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A machine learning tool for identifying newly diagnosed heart failure in individuals with known diabetes in primary care.
ESC heart failure
Oct 20, 2024
AIMS: We aimed to create a predictive model utilizing machine learning (ML) to identify new cases of congestive heart failure (CHF) in individuals with diabetes in primary health care (PHC) through the analysis of diagnostic data.
A deep learning-based, real-time image report system for linear EUS.
Gastrointestinal endoscopy
Oct 19, 2024
BACKGROUND AND AIMS: The integrity of image acquisition is critical for biliopancreatic EUS reporting, significantly affecting the quality of EUS examinations and disease-related decision-making. However, the quality of EUS reports varies among endos...
Development of machine learning model for predicting prolonged operation time in lumbar stenosis undergoing posterior lumbar interbody fusion: a multicenter study.
The spine journal : official journal of the North American Spine Society
Oct 19, 2024
BACKGROUND CONTEXT: Longer posterior lumbar interbody fusion (PLIF) surgeries for individuals with lumbar spinal stenosis are linked to more complications and negatively affect recovery after the operation. Therefore, there is a critical need for a m...
Grading of diabetic retinopathy using a pre-segmenting deep learning classification model: Validation of an automated algorithm.
Acta ophthalmologica
Oct 19, 2024
PURPOSE: To validate the performance of autonomous diabetic retinopathy (DR) grading by comparing a human grader and a self-developed deep-learning (DL) algorithm with gold-standard evaluation.
External evaluation of a commercial artificial intelligence-augmented digital auscultation platform in valvular heart disease detection using echocardiography as reference standard.
International journal of cardiology
Oct 19, 2024
OBJECTIVE: There are few studies evaluating the accuracy of commercially available AI-powered digital auscultation platforms in detecting valvular heart disease (VHD). Therefore, the utility of these systems for diagnosing clinically significant VHD ...
Development of a machine learning model for precision prognosis of rapid kidney function decline in people with diabetes and chronic kidney disease.
Diabetes research and clinical practice
Oct 19, 2024
AIMS: To develop a machine learning model for predicting rapid kidney function decline in people with type 2 diabetes (T2D) and chronic kidney disease (CKD) and to pinpoint key modifiable risk factors for targeted interventions.
Preoperative identification of early extrahepatic recurrence after hepatectomy for colorectal liver metastases: A machine learning approach.
World journal of surgery
Oct 19, 2024
BACKGROUND: Machine learning (ML) may provide novel insights into data patterns and improve model prediction accuracy. The current study sought to develop and validate an ML model to predict early extra-hepatic recurrence (EEHR) among patients underg...
Development and Validation of an Artificial Intelligence-Assisted Patient Education Material for Ostomy Patients: A Methodological Study.
Journal of advanced nursing
Oct 18, 2024
AIM: To develop and test the validity of an artificial intelligence-assisted patient education material for ostomy patients.
Assessment of machine learning classifiers for predicting intraoperative blood transfusion in non-cardiac surgery.
Transfusion clinique et biologique : journal de la Societe francaise de transfusion sanguine
Oct 18, 2024
BACKGROUND: This study aimed to develop a machine learning classifier for predicting intraoperative blood transfusion in non-cardiac surgeries.
Machine learning-based CT radiomics approach for predicting occult peritoneal metastasis in advanced gastric cancer preoperatively.
Clinical radiology
Oct 18, 2024
AIM: To develop a machine learning-based CT radiomics model to preoperatively diagnose occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients.
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