moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Remote Heart Rhythm Monitoring Using Photoplethysmography-based Smartphone Technology for the Early Detection of Atrial Fibrillation and Adverse Events After Cardiac Surgery
Trial ID: NCT05509517
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-11-02
Completion date: 2023-09-04
Sponsor: Universitaire Ziekenhuizen KU Leuven
Conditions: Atrial Fibrillation and Flutter; Atrial Fibrillation; Postoperative Complications; Arrhythmia
Interventions: Photoplethysmography based smartphone application for heart rhythm analysis (FibriCheck™)
Locations: University Hospitals Leuven
Countries: Belgium
Enrollment: 450
Age range: 18 Years - None
Sex: ALL

Summary:
The objective of the study is to evaluate the clinical consequences following the detection of postoperative atrial fibrillation or flutter (POAF) using a remote heart rhythm monitoring strategy with a photoplethysmography based smartphone technology in the early postoperative period after discharge.

Eligibility criteria:
Inclusion Criteria:

* Subject underwent any of (including a combination of) the following surgical procedures during index hospitalization. (coronary artery bypass graft, surgical repair or replacement of a cardiac valve)
* Subject provides informed consent
* Subject understands and agrees to comply with planned study procedures.
* Subject is able to perform heart rhythm measurement using the FibriCheck application at home.

Exclusion Criteria:

* Pacemaker dependent heart rhythm
* Permanent AF/Aflutter or AF/Aflutter present at the time of inclusion
* Insufficient cognitive or comprehensive level of Dutch to participate to the trial.
* No smartphone available at home.

Outcomes:
Proportion of participants with any of the following therapeutic interventions (composite 4-point endpoint); Time to detection of a postoperative adverse event
The Role of Artificial Intelligence in the Assessment of Multiparametric Resonance of the Prostate in Prostate Cancer Patients
Trial ID: NCT06298305
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-09-01
Completion date: 2024-12
Sponsor: IRCCS San Raffaele
Conditions: Prostate Cancer
Interventions: Quantib prostate (Quantib B.V. Rotterdam, The Netherlands) software
Locations: IRCCS Ospedale San Raffaele
Countries: Italy
Enrollment: 200
Age range: 18 Years - None
Sex: MALE

Summary:
This is a single center, blinded (the mpMRI will be read by expert personnel not aware of the patient's diagnosis ), retrospective study. A total of 200 patients with suspicious prostate cancer (PCa) who underwent, between January 2014 and January 2022, mpMRI of the prostate and subsequent prostate biopsy will identified. All mpMRI images will be retrospectively collected and evaluated using the Quantib Prostate software. In particular, 200 mpMRI with endorectal coil will be read by the expert radiologist and by the Quantib prostate (Quantib B.V. Rotterdam, The Netherlands) software, thus evaluating the predictive characteristics of Quantib Prostate for the identification of suspicious lesions. The sensitivity, specificity, negative predictive value, positive predictive value and accuracy of Quantib Prostate vs. radiologist in detecting csPCa will be evaluated using the biopsy report as reference standard.

Eligibility criteria:
Inclusion Criteria:

1. Men at least 18 years of age referred with clinical suspicion of prostate cancer
2. Men who have undergone mpMRI and subsequent targeted plus random systematic prostate biopsies
3. Available pathological report with details regarding the site of positive cores and grade group
4. Serum PSA ≤ 20
5. Patient who underwent a 1.5T mpMRI with or without endorectal coil with the following protocol restrictions and according to recent European Guidelines, each case containing at least the following sequences, will be considered:

T2w

* No fat suppression
* At least axial acquisition
* Max voxel size 1.0x1.0x3.0mm DWI
* At least on b-value ≥800 s/mm\^2 required, ≥1400 s/mm\^2 preferred
* ADC map required
* Max voxel size: 2.0x2.0x4.0mm
* Axial scanning protocol only DCE scan
* Recommended temporal resolution \<15s
* Axial scanning protocol only

Exclusion Criteria:

NA

Outcomes:
To evaluate the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in detecting csPCa of Quantib Prostate vs. traditional radiologist reading of mpMRI
Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits
Trial ID: NCT06245694
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2030-01-01
Sponsor: Medical University of Vienna
Conditions: Artificial Intelligence; Resource Allocation; Neurologic Manifestations
Locations: Emergency Department, Medical University Vienna
Countries: Austria
Enrollment: 50000
Age range: 18 Years - 120 Years
Sex: ALL

Summary:
Future predictive modeling in emergency medicine will likely combine the use of a wide range of data points such as continuous documentation, monitoring using wearables, imaging, biomarkers, and real-time administrative data from all health care providers involved. Subsequent extensive data sets could feed advanced deep learning and neural network algorithms to accurately predict the risk of specific health conditions. Moreover, predictive analytics steers towards the development of clinical pathways that are adaptive and continuously updated, and in which healthcare decision-making is supported by sophisticated algorithms to provide the best course of action effectively and safely. The potential for predictive analytics to revolutionize many aspects of healthcare seems clear in the horizon. Information on the use in emergency medicine is scarce.

Aim of the study is to evaluate the performance of using routine-data to predict resource usage in emergency medicine using the commonly encountered symptom of acute neurologic deficit. As an outlook, this might serve as a prototype for other, similar projects using routine medical data for predictive analytics in emergency medicine.

Eligibility criteria:
Inclusion Criteria:

* Female and Male subjects
* Age ≥ 18 years

Exclusion Criteria:

\- none

Outcomes:
Prediction model
Development of an Algorithm for Predicting Anatomical and Functional the Results of Therapy With Angiogenesis Inhibitors in Patients With Retinal Pigment Epithelium Detachments in Neovascular Age-related Macular Degeneration, Based on Primary Optical Coherence Tomography of the Macular Zone and Clinical Data.
Trial ID: NCT05208931
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-11-01
Completion date: 2022-09-01
Sponsor: The S.N. Fyodorov Eye Microsurgery State Institution
Conditions: Neovascular Age-related Macular Degeneration
Interventions: Anti-vascular endothelial growth factor therapy
Locations: The S.N. Fyodorov Eye Microsurgery State Institution
Countries: Russia
Enrollment: 300
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The study involves the development of an algorithm for predicting anatomical and functional results of therapy with angiogenesis inhibitors in patients with retinal pigment epithelium detachments in neovascular age-related macular degeneration, based on primary optical coherence tomography of the macular zone and clinical data.

Eligibility criteria:
Inclusion criteria:

* Linear B - scan through the macular area with the longest detachment
* Other pathologies

Exclusion criteria:

* Images without detachment
* Images on which it is possible to diagnose the need for therapy only in the presence of additional factors not considered in the study.

Outcomes:
Prediction algorithm
Acquisition and Frequency Spectroscopic Evaluation of Broadband Clinical Ultrasound Raw Data for Liver Cirrhosis and Focal Pathologies Using Neural Networks for Tissue and Pathology Differentiation
Trial ID: NCT06317181
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-04-01
Completion date: 2025-12
Sponsor: Technische Universität Dresden
Collaborators: University Hospital Dresden; University of Leipzig
Conditions: Artificial Intelligence; Ultrasonography; Elasticity Imaging Techniques; Liver Diseases; Metastasis to Liver
Interventions: Collection of elastography data; Collection of ultrasonic raw data
Locations: University Hospital; Diakonissen Hospital Dresden; University Hospital Halle (Saale); University Hospital Leipzig
Countries: Germany; Germany; Germany; Germany
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to test the performance of neuronal networks trained on ultrasonic raw Data (=radiofrequency data) for the assessment of liver diseases in patients undergoing a clinical ultrasound examination. The general feasibility is currently evaluated in a retrospective cohort.

The main questions the study aims to answer are:

* Can a neuronal network trained on RF Data perform equally good as elastography in the assessment of diffuse liver diseases?
* Can a neuronal network trained on RF Data perform better than a neuronal network trained on b-mode images in the assessment of diffuse liver diseases?
* Can a neuronal network trained on RF Data distinguish focal pathologies in the liver from healthy tissue?

To answer these questions participants with a clinically indicated fibroscan will undergo:

* a clinical elastography in Case ob suspected diffuse liver disease
* a reliable ground truth (if normal ultrasound is not sufficient e.g. contrast enhanced ultrasound, biopsy, MRI or CT) in case of focal liver diseases, depending on the standard routine of the participating center
* a clinical ultrasound examination during which b-mode images and the corresponding RF-Data sets are captured

Eligibility criteria:
Inclusion Criteria:

* scheduled for an ultrasound investigation by an independent physician
* signed declaration of consent

Exclusion Criteria:

* smaller interventions in the same liver during the last 2 Week (for example liver biopsy)
* contrast enhanced ultrasound less than a day ago
* major intervention at the liver (for example partial resection)

Outcomes:
Performance analysis of the trained model
The 1000 Eyes Lens Power Trial: Introducing Artificial Intelligence to Intraocular Lens Power Calculation
Trial ID: NCT05779319
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-02-01
Completion date: 2025-09-30
Sponsor: Johannes Kepler University of Linz
Collaborators: Mein Hanusch-Krankenhaus; Medical University of Graz; Landesklinikum Sankt Polten
Conditions: Cataract
Interventions: non contact measurement with the IOL Master 700
Locations: Kepler University Klinik Departement of Opthalmology
Countries: Austria
Enrollment: 1000
Age range: 21 Years - 110 Years
Sex: ALL

Summary:
The main purpose of the study is to develop an AI approach to improve intraocular lens power calculations.

Eligibility criteria:
Inclusion Criteria:

* Pseudophakic (at least one eye): cataract surgery between 4 weeks and 24 months prior to recruitment
* If both eyes would be suitable for the study only the right eye will be included, if only one eye is suitable for the study, this eye will be included
* Pre-operative swept-source OCT biometry available (no ultrasound measurement for axial eye length)
* Uncomplicated cataract surgery
* Age 21 years and older

Exclusion Criteria:

* Multifocal IOLs
* Combined surgery (cataract plus glaucoma/vitreoretinal/corneal surgery)
* Best corrected distance visual acuity below 0.5 Snellen
* In case of pregnancy (pregnancy test will be taken preoperatively in women of reproductive age)

Outcomes:
a new artificial intelligence based approach for intraocular lens power calculation
Artificial Intelligence Assistance of Pancreatic Exocrine Insufficiency Evaluation in Chronic Pancreatitis From Contrast-Enhanced CT
Trial ID: NCT06278272
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-03-01
Completion date: 2024-01-01
Sponsor: Changhai Hospital
Conditions: Chronic Pancreatitis; Exocrine Pancreatic Insufficiency; Machine Learning; Deep Learning
Locations: Changhai Hospital
Countries: China
Enrollment: 504
Age range: None - None
Sex: ALL

Summary:
Early assessment of pancreatic exocrine insufficiency (PEI) is crucial for determining appropriate chronic pancreatitis (CP) treatment plans, thereby avoiding unnecessary suffering and further complications in patients. A total of 504 patients with CP who underwent fecal elastase-1 test and contrast-enhanced CT at Changhai Hospital between January 2018 and April 2023 were enrolled in this study. The investigators aim to establish a fully automated workflow to establish a PEI classification model based on radiomic features, semantic features and deep learning features on enhanced CT images for evaluating the severity of PEI.

Eligibility criteria:
Inclusion Criteria:

* gender and age are not restricted
* diagnosis of chronic pancreatitis
* fe-1 test was conducted following recent pancreatic enhanced CT
* sign informed consent

Exclusion Criteria:

* enhanced CT was taken during the acute episode of chronic pancreatitis
* history of malignant tumor
* previous pancreatic surgery
* systemic diseases
* poor image quality or image loss

Outcomes:
Classification of pancreatic exocrine insufficiency
Construction and Application of Pressure Injury Risk Prediction Model for Critically Ill Patients Based on Machine Learning Algorithm
Trial ID: NCT05564975
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-10-01
Completion date: 2022-12-01
Sponsor: Yang Chaonan
Conditions: Pressure Injury; Critically Ill Patients; Risk Factors; Machine Learning Algorithms
Interventions: [None]
Enrollment: 350
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
In the previous investigation, investigators found that when the risk factors of stress injury in critical patients changed, clinical nurses lacked the awareness of evaluating the risk of stress injury, and lacked the risk assessment of this link. The stress risk prediction model is based on etiology. By analyzing the risk factors, the machine learning algorithm is used to evaluate the risk of pressure damage, and the prediction model of pressure damage can dynamically and comprehensively evaluate its risk. It is also a risk assessment tool. At present, there is no research on applying the stress injury risk prediction model of critical patients to the intensive care information software in China. In this study, the artificial intelligence algorithm library will be used to construct and apply the stress injury risk prediction model for critical patients.

Eligibility criteria:
Inclusion Criteria:

* Length of ICU stay \> 48 h
* Be at least 18 years old
* Meet the diagnostic criteria of intensive care unit and APACH II \<35 points

Exclusion Criteria:

* Patients with skin diseases and burns
* Patients with pressury injury at admission
* Repeat hospitalization
* Died 24 hours after pressure injury.

Outcomes:
Hospital-acquired pressure injury
Improving Myoelectric Prosthetic and Orthotic Limb Control Using Predictive Regression Algorithms and High-count Surface Electrodes
Trial ID: NCT05509101
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2017-03-01
Completion date: 2027-12-31
Sponsor: University of Utah
Conditions: Hemiparesis
Interventions: commercially available control algorithm; experimental control algorithm
Locations: University of Utah
Countries: United States
Enrollment: 45
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
The purpose of this study is to improve control of myoelectrically-controlled advanced orthotic devices (an exoskeleton device that use the body's muscle signals to drive movements of a robotic brace) by using advanced predictive decode algorithms, and the use of high count (\> 8) surface electromyographic (sEMG) electrodes.

Eligibility criteria:
Inclusion Criteria:

* First-ever ischemic or hemorrhagic stroke
* Chronic Stroke (at least 6 months since onset)
* Chronic hemiparesis
* Functional range of motion for contralateral arm

Exclusion Criteria:

* Individuals who are currently Incarcerated

Outcomes:
Box and Blocks Test (BBT)
A Cluster Randomized Controlled Trial Study on How AI Critical Appraisal Training Enhances Critical Thinking in Nursing Students
Trial ID: NCT07538986
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-07
Completion date: 2027-04-02
Sponsor: Xi Cao
Conditions: Healthy
Interventions: AI Critical Appraisal Training; Traditional Case-Based Learning
Locations: Chengdu Medical College
Countries: China
Enrollment: 400
Age range: 18 Years - 30 Years
Sex: ALL

Summary:
This study is a cluster randomized controlled trial aimed at evaluating the impact of the "AI Critical Thinking Training" teaching method on the critical thinking, AI literacy, and clinical reasoning abilities of undergraduate nursing students. Random allocation methods (such as the coin-tossing method) were used to randomly assign eligible classes as a whole to either the experimental group or the control group. The experimental group received AI critical thinking training (using AI to analyze cases and annotate their errors), while the control group received the same amount of time in traditional case-based teaching. The research tools included the Chinese version of the Critical Thinking Disposition Inventory, the AI Literacy Scale, the Clinical Reasoning Scale, and a standardized case analysis scoring sheet. Data collection was conducted twice, before the experiment (pre-test) and after the experiment (post-test). The protocol was based on mature educational theories, with a clear process and extremely low risk, demonstrating good scientific and feasibility. During the research process, if the participants felt any discomfort, they could immediately request to stop the research and withdraw from the study.

Eligibility criteria:
Inclusion Criteria:

* All full-time undergraduate nursing students currently enrolled, who are willing to participate in this study

Exclusion Criteria:

* Did not complete the survey questionnaire and did not take the final exam.

Outcomes:
Change in Critical Thinking Disposition
Prediction of Transfusion-Associated Complications
Trial ID: NCT05466370
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-07-15
Completion date: 2022-07-31
Sponsor: Kepler University Hospital
Conditions: Transfusion-dependent Anemia
Interventions: Transfusion of Allogeneic Blood
Locations: Kepler University Hospital
Countries: Austria
Enrollment: 3366
Age range: 18 Years - None
Sex: ALL

Summary:
Currently, about 350000 red blood cell concentrates are produced from blood donations in Austria every year.

In addition to the main effect of replacing lost blood, red blood cell concentrates also have many undesirable effects - from blood group compatibilities, which are easily avoidable due to care, to storage-related side effects, to mostly intensive care problems as a result of massive transfusions, to system-wide effects such as TRALI, TACO and TRIM.

Before being administered to patients, red blood cell concentrates undergo an extensive quality assurance process in which a large number of parameters are collected. Prior to use on patients, for example, bedside tests and tests for further incompatibilities with a blood sample from the intended patient are performed. With the implementation of Patient Blood Management (PBM) in recent years, the use of red cell concentrates has become more targeted - the number of transfusions is decreasing in most developed countries. However, it is still possible to suffer transfusion-related adverse events (TRAE). Thus, active research activity to reduce these TRAEs continues to be called for.

To date, however, it is not known which patients experience transfusion-related adverse events. Despite the broad measures of hemovigilance and pre-transfusion testing, it is still not possible to predict which individual patient will respond to a transfusion with a typical adverse event such as hypotension, hemolysis, renal failure, or TRALI. It seems understandable that characteristics of the patient as well as characteristics of the administered unit could play a role for this. In particular, it is conceivable that a combination of characteristics of the blood unit and characteristics of the patient could determine a complication in the course of administration. For this reason, it seems attractive to use artificial intelligence and machine learning methods to predict any complications.

Eligibility criteria:
Inclusion Criteria:

* All adult patients that received at a blood transfusion the Kepler University Hospital in the period between 2016-10-31 to 2020-08-31.

Exclusion Criteria:

None.

Outcomes:
AUROC for Classification of AKI; AUROC for Classification of ARF; AUROC for Classification of AKI and ARF
Machine Learning-Based Prediction of BCG Response in High-Risk Non-Muscle Invasive Bladder Cancer Patients
Trial ID: NCT05332353
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-04-01
Completion date: 2023-12-01
Sponsor: Mansoura University
Conditions: Non-Muscle Invasive Bladder Cancer
Interventions: Bacillus Calmette-Guerin
Locations: Urology and Nephrology Center
Countries: Egypt
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective study aims at using Artificial intelligence to create a helpful unbiased machine learning-based model that predicts BCG unresponsiveness in high risk BCG-naïve NMIBC patients incorporating all potential clinico-pathological, radiological and/or molecular prognostic factors.

Eligibility criteria:
Inclusion Criteria:

1. Ability to give informed consent.
2. High-risk NMIBC (any patient with high grade Ta or high grade T1 or CIS or recurrent multiple large tumors with low grade Ta/T1)
3. Good performance status (Defined as: performance status 0 or 1 at the time of TURBT according to Eastern Cooperative Oncology Group-ECOG-).
4. No prior history of BCG intravesical therapy.

Exclusion Criteria:

1. History of previous radiotherapy or systemic chemotherapy.
2. Post TURBT histopathology report showing any of the following;

   1. Benign histopathology.
   2. Muscle invasive urothelial carcinoma.
   3. Non urothelial carcinoma of the bladder.

Outcomes:
BCG unresponsiveness