability (ECMO, Ventilator, etc. are being applied)
* When it is not possible to secure sufficient fields for artificial intelligence analysis of images due to factors such as anatomical abnormalities of the head and neck

Outcomes:
Correlation between NOMAS evaluation and AI analysis of dysphagia; Correlation between VFSS evaluation and AI analysis of dysphagia
Artificial Intelligence (AI)-Assisted Risk-based Prostate Cancer Detection: A Synergy of Novel Biomarkers, Advanced Imaging, and Robotic-assisted Diagnosis
Trial ID: NCT05443412
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-08-01
Completion date: 2026-10-30
Sponsor: Chinese University of Hong Kong
Conditions: Prostate Cancer
Interventions: Diagnostic test for prostate cancer
Locations: Prince of Wales Hospital, Chinese University of Hong Kong
Countries: Hong Kong
Enrollment: 510
Age range: 18 Years - None
Sex: MALE

Summary:
This is a prospective clinical study recruiting 510 men at risk of PCa to undergo urine, blood, AI-assisted ultrasound and AI-assisted MRI investigations to stratify risk of clinically significant PCa (csPCa). (sample size calculation in section 5)

Eligibility criteria:
Inclusion Criteria:

* Men ≥18 years of age
* Clinical suspicion of prostate cancer
* Serum Prostate-specific antigen (PSA) 4-20 ng/mL
* Digital rectal examination ≤ cT2 (organ confined cancer)
* Able to provide written informed consent

Exclusion Criteria:

* Prior prostate biopsy
* Past or current history of prostate cancer
* Contraindicated to undergo plain MRI scan (e.g. pacemaker in-situ, claustrophobia)
* Contraindicated to transperineal prostate biopsy: active urinary tract infection, fail TRUS probe insertion or lithotomy position, uncorrectable coagulopathy, antiplatelet or anticoagulant which cannot be stopped

Outcomes:
Diagnosis of clinically significant Prostate cancer (csPCa); • csPCa is diagnosis of ISUP Grade group ≥2 prostate cancer in at least 1 biopsy core
A Multicenter Prospective Randomized Controlled Trial of Linked-Color Imaging for Detection of Clinically Significant Serrated Lesions in Computer-Aided Water Exchange Colonoscopy
Trial ID: NCT07127471
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-17
Completion date: 2027-07-31
Sponsor: Evergreen General Hospital, Taiwan
Collaborators: Centre hospitalier de l'Université de Montréal (CHUM); King Chulalongkorn Memorial Hospital; Valduce Hospital
Conditions: Sessile Colonic Polyp; Serrated Lesion; Sessile Serrated Lesion; Colon Adenomas
Interventions: Water exchange with AI-assisted detection; Water exchange with AI-assisted detection and LCI
Locations: University of Montreal Medical Center (CHUM); Ospedale Valduce; Evergreen General Hospital; King Chulalongkorn Memorial Hospital
Countries: Canada; Italy; Taiwan; Thailand
Enrollment: 1090
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
The goal of this clinical trial is to compare the detection rate of clinically significant serrated lesions (CSSL) in participants undergoing water exchange (WE) colonoscopy with artificial intelligence (AI)-based computer-aided detection (CADe) for screening, surveillance, diagnosis for symptoms, or referred owing to a positive fecal immunochemical test (FIT) or guaiac fecal occult blood test (gFOBT) result. There will be two arms in this study: WE with AI-assisted CADe (WEAID) control and WEAID plus linked-color imaging (LCI). The main question it aims to answer is whether the addition of LCI into WEAID colonoscopy increases CSSL detection rate. Both groups use water instead of air to insert the colonoscope into the cecum. The control method uses CADe to help detect colonic lesions. The study method uses a combination of CADe and LCI to detect lesions. Researchers will compare CSSL detection rate to see if the addition of LCI increases the detection of CSSL during CADe-assisted WE colonoscopy.

Eligibility criteria:
Inclusion Criteria:

* Male and female patients aged 40-80 years scheduled for average-risk screening colonoscopy, post-polypectomy surveillance, diagnosis for gastrointestinal symptoms (including unexplained iron deficiency anemia and clinically significant diarrhea of unexplained origin), or referred for colonoscopy owing to a positive fecal immunochemical test (FIT) or guaiac fecal occult blood test (gFOBT) result.

Exclusion Criteria:

* familial adenomatous polyposis and hereditary non-polyposis CRC syndrome
* personal history of serrated polyposis syndrome
* personal history of CRC
* history of inflammatory bowel disease
* previous colonic resection
* overt gastrointestinal bleeding
* emergency colonoscopy or inpatients
* planned EMR or ESD of large polyps
* colon stricture or obstruction
* contraindications to colonoscopy (eg, acute diverticulitis or toxic megacolon)
* antithrombotic therapy precluding complete polyp resection
* American Society of Anesthesiology classification of physical status \>3
* pregnant women or women planning pregnancy
* refusal to provide a written informed consent

Outcomes:
Clinically significant serrated lesion (CSSL) detection rate
Enhanced Quantitative Assessment of Lumbar Facet Joint Degeneration: Refining the Weishaupt Grading System With Grayscale Curve Fitting and Imaging Parameter.
Trial ID: NCT06786611
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-09-01
Completion date: 2024-07-31
Sponsor: Shanghai Changzheng Hospital
Conditions: Facet Joint Pain; Low Back Pain; Facet Joints; Degeneration ; Deep Learning ;Artificial Intelligence; Facet Joint Disease
Interventions: MRI
Locations: Changzheng Hospital, Second Military Medical University
Countries: China
Enrollment: 48
Age range: 20 Years - 80 Years
Sex: ALL

Summary:
Observational study

The purpose of this observational study was to understand the quantitative assessment of the degree of degeneration of lumbar facet joints, and to improve the Weishaupt grading system by using gray-scale curve fitting and imaging parameters.

The main questions it aims to answer are: How to realize quantitative analysis of lumbar facet joint degeneration in patients with chronic low back pain with MRI?

The MRI information of patients with different grades of articular process joint degeneration has been statistically summarized in nearly 5 years

Eligibility criteria:
Inclusion Criteria:

* (1) Age range from 20 to 80 years old; (2) Persisted chronic low back pain (CLBP) more than 12 weeks; (3) Complete preoperative X-ray and 1.5T MRI examination.

Exclusion Criteria:

* (1) Scoliosis, Cobb angle\>20°; (2) Lack of preoperative X-ray or MRI examination; (3) 3.0T MRI; (4) The interval between preoperative X-ray and MRI examinations is more than one month; (5) Previous lumbar spine internal fixation surgery; (6) Lumbar spondylolisthesis \> 5 mm, with severe dislocation of facet joints; (7) Intravertebral bone cement implantation; (8) Severe spinal joint disease (e.g. mandatory spondylitis); (9) Spinal tumors; (10) The maximum extension depth of Modic changes (MC) relative to vertebral body height was greater than 25%.

Outcomes:
Grayscale Curve Fitting