AIMC Topic: Sensitivity and Specificity
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Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.
BMC pediatrics
Apr 24, 2025
BACKGROUND: Congenital heart disease (CHD) affects approximately 1% of newborns and is a leading cause of mortality in early childhood. Despite the importance of early detection, current screening methods, such as pulse oximetry and auscultation, hav...
Evaluating artificial intelligence performance in medical image analysis: Sensitivity, specificity, accuracy, and precision of ChatGPT-4o on Kellgren-Lawrence grading of knee X-ray radiographs.
The Knee
Apr 23, 2025
BACKGROUND: Recent advancements in artificial intelligence, including ChatGPT, have enabled its application in medical image analysis.This study aimed to evaluate the sensitivity and specificity of ChatGPT in assessing knee osteoarthritis (KOA) radio...
Using deep learning for estimation of time-since-injury in pediatric accidental fractures.
Pediatric radiology
Apr 22, 2025
BACKGROUND: Estimating time-since-injury of healing fractures is imprecise, encompassing excessively wide timeframes. Most injured children are evaluated at non-children's hospitals, yet pediatric radiologists can disagree with up to one in six skele...
Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department.
Annals of the Academy of Medicine, Singapore
Apr 22, 2025
INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers a...
Diabetic retinopathy detection based on mobile maxout network and weber local descriptor feature selection using retinal fundus image.
Scientific reports
Apr 22, 2025
Retinal screening provides for earlier detection of diabetic retinopathy (DR) as well as prompt diagnosis. Recognizing DR utilizing color fundus imaging needs qualified specialists to know about the presence and significance of a few insignificant fe...
Development of an Artificial Intelligence-Enabled Electrocardiography to Detect 23 Cardiac Arrhythmias and Predict Cardiovascular Outcomes.
Journal of medical systems
Apr 22, 2025
Arrhythmias are common and can affect individuals with or without structural heart disease. Deep learning models (DLMs) have shown the ability to recognize arrhythmias using 12-lead electrocardiograms (ECGs). However, the limited types of arrhythmias...
Combining Ultrasound Imaging and Molecular Testing in a Multimodal Deep Learning Model for Risk Stratification of Indeterminate Thyroid Nodules.
Thyroid : official journal of the American Thyroid Association
Apr 21, 2025
Indeterminate cytology (Bethesda III and IV) represents 15-30% of biopsied thyroid nodules and require additional diagnostic testing. Molecular testing (MT) is a commonly used diagnostic tool that evaluatesmalignancy risk through next generation seq...
Machine Learning-Based Diagnostic Prediction Model Using T1-Weighted Striatal Magnetic Resonance Imaging for Early-Stage Parkinson's Disease Detection.
Academic radiology
Apr 18, 2025
RATIONALE AND OBJECTIVES: Diagnosing Parkinson's disease (PD) typically relies on clinical evaluations, often detecting it in advanced stages. Recently, artificial intelligence has increasingly been applied to imaging for neurodegenerative disorders....
Diagnosis Test Accuracy of Artificial Intelligence for Endometrial Cancer: Systematic Review and Meta-Analysis.
Journal of medical Internet research
Apr 18, 2025
BACKGROUND: Endometrial cancer is one of the most common gynecological tumors, and early screening and diagnosis are crucial for its treatment. Research on the application of artificial intelligence (AI) in the diagnosis of endometrial cancer is incr...
Deep Learning-Driven Abbreviated Shoulder MRI Protocols: Diagnostic Accuracy in Clinical Practice.
Tomography (Ann Arbor, Mich.)
Apr 17, 2025
BACKGROUND: Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexpl...
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