moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Validation of an Artificial Intelligence System for Postoperative Management of Cataract Patients: A Clinical Trial
Trial ID: NCT04138771
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2013-01-01
Completion date: 2020-03-01
Sponsor: Sun Yat-sen University
Collaborators: Ministry of Health, China; Xidian University
Conditions: Cataract; Postoperative Complications; Artificial Intelligence
Interventions: An artificial intelligence system for postoperative management of cataract patients
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen University
Countries: China
Enrollment: 300
Age range: None - None
Sex: ALL

Summary:
Cataract surgery is the current standard of management for cataract patients, which is typically succeeded by a postoperative follow-up schedule. Here, the investigators established and validated an artificial intelligence system to achieve automatic management of postoperative patients based on analyses of visual acuity, intraocular pressure and slit-lamp images. The management strategy can also change according to postoperative time.

Eligibility criteria:
Inclusion Criteria:

* Patients who had surgery of cataract extraction combined with introcular lens implantation.
* Patients should be aware of the contents and signed for the informed consent.

Outcomes:
The proportion of accurate, mistaken and miss detection of this artificial intelligence diagnostic system.
Comparative Evaluation of the Performance of an Artificial Intelligence Algorithm for the Interpretation of Traumatic Bone Radiographs
Trial ID: NCT07329881
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-11-01
Completion date: 2026-03-31
Sponsor: Hôpital Universitaire Sahloul
Conditions: Trauma (Including Fractures)
Locations: CHU sahloul
Countries: Tunisia
Enrollment: 500
Age range: None - None
Sex: ALL

Summary:
This diagnostic study aims to compare the performance of an artificial intelligence (AI) algorithm designed to assist in the interpretation of traumatic bone radiographs (all anatomical regions excluding the thorax) with that of human readers, including emergency medicine and family medicine residents as well as senior physicians (one emergency medicine specialist and one orthopedic surgeon).

The study follows a paired reader study design: identical anonymized radiographic images are independently interpreted by the AI system and by human readers. The reference standard ("gold standard") will be defined by the consensus reading of the two senior physicians. Inter-observer agreement (kappa statistics) between the AI, residents, and senior reference readings will be estimated, and false negatives and false positives will be analyzed by lesion type and anatomical location.

Eligibility criteria:
Inclusion Criteria:

* Standard radiographs performed for suspected bone trauma (upper limbs, lower limbs, pelvis/hip, cervical/thoracolumbar spine) in the emergency setting.
* Adult and pediatric patients (informed consent by patient or legal guardian for minors).
* Acute trauma context.

Exclusion Criteria:

* Poor-quality images (motion artifact, underexposure preventing diagnostic interpretation).
* Cases lacking senior interpretation for gold standard reference.
* Patients referred with radiographs performed outside the hospital (external facilities).

Outcomes:
Diagnostic sensitivity and specificity of the AI algorithm compared to human readers
Strategies to Stop Acute Kidney Injury (AKI)-Acute Kidney Disease (AKD) -Chronic Kidney Disease (CKD) Continuum - Policy The Intensified AKD Care to Reduce CKD
Trial ID: NCT04145609
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-01-01
Completion date: 2021-12-31
Sponsor: Taipei Medical University Shuang Ho Hospital
Collaborators: National Health Research Institutes, Taiwan; Taipei Medical University
Conditions: Acute Kidney Injury; Renal Insufficiency, Chronic; Acute Kidney Disease; Decision Support Systems, Clinical
Interventions: Multidisciplinary team (MDT) care and Acute kidney disease (AKD) clinic
Enrollment: 690
Age range: 20 Years - None
Sex: ALL

Summary:
Strategies to stop AKI-AKD-CKD continuum - Policy is one of the collaborative projects, Strategies to stop AKI-AKD-CKD continuum, Epidemiology, Immunology, Repair, Artificial intelligence, and Policy (EIRAP). It is aimed to study effective interventional strategies that lower the incidence of CKD among patients with AKD. The intensified AKD care to reduce CKD (ISACC trial) is a prospective, open-labeled, randomized controlled trial is designed to evaluate the efficacy of multidisciplinary team care (MDT) model and acute kidney disease (AKD) clinic visits

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 20 yrs
* Severe AKD: Stage 2, Stage 3 and Dialysis-requiring AKD (AKD-D)

Exclusion Criteria:

* Pregnancy

Outcomes:
Proportion of Major adverse kidney event
A Multicenter Prospective Study to Develop and Validate an Artificial Intelligence-Based Electrocardiogram Model for the Diagnosis of Acute Type A Aortic Dissection in Patients Presenting With Chest Pain
Trial ID: NCT07536932
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04
Completion date: 2026-12
Sponsor: Shanghai Zhongshan Hospital
Collaborators: Yan'an Affiliated Hospital of Kunming Medical University; Taian City Central Hospital; Mianyang Central Hospital; Guangdong Provincial People's Hospital
Conditions: Aortic Dissection Type A; Chest Pain
Enrollment: 10000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The goal of this prospective multicenter observational study is to learn whether an artificial intelligence model based on electrocardiograms (ECGs) can help diagnose acute type A aortic dissection (TAAD) in adults who come to the emergency department with chest pain or related symptoms. The main question it aims to answer is:

Can the AI-ECG model accurately distinguish TAAD from other causes of chest pain in a real-world emergency setting? Researchers will compare the AI model's ECG-based predictions with the final diagnosis confirmed by computed tomographic angiography (CTA), which is the reference standard. Participants will undergo routine emergency ECG testing and subsequent diagnostic evaluation as part of standard care. Clinical and ECG data will be collected from five tertiary hospitals, and the model's diagnostic performance will be assessed across centers.

Eligibility criteria:
Inclusion Criteria:

* Male or female emergency department patients aged 18-80 years;
* Clear presentation of chest pain or related chest/back pain;
* Completion of standard 12-lead electrocardiography (ECG) within 24 hours after onset of chest pain;
* ECG signal quality meeting the following criteria: QRS amplitude ≥ 0.1 mV and noise proportion \< 20%;
* Availability of subsequent diagnostic workup confirming whether the patient had acute type A aortic dissection (TAAD) or another definitive diagnosis.

Exclusion Criteria:

* Poor-quality ECG recordings, defined as missing leads in ≥ 3 leads or severe baseline instability;
* Indeterminate final diagnosis;
* History of prior surgery involving the aortic valve, aortic root, or ascending aorta.

Outcomes:
Diagnostic performance of the AI-based electrocardiogram model for acute type A aortic dissection
Artificial Intelligence-assisted Colonoscopy for Detection of Colon Polyps: a Prospective Randomized Cohort Study
Trial ID: NCT04126265
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-09-01
Completion date: 2020-08-31
Sponsor: Side Liu
Conditions: Artificial Intelligence; Colonoscopy
Interventions: Artificial intelligence assisted colonoscopy
Locations: Nanfang Hospital
Countries: China
Enrollment: 560
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
All subjects shall sign informed consent before screening, and subjects shall be included according to inclusion and exclusion criteria.

A total of four endoscopists were included in the study, two in each group of senior endoscopists and two in each group of junior endoscopists.

Patients were randomly enrolled into the senior endoscopy group and the junior endoscopy group, and received artificial intelligence assisted colonoscopy and conventional colonoscopy successively. The two colonoscopy methods were performed back to back by different endoscopy physicians with the same seniority.

All patients were examined and treated according to routine medical procedures. The routine colonoscopy group and the artificial-intelligence-assisted colonoscopy group made detailed records of the patients' withdrawal time, entry time, number of polyps detected, polyp Paris classification, polyp size, polyp shape, polyp location and intestinal preparation during the colonoscopy process

Eligibility criteria:
Inclusion Criteria:

Chinese population aged 18-80 years old; Patients voluntarily signed informed consent form; In accordance with the indications of colonoscopy.

Exclusion Criteria:

(IBD) history of inflammatory bowel disease; History of colorectal surgery; Previous failed colonoscopy; Polyposis syndrome; Highly suspected colorectal cancer (CRC)

Outcomes:
Detection rate of small polyps (diameter < 6mm)
Data Collection for the Validation of an AI Software to Support Ultrasound-Guided Regional Anesthesia Procedures
Trial ID: NCT06878911
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-13
Completion date: 2025-02-23
Sponsor: Smart Alfa Teknoloji San. ve Tic. A.S.
Conditions: Ultrasound Imaging of Anatomical Structures
Interventions: Ultrasound Scans
Locations: AABP Integrative Pain Care and Wellness Clinic
Countries: United States
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
This single-center, prospective study is being conducted at AABP Integrative Pain Care and Wellness in Brooklyn, New York, USA. The aim of this study is to collect ultrasound images from healthy volunteers and evaluate the performance of the Nerveblox software using this image dataset. Nerveblox is a software as a medical device which is designed to assist anesthesiologist for ultrasound-guided regional anesthesia, prior to needling procedure.

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
* In good general health

Exclusion Criteria:

* Pregnancy
* Inability to lie to flat
* Any complaint or anatomical deformity on the regions to be scanned

Outcomes:
Performance assessment of anatomical landmark detection and highlighting of Nerveblox Software
Quantitative Evaluation of the Performance of a Deformable Registration Algorithm: Single-center Study Aimed at Implementing a New Off-line Adaptive Radiotherapy Technique in the Treatment of Breast Cancer by Tomotherapy
Trial ID: NCT05383144
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-06-27
Completion date: 2023-04-04
Sponsor: Institut de cancérologie Strasbourg Europe
Conditions: Breast Cancer
Interventions: Complete Positioning scans
Locations: Institut de cancérologie Strasbourg Europe
Countries: France
Enrollment: 40
Age range: 18 Years - None
Sex: FEMALE

Summary:
The radiotherapy treatment plan (also called dosimetry) used for all treatment sessions is based on the dosimetric scanner. During the sessions, the anatomy of the breast may vary, and these variations may impact the quality of the treatment. Adaptive radiotherapy is a new technique that allows these changes to be taken into account during treatment by automatically rescheduling the treatment for each session. The proposed trial aims to clinically evaluate one of these adaptive radiotherapy tools (PreciseART) based on deformable registration in order to determine if it can be used in daily practice in the treatment of breast cancer by tomotherapy. The trial will also clarify whether factors, such as duration of treatment, impact the quality of this algorithm.

Eligibility criteria:
Inclusion Criteria:

Female

* With invasive breast carcinoma (or axillary lymphadenopathy whose pathological examination is compatible with the origin of breast cancer but without an identified primary breast tumor)
* Breast surgery (conservative surgery or mastectomy)
* Treated by (neo)adjuvant chemotherapy or hormonotherapy
* eligible to normo-fractionated breast radiotherapy in 25 sessions of 2 Gray or 15 sessions of 2.67 Gray, associated or not to bed irradiation whatever the delivered dose
* eligible to an irradiation of sus-clavicular, inner mammary chain or axillary lymph nodes
* With helicoidal tomotherapy

Exclusion Criteria:

* Absence of indication for breast radiotherapy
* Absence of indication for lymph nodes irradiation
* partial irradiation of breast
* in situ carcinoma
* intranodal isolated tumor cells
* radiotherapy on prosthesis or surgically reconstructed breast
* antecedent of breast irradiation opr homolateral lymh node irradiation
* bilateral breast cancer

Outcomes:
Differences between dosimetric metrics on deformed and delineated contours.; Differences between dosimetric metrics on deformed and delineated contours.
Prospective Randomized Controlled Trial to Evaluate Locally Implemented Large Language Models (LLMs) for Simplifying Patient Communication in Hematology and Oncology
Trial ID: NCT07519811
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-01
Completion date: 2027-04-01
Sponsor: Technical University of Munich
Conditions: Hematologic Neoplasms; Oncologic Disorders
Interventions: LLM-Generated Plain-Language Patient Synopsis
Locations: Technical University Munich
Countries: Germany
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
This study tests whether patients with blood cancer or other cancers better understand their medical information when it is rewritten in plain language by an artificial intelligence (AI) system.

When patients are discharged from the hospital, they receive a medical letter summarizing their diagnosis, treatment, and next steps. These letters are often written in technical language that is difficult for patients to understand. In this study, an AI language model running on the hospital's own secure servers rewrites parts of this letter into simpler language. A physician checks the simplified version before the patient receives it.

Patients are randomly assigned to one of two groups. One group receives both the standard medical letter and the AI-simplified version. The other group receives the standard letter only. A separate group of patients who do not speak German well will receive a simplified and translated version.

After reading their letter, all participants fill out a short questionnaire about how well they understood the information. The study takes place at TUM University Hospital (Klinikum rechts der Isar) in Munich, Germany.

Eligibility criteria:
Inclusion Criteria:

* Age 18 years or older
* Inpatient of the Department of Medicine III (Hematology/Oncology) at TUM University Hospital (Klinikum rechts der Isar), Munich, Germany
* Receipt of a discharge letter including the sections Current Status, Medical History, Epicrisis, and Further Management as part of routine clinical care
* Capacity to provide informed consent
* Written informed consent following the consent procedure

Exclusion Criteria:

* Cognitive impairment precluding independent assessment of comprehension (e.g., dementia, severe encephalopathy)
* Participation in another study with potential influence on the study endpoints
* Lack of capacity to provide informed consent
* Refusal to participate in the study

Outcomes:
Patient Comprehension Score
Emotional Well-being and Biomarkers of Healthy Aging
Trial ID: NCT07334106
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-01
Completion date: 2028-08
Sponsor: Northwestern University
Collaborators: Potocsnak Human Longevity Lab
Conditions: Aging; Aging, Healthy; Aging Well; Aging, Biological
Interventions: Positive Emotion Skills Intervention
Locations: Potocsnak Human Longevity Lab
Countries: United States
Enrollment: 80
Age range: 40 Years - 70 Years
Sex: ALL

Summary:
This randomized control study is designed to understand the role of a positive psychology intervention (PARK: Positive Affect Regulation sKills) in biological aging and well-being. PARK provides a series of positive emotion skills in a self-guided online format, making the program accessible and convenient.

Our main aims are:

Aim 1: To explore the effects of PARK on psychological well-being (e.g., depression, anxiety, positive affect).

Aim 2: To explore the effects of PARK on biological age as defined by DNA methylation (DNAm) GrimAge and electrocardiogram-age (ECG-age), as well as physiological well-being in adults (e.g., cardiovascular, endocrine, musculoskeletal functioning, etc.).

Eligibility criteria:
Inclusion Criteria:

* Age 40-70
* Speaks and reads English
* Able to access the online platform through their phone, a public device (i.e., at the library) or at home.
* Lives in the Chicagoland area
* Willing/able to travel to the Human Longevity Lab for in-person visits

Exclusion Criteria:

* Adults unable to consent
* Individuals who are not yet adults (infants, children, teenagers)
* Pregnant women
* Prisoners
* Vulnerable Populations
* Pre-existing heart disease (prevalent coronary heart and cerebrovascular disease or heart failure)
* Type 2 diabetes
* Severe psychiatric illness

Outcomes:
Patient-Reported Outcomes Measurement Information System (PROMIS) Positive Affect (v1.0) and PROMIS Meaning and Purpose (v1.0); PROMIS Depression (v1.0), Anxiety (v1.0), Social Isolation (v2.0)
An AI-Assisted Art Therapy Co-Creation Intervention for Symptom Burden, Anxiety, and Emotional Regulation in Pediatric Chemotherapy Patients: A Randomized Controlled Trial
Trial ID: NCT07362615
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-20
Completion date: 2027-05-20
Sponsor: Gazi University
Conditions: Pediatric Cancer Chemotherapy-Related Symptoms Anxiety
Interventions: AI-based art-based program
Enrollment: 60
Age range: 10 Years - 18 Years
Sex: ALL

Summary:
This randomized controlled trial aims to evaluate the effects of an AI-assisted interactive art therapy co-creation intervention on symptom burden, state anxiety, and emotional regulation in pediatric patients receiving chemotherapy. The study will be conducted as a parallel-group randomized controlled trial at the Pediatric Hematology-Oncology units of Gazi University Health Application and Research Center between February 2026 and May 2027. A total of 60 children aged 10-18 years who are undergoing a new chemotherapy cycle will be randomly assigned to either an intervention group receiving a four-day AI-assisted art-based program or a control group receiving standard care. Outcome measures will be assessed before and after the intervention period. The study aims to provide evidence on the feasibility and effectiveness of a short-term, technology-assisted psychosocial intervention to support emotional well-being in children undergoing chemotherapy.

Eligibility criteria:
Inclusion Criteria:

* Be between 10 and 18 years of age
* Have been receiving chemotherapy treatment for at least 6 months
* Be between days 1 and 4 of a new chemotherapy cycle
* Be able to communicate in Turkish
* Be clinically stable
* The participant and their parent/guardian must consent to participate in the study and provide written consent

Exclusion Criteria:

* • Severe neurocognitive impairment, severe physical instability, or being under sedation

  * Experiencing a severe traumatic life event unrelated to the disease within the last 6 months
  * Being in the terminal phase clinically
  * Being under clinical observation due to a psychiatric diagnosis
  * Having fatigue, pain, or medical complications that would prevent participation in the intervention

Outcomes:
Emotional Regulation; Symptom Burden; State Anxiety
Predictive Model for Minimal Important Change After Rotator Cuff Repair Using Machine Learning Methods: A Pilot Study
Trial ID: NCT06145815
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-09-01
Completion date: 2023-11-01
Sponsor: La Tour Hospital
Conditions: Rotator Cuff Tears
Interventions: Arthroscopic rotator cuff repair
Locations: La Tour hospital
Countries: Switzerland
Enrollment: 4789
Age range: 18 Years - None
Sex: ALL

Summary:
There is little overall evidence behind clinical practice guidelines for diagnosis and treatment of rotator cuff repair. The purpose of this study was to compare the performance of different machine learning models that use pre-operative data from an international and multicentric database to predict if a patient that underwent rotator cuff repair could achieve the minimal important change (MIC) for single assessment numeric evaluation (SANE) at one year follow-up.

Eligibility criteria:
Inclusion Criteria:

* Primarily treated for rotator cuff tears by partial or complete surgical repair with a planned arthroscopic procedure
* Reparable tears
* No language barrier hindering questionnaire completion or legal incompetence were not included

Exclusion Criteria:

* missing pre- or post-operative single-assessment numeric evaluation (SANE)

Outcomes:
SANE score
A Study on Vertebral Bone Strength by Micro-CT-Like Image: An Approach Based on Conditional Generative Adversarial Nets
Trial ID: NCT04954417
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-05-01
Completion date: 2021-12-30
Sponsor: Peking University Third Hospital
Conditions: Osteoporosis
Interventions: Radiological scanning; Deep learning; Bio-mechanical testing
Locations: Peking University Third Hospital
Countries: China
Enrollment: 100
Age range: None - None
Sex: ALL

Summary:
In this study, we use conditional generation adversarial network to enhance the resolution of MSCT images and obtain micro-CT-like images. Based on this, we measure the bone structure indexes of micro-CT-like images and analyzed the correlation between bone structure and bio-mechanical indexes.

Eligibility criteria:
Inclusion Criteria:

* We will collect human formalin-fixed vertebral specimens provided by the Department of Anatomy, Peking University School of Medicine. The expected number of enrolled specimens is approximately 100 vertebrae (between T6 and L5) .

Exclusion Criteria:

* The vertebrae with significant compression fractures, bone neoplasms, or other causes of significant bone destruction.

Outcomes:
Consistency or correlation
Microbial Dental Plaque Analysis in Young Permanent Teeth Using Deep Learning in Children Aged 8-13 Years
Trial ID: NCT06603233
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-06-01
Completion date: 2023-11-01
Sponsor: Ankara Medipol University
Conditions: Dental Plaque (Diagnosis); Deep Learning
Interventions: The Difference Between The AI Model and Dentists Group; Deep Learning Models
Locations: Banu Çiçek Tez
Countries: Turkey (Türkiye)
Enrollment: 31
Age range: 8 Years - 13 Years
Sex: ALL

Summary:
Background: Dental plaque contributes to a number of common oral conditions such as caries, gingivitis, and periodontitis. As a result, detection and management of plaque is of great importance for the oral health of individuals. The primary objectives of this study were to design a deep learning model for the detection and segmentation of plaque in young permanent teeth and to evaluate the diagnostic accuracy of the model. Methods: The dataset contains 506 dental images from 31 patients aged 8 to 13 years. Six state-of-the-art models were trained and tested using this dataset. The U-Net Transformer model was compared with three dentists for clinical applicability using 35 randomly selected images from the test set.

Eligibility criteria:
Inclusion Criteria:

* Anterior young permanent teeth

Exclusion Criteria:

* Anterior young permanent teeth exhibiting disruptions in enamel tissue integrity such as decay
* Hypoplasia, hypomineralization
* Restored and prosthetically treated teeth
* Young permanent teeth located in the posterior region
* Primary teeth

Outcomes:
Determination of IoU and Dice Coefficient values among six state-of-the-AI models; Prediction scores of the dentists and U-Net Transformer on 35 test images