moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Epigenetics and Prevention of Non-communicable Diseases in Kazakhstan: a Personalized Approach and Biological Age Prediction
Trial ID: NCT06953180
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-03-03
Completion date: 2026-12-31
Sponsor: Asfendiyarov Kazakh National Medical University
Conditions: Cardiovascular Diseases (CVD); Type 2 Diabetes; Chronic Respiratory Diseases; Obesity (Disorder); Chronic Kidney Diseases
Interventions: Genetic: DNA analysis
Locations: Asfendiyarov Kazakh National Medical University
Countries: Kazakhstan
Enrollment: 6720
Age range: 18 Years - 69 Years
Sex: ALL

Summary:
This study aims to enhance personalized and preventive care for non-communicable diseases (NCDs) in Kazakhstan by examining epigenetic factors, predicting biological age and reproductive function using machine learning, and developing health improvement recommendations.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18 to 69 years.
* Residents of 17 regions of Kazakhstan.
* Willingness to participate and provide informed consent.

Exclusion Criteria:

* Age less than 18 years old or over 69 years old.
* Failure to provide informed consent or incomplete participation in data collection procedures.

Outcomes:
Accuracy of Machine Learning Model for Predicting Biological Age; Accuracy of Reproductive Function Prediction Model
Accuracy Study of an AI-based, Real-time Anatomy Identification Tool for Use in Ultrasound-guided PENG (Pericapsular Nerve Block) and Suprainguinal Fascia Iliaca Blocks
Trial ID: NCT06283485
Status: WITHDRAWN
Study type: OBSERVATIONAL
Start date: 2023-12-15
Completion date: 2025-04-25
Sponsor: Konya City Hospital
Collaborators: Betül Afşar
Conditions: Artificial Intelligent; Ultrasound Therapy; Complications; Nerve Block
Interventions: ultrasound examination
Locations: Yasin Tire
Countries: Turkey (Türkiye)
Enrollment: 0
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
Background and rationale: Ultrasound-guided regional anesthesia is a widely used pain control method today. A critical aspect of the procedure is accurate visualization of anatomical structures on ultrasound to precisely define target areas. Distinguishing surrounding tissues with an imaging model that automatically recognizes sonoanatomy in ultrasound images will reduce unintended intraneural injections or injury to other anatomical structures in close proximity and increase patient safety.

Research question; How can we improve the ultrasound images we frequently use in regional blocks by integrating them with artificial intelligence to reduce complications and improve applications? And what is the accuracy of the developed artificial intelligence support during imaging?

Research purpose; This work; We aim to further increase the safety of different regional block positions, minimize the risk of complications, and improve ultrasound visualization by developing an artificial intelligence model (AI Model-Artificial Intelligence) that automatically identifies and segments anatomical landmarks, provides visual guidance for inexperienced colleagues, and improves the performance of the developed model during application. aims to demonstrate its accuracy.

Hypothesis; Numerous studies have shown that the use of ultrasound and neurostimulators in practice increases the success, onset and quality of nerve blocks, but due to the low incidence of major complications and the absence of comparable randomized studies, no definitive statement can be made as to whether ultrasound reduces the overall rate of nerve damage. An imaging model that automatically marks sonoanatomy with artificial intelligence in ultrasound images can reduce unintended intraneural injections or injury to other anatomical structures in close proximity and improve patient safety.

Eligibility criteria:
Inclusion Criteria:

* Agreeing to take ultrasound images
* Healthy - No comorbidities
* Adult individuals between the ages of 18-65

Exclusion Criteria:

* Those who do not accept ultrasound images
* Individuals under 18 years of age
* with comorbidities
* Pregnancy

Outcomes:
Artificial intelligence Program size; Score of assessment the pictures
Artificial Intelligence Based Colorectal Polyp Histology Prediction by Using Narrow-band Imaging Magnifying Colonoscopy
Trial ID: NCT04425941
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2014-01-05
Completion date: 2020-05-31
Sponsor: Petz Aladar County Teaching Hospital
Conditions: Software Analysis on Polyp Histology Prediction
Interventions: artificial intelligence diagnosis
Enrollment: 373
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
Background We are developing artificial intelligence based polyp histology prediction (AIPHP) method to automatically classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the non-neoplastic or neoplastic histology of polyps.

Aim Our aim was to analyse the accuracy of AIPHP and NICE classification based histology predictions and also to compare the results of the two methods.

Methods We examined colorectal polyps obtained from colonoscopy patients who had polypectomy or endoscopic mucosectomy. Polyps detected by white light colonoscopy were observed then by using NBI at the optical maximum magnificent (60x). The obtained and stored NBI magnifying images were analysed by NICE classification and by AIPHP method parallelly. Pathology examinations were performed blinded to the NICE and AIPHP diagnosis, as well. Our AIPHP software is based on a machine learning method. This program measures five geometrical and colour features on the endoscopic image.

Eligibility criteria:
Inclusion Criteria:

* endoscopic diagnosis of colorectal polyp

Exclusion Criteria:

* colonoscopy result without polyps or IBD diagnosis

Outcomes:
Software accuracy of polyp histology prediction
Artificial Intelligence Guided Patient Selection for Atrial Fibrillation Catheter Ablation: Randomized Clinical Trial (AI-PAFA Trial)
Trial ID: NCT04997824
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-10-07
Completion date: 2031-06
Sponsor: Yonsei University
Conditions: Atrial Fibrillation
Interventions: Artificial Intelligence-based atrial fibrillation catheter ablation; Medical Therapy
Locations: Severance Hospital, Yonsei University Health System
Countries: South Korea
Enrollment: 1000
Age range: None - None
Sex: ALL

Summary:
Atrial fibrillation (AF) is a major cardiovascular disease with a prevalence of 1.7% of the total population in Korea, associated with 25% of ischemic stroke and 30% of heart failure, and is a major cardiovascular disease that doubles the risk of dementia. AF catheter ablation (AFCA) is an effective procedure that lowers the risk of heart failure mortality and cerebral infarction and improves cognitive or renal functions. However, the recurrence rate after the procedure is relatively high, especially in patients with long-standing persistent AF in which atrial remodeling has already progressed. Research on the prediction of treatment efficacy using artificial intelligence (AI) is being actively conducted around the world. We predicted the AFCA poor responders who will progress to permanent AF despite AFCA among a total of 3,372 patients included in the Yonsei AF Ablation cohort and the 2nd independent cohort with a long-term follow-up through AI with area under curve (AUC) 0.943. Therefore, in this prospective randomized clinical study, the difference between the patient selection for AFCA using AI algorithm and the clinical guidelines-based decision will be compared and evaluated in terms of long-term rhythm outcome.

Eligibility criteria:
Inclusion Criteria:

1. Left atrium size \< 55 mm
2. AF recurred during the administration of antiarrhythmic drugs or that antiarrhythmic drugs intolerable patients
3. Patients eligible for anticoagulant therapy (to prevent thromboembolic events)

Exclusion Criteria:

1. AF associated with severe cardiac anomalies or structural heart disease with hemodynamic influence
2. Patients who have difficulty in CT imaging using a contrast medium
3. Patients with active internal bleeding
4. Inappropriate anticoagulant therapy
5. Serious comorbidities
6. Patients expected to survive less than 1 year
7. People with drug or alcohol addiction
8. Those who cannot read the consent form (illiterate, foreigners, etc.)
9. Patients who are judged unsuitable for participation in clinical research by the judgment of other investigators

Outcomes:
Efficacy of Atrial Fibrillation Catheter Ablation(AFCA); safety of Atrial Fibrillation Catheter Ablation(AFCA)
Real-word Study of the de NOVO Use of Once-daily Extended-release tacrolimuS (LCPT) in Kidney Transplantation Using an ARTificial Intelligence Prognostic System: a Multicenter Prospective Ob-servational Study
Trial ID: NCT06960824
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05
Completion date: 2027-12-31
Sponsor: Chiesi SAS
Conditions: Chronic Renal Disease
Interventions: Life-Cycle Pharma Tacrolimus; Kidney transplantation
Locations: Chru Tours
Countries: France
Enrollment: 165
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to assess the clinical efficacy of the LCPT En-varsus \[Life-Cycle Pharma Tacrolimus\] initiated in de novo (Day 0 of transplantation) using the Biopsy Proven Acute Re-jection and iBox (full and abbreviated algo-rithms).

Eligibility criteria:
Inclusion Criteria:

* Kidney recipients aged 18 years and older.
* Kidney recipients were transplanted from living or deceased donors
* Patients receiving Envarsus® \[Life-Cycle Pharma Tacrolimus\] within 24 hours of kidney transplantation in a once-daily formulation at the discretion of the investiga-tor and in compliance with the summary of product characteristics.
* Patients are willing to participate in the study and do not oppose the use of their data.
* Patients are either affiliated with or beneficiaries of a social security scheme.

Exclusion Criteria:

* Contraindications to the use of Life-Cycle Pharma Tacrolimus (Envarsus®).
* Pre-transplant desensitization.
* ABO is incompatible with a living donor.
* Combined transplant (e.g. kidney-heart transplant, kidney-liver transplant).
* Previous bariatric (or bypass) surgery.
* Gastrointestinal problems are likely to affect the absorption of the Life-Cycle Pharma Tacrolimus (Envarsus®).
* Diagnosis of Parkinson's disease or any other neurological syndrome causing tremor.
* Patients who are part of another clinical trial or in a period of exclusion from another clinical trial with a molecule under study.
* Pregnant women or breastfeeding.
* Patients under legal protection (guardianship, curatorship) or deprived of their liberty through judicial or administrative decisions.
* Patients under State Medical Assistance.

Outcomes:
Incidence of Biopsy Proven Acute Rejection
Development and Validation of a Machine Learning Algorithm for Prediction of Complete Neuromuscular Recovery in Adult Surgical Patients
Trial ID: NCT05471882
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-03-01
Completion date: 2027-01-01
Sponsor: University Hospital Ulm
Collaborators: Technical University of Munich
Conditions: Residual Paralysis, Post Anesthesia; Postoperative Complications; Neuromuscular Blockade; Quantitative Neuromuscular Monitoring
Locations: University Hospital Ulm; Technical University Munich
Countries: Germany; Germany
Enrollment: 240000
Age range: 18 Years - None
Sex: ALL

Summary:
Despite emerging efforts to decrease residual paralysis and postoperative complications with the use of quantitative neuromuscular monitoring and reversal agents their incidences remain high. In an optimal setting, neuromuscular blocking agents are dosed in a way that there is no residual block at the end of surgery. The effect of neuromuscular blocking agents, however, is highly variable and is not only influenced by their dose, but also by several patient-related factors such as muscle status, metabolic activity, and anesthesia management. Accordingly, the duration of action is difficult to predict.

The PINES project will use artificial intelligence methods to develop a model that can accurately predict the course of action of neuromuscular blocking agents. It will be used to predict time to complete neuromuscular recovery (train-of-four \[TOF\] ratio \>0.9) and may provide as a decision support in the individual management of timing and dosing of neuromuscular blocking drugs and their reversal agents.

In a secondary analysis, the association between the choice of neuromuscular blocking agent and postoperative pulmonary complications will be evaluated.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (≥18 years) undergoing non-cardiac surgery receiving general anesthesia with intraoperative neuromuscular blocking agent administration and available TOF data.

Exclusion Criteria:

* none

Outcomes:
complete neuromuscular recovery
PRognOstiC valuE of rEd Density in Ulcerative Colitis: PROCEED-UC STUDY
Trial ID: NCT04408703
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-08-03
Completion date: 2022-06-01
Sponsor: Universitaire Ziekenhuizen KU Leuven
Collaborators: Sapporo Medical University; Imelda GI Clinical Research Center
Conditions: Ulcerative Colitis
Interventions: Red Density
Locations: Imelda GI clinical research center; UZ Leuven
Countries: Belgium; Belgium
Enrollment: 243
Age range: 16 Years - None
Sex: ALL

Summary:
Observational prospective multicenter study: baseline assessement of the disease activity by an automated endoscopic tool and follow up of 52 weeks to evaluate sustained clinical remission.

Eligibility criteria:
Inclusion Criteria:

* Confirmed diagnosis of UC for at least 3 months
* Clinical remission with SCCAI \<3 at baseline and stable remission for the last 3 months
* Ability to give informed consent by the patient or legal representative in case of minority.

Exclusion Criteria:

* Any contraindication for sigmoidoscopy or undergoing biopsies of the rectosigmoid.
* Uncontrolled coagulopathy.
* Any planned change in UC related treatment (both escalation and de-escalation) after the baseline endoscopy
* Planned UC related surgery
* Previous subtotal or total colectomy
* Short bowel, ileostomy or colostomy
* Pregnancy at baseline
* Age younger than 16 years

Outcomes:
sustained clinical remission
Development and Applications of Daily-use Fall Risk Assessment Device to Prevent Elderly People From Falling
Trial ID: NCT05693480
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-07-01
Completion date: 2024-10-30
Sponsor: The University of Hong Kong
Collaborators: Aberdeen Kai-fong Welfare Association; Hong Kong Young Women's Christian Association
Conditions: Fall; Balanced
Interventions: Balance sensor
Locations: Name: The University of Hong Kong
Countries: Hong Kong
Enrollment: 1300
Age range: 60 Years - 80 Years
Sex: ALL

Summary:
This research attempts to develop an artificial intelligence (AI) enabled device for measuring the dynamic balance ability of older people with a sensor using an optical principle called Frustrated Total Internal Reflection. The AI-based algorithm embedded in the device performs the data analysis for balance ability assessment and falling risk prediction. As a critical part of the research, a large-scale user study is needed to test the validity of the device regarding the dynamic balance ability assessment and the accuracy of the falling risk prediction provided by the device. Also, we plan to study the factors influencing user engagement in this device through the questionnaire-based survey and interview.

Eligibility criteria:
Inclusion Criteria for reference group participants:

* Age ≥ 60
* Ability to communicate with experiment operators (in Chinese or English)
* Ability to provide informed consent to participate after being given information about the experiment and other information that the participant must know to participate

Exclusion Criteria for reference group participants:

• Do not fall in the past twelve months

Inclusion Criteria for intervention group participants:

* Age ≥ 60
* Ability to communicate with experiment operators (in Chinese or English)
* Ability to provide informed consent to participate after being given information about the experiment and other information that the participant must know to participate

Exclusion Criteria for intervention group participants:

* Inability to give written informed consent (e.g., illiterate or with cognitive impairment)
* Inability to stand still without any assistance for 30 seconds

Outcomes:
Precision assessment of falling risk prediction
PARamedical Abnormalties Detection of Traumatic Bone Lesion of the extremitIeS
Trial ID: NCT05076188
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-03-01
Completion date: 2021-09-01
Sponsor: Poitiers University Hospital
Conditions: Radiography; Emergencies; Diagnostic
Interventions: Emergency radiography
Locations: C.H.U. de Poitiers
Countries: France
Enrollment: 19
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
This study assesses the performance of radiographers in detecting radiological anomalies of the appendicular skeleton in emergency department. This is a retrospective study comparing the radiographers' diagnostic performance before and after dedicated training, assisted or not by artificial intelligence software. All performances will be evaluated and compared.

Eligibility criteria:
Inclusion Criteria:

* Working in an emergency radiography department Volunteer to participate in the study

Exclusion Criteria:

* Planned departure from the establishment within 12 months.

Outcomes:
Evaluate the radiographers' diagnostic performance to issue an advisory opinion to X-ray reading of the appendicular skeleton in emergency department
Artificial Intelligence-assisted Screening of Malignant Pigmented Tumors on the Ocular Surface
Trial ID: NCT05645341
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-12-05
Completion date: 2023-06-05
Sponsor: Sun Yat-sen University
Conditions: Orbital Neoplasms; Conjunctival Neoplasms; Eye Neoplasms
Interventions: screening system for ocular surface malignant tumors
Locations: Zhognshan Ophthalmic Center, Sun Yat-sen University
Countries: China
Enrollment: 535
Age range: None - None
Sex: ALL

Summary:
Rare diseases generally refer to diseases whose prevalence rate is lower than 1 / 10 000 and the number of patients is less than 140000. Rare diseases are generally faced with the dilemma of a lack of qualified doctors, difficulty in large-scale screening, and a lack of rapid and effective channels for medical treatment. Studies have shown that 42% of patients say they have been misdiagnosed, and each patient with a rare disease needs to go through an average of eight doctors in seven years to see a corresponding rare disease specialist. More importantly, most rare diseases seriously affect the health and quality of life of patients. The ocular surface malignant tumor is a typical rare disease, and its incidence is less than 1 / 100000. The ocular surface not only affects the patient's appearance, but also damages the visual function, and the malignant tumor may even affect life. These uncommon malignant tumors are often hidden in the common black nevus on the eye surface, which is easy to be ignored and has great potential risks. With the improvement of people's living standards, people start to pay attention to rare diseases.

In recent years, the rapid development of digital technology has also provided new opportunities for the prevention and treatment of rare diseases. Our team established the database of rare ophthalmopathy in China in the early stage, which provided a solid foundation for the digitization of precious clinical data. This study intends to develop an intelligent screening system for ocular surface malignant tumors, using the mobile phone for real-world verification and scale screening, and explore it to improve the ability of doctors to diagnose and treat rare diseases. This study is expected to improve the ability to screen malignant tumors on the ocular surface and provide a novel model for the universal screening of rare diseases.

Eligibility criteria:
Inclusion Criteria:

* Dark-brown lesions on the ocular surface are found: i.e. ocular surface malignant melanoma, ocular basal cell carcinoma, conjunctival nevus, eyelid nevus, sclera pigmentation, benign eyelid keratosis

Exclusion Criteria:

* Non-pigmented ocular surface tumors: pterygium, corneal dermoid tumor, meibomian gland cyst, cataract, blepharitis, etc.
* The image quality does not meet the clinical requirements.

Outcomes:
Area under the curve (AUC)
Monitoring Hyperkalemia Using Point-of-care AI-enabled ECG Device Among Patients With Chronic Kidney Disease Stages 3b-5 to Reduce Rate of Hyperkalemia - a Practical Randomized Clinical Trial
Trial ID: NCT06901960
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-02
Completion date: 2026-12-31
Sponsor: Peking University First Hospital
Conditions: Chronic Kidney Disease (Stages 3b-5)
Interventions: A mobile artificial intelligence-enhanced electrocardiogram device estimating plasma potassium categories (<5 mmol/L, 5 - <5.5 mmol/L, 5.5 - <6.0 mmol/L and ≥ 6.0 mmol/L)
Locations: Peking University First Hospital; The First Affiliated Hospital of Guangxi Medical University; Hubei Provincial Hospital of TCM
Countries: China; China; China
Enrollment: 1066
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
The clinical trial will be conducted in a real-world outpatients setting with the goal of learning if monitoring serum potassium frequently among patients with moderate-to-advanced stages of chronic kidney disease leading to optimal management of hyperkalemia (the reduced occurrence of serum potassium ≥ 5.0 mmol/L during 6 months of follow-up). It will also learn about the adherence of using a mobile monitoring device.

Participants will:

* Measure their serum potassium using an AI-enhanced point-of-care ECG device at least twice per week
* Receive health education prompts to reduce intake of potassium rich foods if their electrocardiogram device measured serum potassium is abnormal
* Receive alerts to visit nephrology clinic if their electrocardiogram device measured serum potassium is continuously abnormal

Eligibility criteria:
Inclusion Criteria:

* Patients with chronic kidney disease stages 3b-5 (eGFR: 10 - \<45 ml/min/1.73m²)
* Patients with regular visits in the study sites to manage their chronic kidney disease (≥ 1 visit per 3 months during the past year before enrollment)
* Willingness to participate in the study with signed informed consent

Exclusion Criteria:

* Patients already under kidney replacement therapy or will receive kidney replacement therapy in 6 months
* Unwillingness or without the ability to monitor hyperkalemia using the study device
* Patients with heart pacemaker implanted

Outcomes:
Incidence of serum potassium ≥ 5.0 mmol/L