moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Arm Motor Rehabilitation, Entertainment and Cognition System for the Elderly (Feasibility Study)
Trial ID: NCT04252170
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-09-15
Completion date: 2019-01-31
Sponsor: Bright Cloud International Corp
Collaborators: National Institute on Aging (NIA); Genesis HealthCare System
Conditions: Stroke
Interventions: Rehabilitation Therapy, Experimental on the BrightArm Compact device
Locations: PowerBack Rehabilitation
Countries: United States
Enrollment: 2
Age range: 50 Years - 85 Years
Sex: ALL

Summary:
The research project is intended to provide information pertaining to the feasibility of the BrightArm Compact robotic rehabilitation system for patients early sub-acute post Cerebral Vascular Accident. The aims are to: a) determine clinical benefit to motor and cognitive function as well as mood; 2) to ascertain technology acceptance by patients and therapists. The experimental training consists of 12 integrative arm/hand and cognitive training by playing therapeutic games. Participants are evaluated pre-and post-intervention and provide subjective evaluation of the system.

Eligibility criteria:
Inclusion Criteria:

* Age 50 to 85;
* diagnosis of first-time CVA that occurred more than 5 days prior and less than 21 days prior
* English speakers;
* UE unilateral or bilateral involvement (from new bilateral CVA)
* motor involvement (FMA score 20 to 45);
* ability to actively move UE more than 10 degrees for shoulder and elbow flexion/extension;
* ability to actively extend fingers at least 5 degrees
* cognitive skills to participate (Montreal Cognitive Assessment (MoCA) \[Nasreddine et al 2005\] score 10-30).
* Subjects may have normal cognition, MCI or dementia

Exclusion Criteria:

* being younger than 50 or older than 85 years of age
* previous stroke
* Stroke that occurred more than 20 days prior to enrollment
* Inability to actively extend fingers at least 5 degrees;
* Fugl-Meyer scores of 19 or less;
* severe visual neglect or legally blind
* severe hearing loss or deafness
* receptive aphasia or severe expressive aphasia;
* severe spasticity (Modified Ashworth Scale 4/4)
* contractures of the upper limb joints
* uncontrolled hypertension (\>190/100 mmHg)
* severe cognitive impairment determined by Montreal Cognitive Assessment (MoCA) \[Nasreddine et al, 2005\] test of 9 and below;
* No chemodenervation or nerve block to upper limb involved during the experimental period (e.g., botulinum toxin injection)
* inability to speak English;
* a history of violence or drug abuse;
* paranoia and psychotic behavior;
* inability participate in the neuropsychological pre-study assessment for reliable scores (e.g., cognitive impairment, communication disorders).

Outcomes:
Artificial Intelligence-determined game difficulty level; Game Score (baseline and performance); Participants' feedback using 5-point Likert scale; Therapist subjective evaluation using 5-point Likert scale
Comprehensive Evaluation of MRI-AI in Prostate Cancer Diagnosis: a Real-World Prospective Diagnostic Study
Trial ID: NCT06575361
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-01-01
Completion date: 2025-08-31
Sponsor: Peking University First Hospital
Conditions: Prostate Cancer
Interventions: Combination of targeted biopsy and systematic biopsy
Locations: Peking University First Hospital
Countries: China
Enrollment: 365
Age range: 45 Years - 85 Years
Sex: MALE

Summary:
The goal of this real-world prospective diagnostic study is to comprehensively evaluate the value of MRI artificial intelligence (MRI-AI) in assisting the diagnosis of prostate cancer (PCa). The main questions it aims to answer are:

Does MRI-AI promote the accurate diagnosis and treatment of prostate cancer? What's the capability of prostate MRI-AI in calculating the prostate volumn? What's the value of prostate MRI-AI assistant diagnosis system in detecting the suspicious lesions on MRI and guiding prostate targeted biopsy? What's the value of prostate MRI-AI assistant diagnosis system in predicting the pathological results of prostate targeted biopsy? Researchers will compare the cancer detection rates of suspicious lesions detected by MRI-AI and senior radiologists.

Participants will:

Receive combination of systematic biopsy and targeted biopsy.

Eligibility criteria:
Inclusion Criteria:

* The age of the patient is between 45 and 85.
* Patients with complete magnetic resonance imaging (MRI) data, qualified image quality control.
* Patients were in accordance with the indication of prostate biopsy, including patients with suspicious prostate nodes found by digital rectal examination (DRE), the suspicious lesions found by transrectal ultrasound (TRUS) or MRI, total prostate-specific antigen (tPSA) \>10ng/mL, tPSA 4-10ng/mL with free-to-total PSA ratio (f/tPSA) \<0.16 or PSA density (PSAD) \>0.15.
* Patients had no history of prior prostate surgery or biopsy.
* The PSA of patients should be ≤20 ng/mL.
* The prostate biopsy pathological results of above lesions were complete. The time interval between targeted prostate biopsy and prostate MRI examination should not exceed one month.
* Patients with complete clinical information.

Exclusion Criteria:

* The clinicopathological information and MRI data was unqualified or incomplete.
* Patients had received radiotherapy, chemotherapy, androgen deprivation therapy, or surgery treatment before prostate MRI examination or prostate biopsy.
* Patients received prior prostate biopsy.
* Patients had contraindications to MRI or prostate biopsy.
* Patients were not in accordance with the indication of prostate biopsy.

Outcomes:
The clinically significant prostate cancer (csPCa) detection rate for suspicious lesions found by MRI-AI and urogenital radiologists; High-grade PCa detection rate
Fusing Ultrasound and Magnetic Resonance Imaging to Intelligently Plan Highly Conformal Ablation Thermal Field for Hepatocellular Carcinoma
Trial ID: NCT06798194
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-30
Completion date: 2027-12-30
Sponsor: Chinese PLA General Hospital
Conditions: Hepatocellular Carcinoma
Interventions: Post-ablation MRI is used to evaluate whether the ablation area of the tumor is consistent with the highly conformal ablation thermal field provided by the AI model.
Locations: Chinese PLA Hospital
Countries: China
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
Thermal ablation is an important minimally invasive treatment for hepatocellular carcinoma (HCC), but local tumor progression (LTP) after ablation restricts the efficacy and status of ablation technology and seriously threatens patient survival. Insufficient coverage of thermal field is an important factor on the occurrence of LTP. Current thermal field planning relies on tumor contours and doctor experience, and the safety margin is uniform. Therefore, it cannot cope with the problem of insufficient coverage of thermal field caused by the different invasion capabilities of different tumors and different parts of the same tumor. This project intends to integratively analyze gray-scale ultrasound, contrast-enhanced ultrasound, magnetic resonance imaging and clinical information of HCC through deep canonical correlation analysis; summarize the prior knowledge of LTP risk factors in previous studies and perform conjoint analysis individual case data and common conclusions through knowledge graph; interpretatively predict the LTP risk and the high-risk LTP locations through link prediction; accurately predict the ablation safety margin required for different tumor parts through graph neural network, and achieve highly conformal thermal field planning based on different invasion capabilities to minimize the LTP risk of HCC. The project leverages tumor multi-modal imaging and prior knowledge as the entry point, performs highly conformal planning of the ablation thermal field through artificial intelligence technology, and provides a new method for precise ablation.

Eligibility criteria:
Inclusion Criteria:

1. Pathologically confirmed primary hepatocellular carcinoma
2. Undergo curative ablation
3. With complete clinical information and pre- and post-operative imaging information

Exclusion Criteria:

1. Undergo palliative ablation
2. Lack of clinical or imaging information
3. Age less than 18 years

Outcomes:
Local tumor progression
Identification of Neoatherosclerosis in In-stent Restenosis Patients Based on Artifical Intelligence
Trial ID: NCT04220437
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2015-02-01
Completion date: 2021-02-28
Sponsor: Chinese PLA General Hospital
Conditions: In-stent Restenosis; Neo-atherosclerosis; Retrospective
Interventions: no interventin
Locations: The General Hospital of PLA
Countries: China
Enrollment: 90
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Based on the large population of patients, in-stent restenosis (ISR) is still an important problem in the field of cardiovascular disease. How to reduce the incidence of ISR and the treatment of ISR has become the focus and hot spot. The 2018 ESC Guidelines for Cardiovascular Intervention recommends treatment of ISR under the guidance of intravascular ultrasound (IVUS), or optical coherent tomography (OCT). Circulation published a new Waksman ISR classification based on mechanisms and components of the restenosis tissue, which provides guidance for treatment strategy. Because of its good resolution, OCT makes it more accurate to distinguish the components of vascular tissue, thus providing a decision-making basis for interventional therapy. OCT examination can obtain the characteristics of the ISR more precisely. Neoatherosclerosis (NA), is one of the ISR types and accounts for more stent failure and target lesion failure than other types. Identification NA is important for decision-making of interventional therapy. However, the acquisition and analysis of OCT images not only need the digital angiography machine (DSA) equipped with the majority of hospitals, but also need professional OCT imaging equipment and technicians. Patients with severely CKD cannot bear OCT examination because of the large amount of contrast agent. OCT catheter is more than ten times the price of the CAG catheter. Therefore, identification of NA by the use of artificial intelligence (AI) is of significance to set therapeutic strategy for ISR patients, especially in patients with CKD. Our study retrospectively analyzed CAG images and OCT images of ISR patients obtained from Jan 1st,2015 to Oct 31st,2020. Identify NA by analyzing OCT images, build up U-net and V-net to analyze the CAG and OCT images, and finally build up an identification system of NA based on CAG images by AI. This study has been approved by Ethics Committee of Chinese PLA General Hospital (S2018-033-01)

Eligibility criteria:
Inclusion Criteria:

\- all gender

18ys old to 80ys old

diagnosed of in-stent restenosis based on CAG

both CAG images and OCT images were obtained in the same patient on the same day

Exclusion Criteria:

* CAG images and OCT images were not obtained on the same day in the same patient

low quality in CAG images

low qualitiy in OCT images

Outcomes:
The identification of NA; neovascularizaion; ISR segment in the CAG images; lipid-core arc; Thin-cap fibroatheroma-like neointima; macrophage arc
AI-Based Fine Morphological Subtyping of Myeloma Single Cells for Predicting FISH Abnormalities
Trial ID: NCT07410403
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2018-08-01
Completion date: 2026-12-31
Sponsor: Fuling Zhou
Collaborators: Wuhan Central Hospital; The Affiliated Hospital Of Southwest Medical University; Linyi People's Hospital
Conditions: Multiple Myeloma
Locations: Zhongnan Hospital of Wuhan University
Countries: China
Enrollment: 10
Age range: None - None
Sex: ALL

Summary:
This study developed an artificial intelligence (AI)-based methodology for the quantitative analysis of single-cell morphological data in multiple myeloma (MM). The approach achieves high-precision AI-driven identification and segmentation of myeloma cells, nuclei, cytoplasm, and nucleoli, overcoming the inherent limitations of subjective traditional morphological analysis.

Furthermore, integrating this morphological quantification with cytogenetic abnormality analysis of myeloma cells provides an efficient predictive tool for identifying high-risk cytogenetic abnormalities.

Leveraging AI-guided selection of genetic testing targets, the research applied a rapid genetic abnormality detection technique utilizing first-drop bone marrow aspirate smears. This methodology achieves orders of magnitude improvements in testing cost, sample preprocessing time and detection sensitivity.

Eligibility criteria:
Inclusion Criteria:

* according to the criteria outlined in the Chinese guidelines for MM diagnosis and management (2024 edition)

Exclusion Criteria:

* non-MM

Outcomes:
Cytogenetic result of multiple myeloma
Establishment of AI Prediction Model of Blood Transfusion Management in Patients With Mitral Valve Replacement
Trial ID: NCT03885570
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-09-16
Completion date: 2021-12-31
Sponsor: The Third Xiangya Hospital of Central South University
Collaborators: Second Xiangya Hospital of Central South University; Chinese Academy of Medical Sciences, Fuwai Hospital; The Affiliated Hospital Of Southwest Medical University; Qilu Hospital of Shandong University; Zhejiang Provincial People's Hospital; Beijing Aerospace General Hospital
Conditions: Mitral Valve Replacement; Transfusion Reaction; Blood Transfusion Complication
Interventions: Mitral valve replacement
Locations: Third Xiangya Hospital
Countries: China
Enrollment: 2000
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
The evaluation uses ΔHb as an independent factor combined with artificial intelligence (AI) to predict its impact on the prognosis and blood transfusion of patients undergoing cardiac surgery, thereby guiding perioperative clinical blood use and improving patient prognosis.

Eligibility criteria:
Inclusion Criteria:

1. Patients undergoing "mitral valve replacement" surgery;
2. Age 18-75 years old;
3. 48h biochemical indicators before surgery, blood general indicators, coagulation indicators are complete.

Exclusion Criteria:

1. Lack of necessary tests and patient test information is not detailed, blood transfusion information is not detailed;
2. The blood transfusion information is not detailed in the same period as other cardiac operations (such as aortic valve surgery, coronary artery bypass grafting, etc.), but the tricuspid valvuloplasty is not ruled out at the same time;
3. Heart surgery or emergency surgery again.

Outcomes:
Mortality; Intraoperative blood transfusion
Use of Artificial Intelligence in Assessment of Aesthetic Outcomes in Rhinoplasty
Trial ID: NCT07580573
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06-01
Completion date: 2027-07-01
Sponsor: Assiut University
Conditions: Rhinoplasty; AI (Artificial Intelligence)
Interventions: AI
Enrollment: 20
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to thoroughly assess the predictive accuracy of artificial intelligence-based nasal outcome simulations by comparing AI-generated preoperative predictions with objective postoperative nasal morphology using digital image analysis.

To assess accuracy of AI-image measurement compared with imageJ software

Eligibility criteria:
Inclusion Criteria:

* Patients age\> 18 years old.
* patients schedule for rhinoplasty surgery

Exclusion Criteria:

* Pervious nasal trauma that affect anatomical land mark
* pervious nasal surgery (rhinoplasty or others)
* patients with psychological disorders.
* patients with any coagulopathy disorders

Outcomes:
Agreement between ImageJ and AI application measurements; Evaluate the accuracy of AI-based simulation in predicting postoperative aesthetic outcomes following structural rhinoplasty by comparing AI-generated preoperative simulations with actual postoperative nasal morphology using objective digital image a
Pilot Evaluation for SMartphone-adaptable Artificial Intelligence for PRediction and DeTection of Left Ventricular Systolic Dysfunction
Trial ID: NCT05630170
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-09-13
Completion date: 2024-05-22
Sponsor: Yale University
Conditions: Left Ventricular Systolic Dysfunction
Interventions: AI-ECG
Locations: Yale New Haven Hospital
Countries: United States
Enrollment: 10
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this pilot study is to evaluate the prospective performance of an image-based, smartphone-adaptable artificial intelligence electrocardiogram (AI-ECG) strategy to predict and detect left ventricular systolic dysfunction (LVSD) in a real-world setting.

Eligibility criteria:
Inclusion Criteria:

* Provision of signed and dated informed consent form.
* Stated willingness to comply with all study procedures and availability for the duration of the study

Exclusion Criteria:

* Patients who have undergone a prior echocardiogram.
* Patients with a prior diagnosis of left ventricular dysfunction, based on a documented low ejection fraction (EF) in the medical record.
* Patients with an intermediate predicted probability of low EF (10 to 80%)
* Patients with a prior diagnosis of heart failure as determined by International Classification of Diseases-10 diagnosis code for heart failure.
* Research opt-out patients

Outcomes:
Successful detection of asymptomatic LVSD by AI-ECG
Artificial Intelligent Clinical Decision Support System Simulation Center Study: Trust and Usefulness of Machine Learning Risk Stratification Tool for Acute Gastrointestinal Bleeding Using the Technology Acceptance Model
Trial ID: NCT05816473
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-05-23
Completion date: 2024-12-31
Sponsor: Yale University
Collaborators: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Conditions: Gastrointestinal Hemorrhage
Interventions: LLM
Locations: Yale New Haven Hospital
Countries: United States
Enrollment: 106
Age range: None - None
Sex: ALL

Summary:
The purpose of this research study is to measure the effect on of a large language model interface on the usability, attitudes, and provider trust when using a machine learning algorithm-based clinical decision support system in the setting of bleeding from the upper gastrointestinal tract (upper GIB). Specifically, the investigators are looking to assess the optimal implementation of such machine learning algorithms in simulation scenarios to best engender trust and improve usability. Participants will be randomized to either machine learning algorithm alone or algorithm with a large language model interface and exposed to simulation cases of upper GIB.

Eligibility criteria:
Inclusion Criteria:

* Internal Medicine residency trainees at study institution
* Emergency Medicine residency trainees at study institution

Exclusion Criteria:

* N/A

Outcomes:
Change in Attitudes Towards Machine Learning Algorithms in Clinical Care using UTAUT; Clinician Decision Making of Triage of GI bleeding
System-integrated Point-of-Care Ultrasound in Breast Cancer Screening
Trial ID: NCT07462351
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-11
Completion date: 2026-12-31
Sponsor: Region Skane
Collaborators: Arba Minch University
Conditions: Breast Cancer
Interventions: AI
Locations: Arba Minch University and health care facilities and markets in the Gamo Zone
Countries: Ethiopia
Enrollment: 4800
Age range: 18 Years - None
Sex: FEMALE

Summary:
To assess the feasibility, safety, and clinical performance of community-based breast cancer screening incorporating clinical breast exam and short-termed trained examiners performing AI-supported breast POCUS for triage in limited-resource settings.

Eligibility criteria:
Inclusion Criteria:

* Asymptomatic women from the age of 30 with no upper age limit
* Symptomatic women 18 years or older.

Exclusion Criteria:

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

Outcomes:
Sensitivity
Computer-aided Gastric Lesion Localization and Miss Rate of Gastric Neoplasms: a Tandem, Randomized Controlled Study
Trial ID: NCT06495645
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-01
Completion date: 2026-12-31
Sponsor: The University of Hong Kong
Conditions: Gastric Neoplasm; Artificial Intelligence
Interventions: AI-assisted upper gastrointestinal endoscopy
Locations: Queen Mary Hospital, the University of Hong Kong
Countries: Hong Kong
Enrollment: 1000
Age range: 40 Years - None
Sex: ALL

Summary:
This prospective randomized trial compares AI-assisted upper gastrointestinal endoscopy with high definition upper gastrointestinal endoscopy in term of missed rate of gastric neoplasm. The investigators hypothesize the miss rate of high definition upper gastrointestinal endoscopy is higher than AI-assisted upper gastrointestinal endoscopy.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 40 or older
* Scheduled for elective upper endoscopy

Exclusion Criteria:

* Pregnant women,
* Inability to provide written informed consent
* Prior gastrectomy, and
* Patients deemed unsuitable or high-risk for endoscopy with severe comorbid illnesses

Outcomes:
Gastric neoplasia miss rate
Short-term Outcome of Artificial Intelligence-assisted Preoperative Three-dimensional Planning of Total Hip Arthroplasty for Developmental Dysplasia of the Hip Compared With Traditional Surgery
Trial ID: NCT05929105
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-01-04
Completion date: 2022-10-30
Sponsor: General Hospital of Ningxia Medical University
Conditions: Developmental Dysplasia of the Hip
Interventions: artificial intelligence (AI)-assisted three-dimensional preoperative planning technology; X-ray film template for 2D planning
Locations: General Hospital of Ningxia Medical University
Countries: China
Enrollment: 61
Age range: None - None
Sex: ALL

Summary:
In this study, the investigators retrospectively collected DDH case data of THA assisted by AI 3D planning and THA assisted by traditional planning. The compliance between preoperative planning and the type of prosthesis used in the operation, the position of the acetabular prosthesis, the correction of LLD and the postoperative joint function score were analysed to evaluate the short-term effect of AI combined with preoperative 3D planning in THA for DDH.

Eligibility criteria:
Inclusion Criteria:

* DDH was diagnosed by clinical signs and imaging examination, and a primary THA was planned;
* the patient had persistent unilateral hip pain, which seriously affected their quality of life;
* the standard posterolateral approach was used;
* the contralateral hip was normal or had received THA;
* the proximal femoral Dorr classification was type A or B;
* The acetabular classification was Crowe type Ⅰ, Ⅱ, Ⅲ or IV;
* all patients were treated with the bioartificial hip joint PINNACLE® cup or SUMMIT® stem, developed by Johnson \& Johnson Biological.

Exclusion Criteria:

* preoperative and postoperative imaging examinations did not meet the evaluation criteria (non-standard double-hip anteroposterior film, acetabular angle and LL length could not be accurately measured);
* severe osteoporosis, tumour or metabolic disease around the affected hip joint;
* spinal deformity by other causes or a history of lumbar internal fixation;
* external deformity of the affected hip joint in the lower extremity;
* neuromuscular insufficiency (with hip abduction weakness and poliomyelitis);
* severe disease with intolerance to surgery.

Outcomes:
Type of acetabular cup and femoral stem; Difference in the length of lower limbs before and after surgery