moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Combining Chest X-Ray Findings With Arterial Blood Gas Analysis for Generation of Machine Learning Model Assessing the Need for Mechanical Ventilation in Critically Ill Patients
Trial ID: NCT07001696
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06-01
Completion date: 2026-01-30
Sponsor: Zagazig University
Conditions: Respiratory Failure; Critical Illness; Mechanical Ventilation
Locations: Faculty of medicine, zagazig university
Countries: Egypt
Enrollment: 2160
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective cross-sectional study aims to develop and validate a machine learning model that combines chest X-ray findings with arterial blood gas (ABG) analysis to assess the necessity for mechanical ventilation in critically ill adult patients. Conducted at Zagazig University Hospitals, the study seeks to improve clinical decision-making by integrating radiological and biochemical data using artificial intelligence. The model's predictive performance will be evaluated against standard clinical assessments.

Eligibility criteria:
Inclusion Criteria:

Critically ill adult patients aged 18 years or older.

Patients assessed to require mechanical ventilation.

Control group: Age- and sex-matched critically ill patients not requiring mechanical ventilation.

Availability of both chest X-ray and arterial blood gas (ABG) analysis at the time of evaluation.

Exclusion Criteria:

Patients with missing or incomplete data (e.g., absent chest X-ray or ABG results).

Patients with chronic lung diseases unrelated to the current admission (e.g., COPD, pulmonary fibrosis).

Pregnant females.

Outcomes:
Accuracy of Machine Learning Model in Predicting the Need for Mechanical Ventilation
AI4Triage - Development of an Artificial Intelligence Based Methods for the Analysis of Triage Data
Trial ID: NCT07312968
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-11-01
Completion date: 2027-11-30
Sponsor: University of Catanzaro
Collaborators: Azienda Ospedaliera Universitaria Renato Dulbecco
Conditions: Triage
Interventions: Observation
Locations: University of Catanzaro
Countries: Italy
Enrollment: 1500
Age range: None - None
Sex: ALL

Summary:
Artificial intelligence, and in particular Graph Neural Networks (GNNs), have shown enormous potential in the analysis of complex clinical data. Thanks to their ability to model relationships between variables, GNNs represent a significant evolution compared to traditional models, enabling better interpretation of medical information and supporting data-driven decision-making in complex contexts such as emergency medicine.

The application of GNNs to clinical triage and to the prediction of length of stay can improve clinical efficiency by optimizing resource allocation and patient management. This observational study aims to evaluate the accuracy of predictions with respect to real clinical data, contributing to the development of advanced predictive tools to support healthcare decision-making processes.

Eligibility criteria:
Inclusion Criteria:

* all

Exclusion Criteria:

* Patients labelled with red codes and serious injuries.

Outcomes:
Numbers of Undertriaged; Undertriaged
A Study to Measure Underlying Coronary Stenosis; a Retrospective, Multi-center Study to Measure Efficacy of ECGio Against Multiple Reference Standards
Trial ID: NCT07375810
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-01
Completion date: 2026-09-01
Sponsor: Heart Input Output Inc
Conditions: Coronary Artery Disease (CAD)
Interventions: AI-ECG Analysis
Locations: Medstar Washington Hospital Center; Cena Research Institute
Countries: United States; United States
Enrollment: 978
Age range: 18 Years - 89 Years
Sex: ALL

Summary:
The study objective is to evaluate the effectiveness of the ECGio algorithm in predicting clinically significant coronary artery disease . ECGio's diagnostic performance during the trial will be compared against an objective performance ¬criteria using a mixed reference standard of quantitative coronary angiography and quantitative coronary computed tomography angiography in patients a general adult population under suspicion of coronary artery disease.

Eligibility criteria:
Inclusion Criteria:

1. Patients 18 years of age or older at time of data collection.
2. Patients with medical records stored in a digitized format.
3. Patients under suspicion of coronary artery disease (both suspicion of significant coronary artery disease as well as to rule out significant CAD) who present to the site with an electrocardiogram recorded up to 30 days prior to Coronary Computed Tomography Angiography.

Exclusion Criteria:

1. Patients with acute coronary syndrome.
2. Patients who previously underwent coronary artery bypass grafting.
3. Patients whose electrocardiogram tracing has extreme noise or artifact to the extent that it would be recommended to redo the tracing.
4. Patients with prior percutaneous coronary intervention resulting in stenting.
5. Unanalyzable invasive coronary angiogram.
6. Unanalyzable Coronary Computed Tomography Angiography.
7. Unanalyzable electrocardiogram signal.
8. Incomplete invasive coronary angiogram (e.g., only the right coronary artery was injected and visualized).
9. Patient core lab analyzed Coronary Computed Tomography Angiography showed ≥ 50% blockage in any vessel but patient was not referred to invasive coronary angiogram.

Outcomes:
Sensitivity & Specificity
Evaluation of Artificial Intelligence-Assisted Echocardiography (AI-echo) in the Early Diagnosis and Prognostic Stratification of Left Atrial Cardiomyopathy (LACM) in Patients With Acute Cardiac Disease
Trial ID: NCT07009639
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-01
Completion date: 2025-11-30
Sponsor: University of Calabria
Conditions: Atrial Cardiomyopathy
Locations: "Annunziata" Hospital
Countries: Italy
Enrollment: 45
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
Left atrial cardiomyopathy (LACM) is frequently underdiagnosed but plays a key role in increasing the risk of atrial fibrillation (AF) and thromboembolic events. While atrial strain is a validated marker of LACM, its measurement with conventional echocardiography can be time-consuming and less feasible in acute settings. The use of AI-assisted echocardiography (AI-echo) may help streamline image acquisition and analysis, offering faster and potentially more accurate assessment.

This study aims to compare the time required for atrial strain analysis using AI-echo versus standard methods. It also explores how changes in strain parameters (LASr, LASct, LAScd) relate to the onset of AF and in-hospital adverse outcomes, adjusting for comorbidities and conventional echo variables.

Main endpoints include time reduction with AI-echo and the association between strain changes and AF, complications, or mortality during hospitalization.

Eligibility criteria:
Inclusion Criteria:

1. Age between 18 and 85 years.
2. Hospitalization in CCU for acute cardiac pathology (e.g. acute coronary syndrome, acute or exacerbated heart failure, malignant arrhythmias, etc.).
3. Ability to provide written informed consent.

Exclusion Criteria:

1. Severe hemodynamic instability, history to contraindicate the execution of the echocardiographic examination.
2. Severely inadequate echocardiographic window that precludes atrial or ventricular morpho-functional assessment.
3. Inability to continue the study for clinical reasons, logistics or patient refusal.

Outcomes:
Echocardiographic acquisition times changes; Change in left atrial reservoir strain (LASr) between admission and discharge; Change in left atrial conduit strain (LAScd) between admission and discharge; Change in left atrial contractile strain (LASct) between admission and discharge Description; Correlation between atrial strain variation (ΔLASr, ΔLASct, ΔLAScd) and in-hospital heart failure worsening
Deep-learning Based Classification of Spine CT
Trial ID: NCT03790930
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-02-22
Completion date: 2020-05
Sponsor: Shanghai 10th People's Hospital
Collaborators: Third Affiliated Hospital, Sun Yat-Sen University
Conditions: Surgical Procedure, Unspecified
Interventions: deep learning
Locations: Shanghai Tenth People's Hospital
Countries: China
Enrollment: 500
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
It is time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal CT, which may also be correlated with high inter-observer variance. With the development of computer science, deep learning has emerged as a promising technique to classify images from individual level to pixel level. The main of the study is to automatically identify and classify the lesions, or segment targeted structures on spinal CT with deep learning.

Eligibility criteria:
Inclusion Criteria:

\- spinal thin layer CT

Exclusion Critera:

* medals or other implants induce artifact
* poor image quality

Outcomes:
classification accuracy; segmentation accuracy
Prediction of Disease Progression Using Machine Learning Based on Clinical Data in People With Multiple Sclerosis
Trial ID: NCT07292116
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-12-15
Completion date: 2026-03-01
Sponsor: Hüseyin Bektaş
Conditions: Prediction of Disease Progression; Multiple Sclerosis
Enrollment: 53
Age range: 18 Years - None
Sex: ALL

Summary:
Demographic and clinical data will be obtained from people with MS. Based on this data, the progression of the disease will be predicted with artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* Having been diagnosed with MS

Exclusion Criteria:

* be literate

Outcomes:
Fragility level
Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy
Trial ID: NCT05576506
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-10-08
Completion date: 2022-12-31
Sponsor: Shandong University
Conditions: Colorectal Neoplasms; Colorectal Polyp; Colorectal Adenoma; Colorectal SSA
Locations: Qilu hosipital
Countries: China
Enrollment: 86
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of colorectal cancer other colorectal disease by marking and analyzing the characteristics of hyperspectral images based on the pathological results of colonoscopic biopsy, so as to improve the objectiveness and intelligence of early colorectal cancer diagnosis.

Eligibility criteria:
Inclusion Criteria:

* patients aged 18-75 years who undergo the colonoscopy examination and biopsy

Exclusion Criteria:

* patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in colonoscopy
* patients with previous surgical procedures on the gastrointestinal tract.
* patients with contraindications to biopsy
* patients who refuse to sign the informed consent form

Outcomes:
Accuracy of HSI artificial intelligence model to identify colorectal adenoma and cancer; Sensitivity; Specificity; Negative predictive values(NPV); AUC (95% CI)
Improving Working Memory of Children With Autism Through Instruction on Rehearsal Strategy and Reinforcement
Trial ID: NCT04209452
Status: TERMINATED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-01-15
Completion date: 2020-11-10
Sponsor: Arizona State University
Collaborators: Shanghai Xuhui Clover Children Healthy Garden; Jingdezhen Kindkids Autism Rehabilitation and Training Center; Sunshine Special Education Center
Conditions: Autism
Interventions: Rehearsal strategy; Reinforcement; Rehearsal strategy and reinforcement
Locations: Jingdezhen Kindkids Autism Rehabilitation and Training Center; Sunshine Special Education Center; Shanghai Xuhui Clover Children Healthy Garden
Countries: China; China; China
Enrollment: 15
Age range: 4 Years - 8 Years
Sex: ALL

Summary:
Existing findings suggest that challenges in working memory (WM) of children with autism are common (Habib, Pollick, Melville, \& 2019). The WM malfunction can last across the lifespan and individuals with autism exhibit large WM impairments in both phonological and visuospatial domains (Habib, Harris, Pollick \& Melville, 2019). Despite the well-documented effects of WM impairments, little research has been dedicated to strategies for improving the working memory of children with autism.

Previous research found that individuals who verbally repeat a previously presented stimulus (i.e., rehearsal) tended to perform better than those who do not rehearse (Bebko, Rhee, Ncube, \& Dahary, 2017; Joseph, Steele, Meyer, \& Tager-Flusberg, 2005). In addition, among studies that investigated WM in children with autism, Baltruschat et al (2011a; 2011; 2012) conduct studies using positive reinforcement. Their results reveal that the positive reinforcement appeared to have produced better working memory.

The present study intends to investigate if training children with autism to use rehearsal strategy would improve their working memory. In addition, the investigators are also interested to see if reinforcement is a critical component that may provide additive effects beyond the effects of rehearsal. The investigators intend to randomly assign children to four different conditions: control, rehearsal, reinforcement, and rehearsal + reinforcement conditions. The results of this study should provide empirical evidence for practitioners to improve WM in children with autism.

Eligibility criteria:
Inclusion Criteria:

* have a diagnosis of autism
* have an echoic repertoire

Exclusion Criteria:

* problem behavior or noncompliance

Outcomes:
Change in percent recall
The Effect of An Artifıcial Intelligence-Supported Gamifıed Training Program on Nurses' Knowledge and Attitudes Toward The Prevention of Sharps Injuries
Trial ID: NCT07562867
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06-01
Completion date: 2027-06-01
Sponsor: Baskent University
Conditions: Sharps Injuries; Needlestick Injuries; Occupational Exposure
Interventions: Experimental: Intervention Group; control group
Enrollment: 36
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to evaluate the effect of a Health Belief Model (HBM)-based, artificial intelligence (AI)-supported gamified training program on nurses' knowledge and attitudes toward the prevention of sharps injuries. Sharps injuries remain a significant occupational risk for healthcare workers, particularly nurses, despite existing standard precautions.

The study will be conducted in two phases. In the first phase, the validity and reliability of the Sharps Injury Prediction Scale will be tested in a nurse population. In the second phase, a quasi-experimental pretest-posttest control group design will be used to assess the effectiveness of the intervention.

The study will be carried out in two hospitals from the same healthcare group located in different cities to prevent interaction between groups. A total of 36 nurses will be included, with 18 participants in the intervention group and 18 in the control group.

The intervention group will receive a structured, HBM-based training program consisting of seven sessions incorporating AI-supported content, gamified scenarios, interactive materials, and feedback mechanisms to enhance engagement and promote behavior change. The control group will receive routine institutional training on sharps injury prevention.

Data will be collected at baseline, immediately after the intervention, and two months later. Outcome measures include nurses' knowledge, attitudes toward safe sharps use, and sharps injury risk perception.

It is expected that the AI-supported gamified training program will significantly improve knowledge, attitudes, and risk awareness compared to routine training. The findings may support the integration of innovative, theory-based educational interventions into institutional training programs to enhance occupational safety.

Eligibility criteria:
Inclusion Criteria:

* Registered nurses working in the participating hospitals
* Working in the institution for at least 2 months (Phase 1) or newly employed within the last 1 month (Phase 2)
* Willing to participate in the study
* Able to use smartphone-based technologies
* Providing written informed consent

Exclusion Criteria:

* Having received prior training based on the Health Belief Model for sharps injury prevention
* Previous professional nursing experience before current employment (for newly recruited nurses in Phase 2)
* Being on leave during the data collection period
* Failure to attend training sessions (intervention group)
* Incomplete data collection forms
* Withdrawal from the study at any stage

Outcomes:
Nurses' knowledge level regarding sharps injury prevention
The Prediction of Anastomotic Insufficiency Risk After Colorectal Surgery (PANIC) Study: Development and External Validation of an International, Multicenter Machine Learning Algorithm for Prediction of Anastomotic Insufficiency After Colonic or Colorectal Anastomosis
Trial ID: NCT04985981
Status: SUSPENDED
Study type: OBSERVATIONAL
Start date: 2021-04-01
Completion date: 2022-12-31
Sponsor: Michel Adamina, MD
Collaborators: Cantonal Hospital of St. Gallen; University Hospital, Geneva; Clarunis - Universitäres Bauchzentrum Basel; University Hospital, Basel, Switzerland; University of Zurich
Conditions: Anastomotic Leak; Colorectal Cancer; Diverticulosis; Crohn Disease; Ulcerative Colitis; Mesenteric Ischemia
Locations: Clinical Research and Artificial Intelligence in Surgery, Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland; Kantonsspital Winterthur
Countries: Switzerland; Switzerland
Enrollment: 11000
Age range: 18 Years - None
Sex: ALL

Summary:
The Prediction of Anastomotic Insufficiency risk after Colorectal surgery (PANIC) study aims to establish a machine-learning-based application that allows for accurate preoperative prediction of patients at risk for anastomotic insufficiency after colon and colorectal surgery.

Eligibility criteria:
Inclusion Criteria:

* Patients who underwent colon or colorectal anastomosis for neoplasia, diverticulitis, mesenterial ischemia, iatrogenic or traumatic perforation, or inflammatory bowel disease

Exclusion Criteria:

* age \< 18
* recurrent colorectal cancer
* peritoneal carcinomatosis or unresectable metastatic disease at time of bowel resection
* informed consent not obtainable
* follow-up \< 6 weeks after surgery
* no reversal of and ostomy

Outcomes:
Occurrence of Anastomotic leak
Intraocular Lens Calculation Using Artificial Intelligence
Trial ID: NCT07202767
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-11-01
Completion date: 2028-10-30
Sponsor: Johannes Kepler University of Linz
Conditions: Cataract
Interventions: Swept-Source Anterior Segment OCT
Locations: Kepler University Clinic, Linz
Countries: Austria
Enrollment: 170
Age range: 21 Years - None
Sex: ALL

Summary:
The aim of this study is to investigate the lens diameter (LD) as a useful parameter in intraocular lens (IOL) power calculation by using several different non-invasive imaging techniques. The aim is to establish an accurate model for lens diameter estimation and subsequently evaluate the influence of the LD on the effective postoperative lens position. The comparability of the different devices in terms of variability between the biometric measurements will also be assessed.

By then combining these two approaches with artificial intelligence, the aim is to develop a new approach to effectively incorporate the LD in IOL power calculation and improve patient's outcome in the long run.

Eligibility criteria:
Inclusion criteria

* age 21 years and older
* planned uncomplicated bilateral cataract surgery
* availability, willingness, ability and sufficient cognitive awareness to comply with examination procedures and study visits
* ability to consent to the participation in the study
* signed informed consent

Exclusion criteria

* multifocal IOL
* combined surgery (cataract plus glaucoma/vitreoretinal/corneal surgery)
* PEX, previous ocular surgery, severe trauma or any pathology that could lead to an unstable capsular bag
* glaucoma or any other retinal disease that may affect visual acuity significantly
* pregnancy
* pre-operative visual acuity below 0.1 Snellen decimal (1.0 Log MAR)
* pupil diameter \<4mm

Outcomes:
Prediction model to estimate lens diameter with different measurement tools (swept-AS segment OCT; anterior segment-OCT and Placido topography with OCT-based anterior segment tomography; wave-front abberometry; swept-source OCT and optical biometer); Prediction model to estimate lens diameter with cerebral Magnetic Resonance Imaging
YOOMI: Effect of Gamified Physical Therapy Exercise Software on Inpatient Mobility
Trial ID: NCT05815017
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-11-29
Completion date: 2024-12
Sponsor: Rutgers, The State University of New Jersey
Conditions: Weakness, Muscle; Delirium in Old Age; Muscle Atrophy or Weakness; Muscle Loss; Hospital Acquired Condition; Physical Disability; Physical Inactivity
Interventions: Yoomi Physical Therapy Software
Locations: Robert Wood Johnson University Hospital
Countries: United States
Enrollment: 80
Age range: 65 Years - None
Sex: ALL

Summary:
Patients admitted to the hospital often develop functional impairments due to being in bed most of the day. Each day of bedrest leads to significant muscle loss. As a result, many patients become dependent on others or require rehabilitation at a facility to improve mobility and function prior to returning home. Staff in the hospital is limited and often unable to mobilize patients every day while hospitalized. The investigators are testing a new experimental gamified physical therapy exercise software to see if it can be a fun, enjoyable way to help mobilize patients without the assistance of staff. The primary aim of this pilot/proof of concept study is to determine whether gamified physical therapy software can help inpatients exercise within the safety of their own beds and preserve pre-hospitalization function.

Eligibility criteria:
Inclusion Criteria:

* \>= 65 years of age
* English-fluency in reading and speaking
* Capacity to consent
* Enrolled in HELP program
* Attending physician confirms there are no medical contraindications to in-bed exercises

Exclusion Criteria:

* Unstable Psychiatric Illness
* Unstable/Critically Ill Patients requiring ICU-level of care
* Surgical tube present (e.g. JP drains)
* Surgery during the hospitalization
* Bed Rest Activity Order

Outcomes:
Patient Satisfaction Score for Intervention Group Questionnaire (change is being assessed); Software Utilization Time for Intervention Group (change is being assessed); Percentage of exercise repetitions completed for Intervention Group (change is being assessed)