RATIONALE AND OBJECTIVES: The aim of this study is to explore the utility of Inductive Decision Tree models (IDTs) in distinguishing between benign, malignant, and high-risk (B3) breast lesions.
BACKGROUND AND OBJECTIVES: Intramuscular fat fraction (FF), assessed with quantitative MRI (qMRI), has emerged as one of the few responsive outcome measures in CMT1A patients. The main limitation for its use in future therapeutic trials is the time r...
Drug-resistant tuberculosis (DR-TB) and HIV coinfection present a conundrum to public health globally and the achievement of the global END TB strategy in 2035. A descriptive, retrospective review of medical records of patients, who were diagnosed wi...
The aim of this study was to propose a novel method to identify individuals by recognizing dentition change, along with human identification process using deep learning. Recent and past images of adults aged 20-49 years with more than two dental pano...
OBJECTIVES: To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm.
OBJECTIVES: Artificial intelligence (AI) is thought to improve lesion detection. However, a lack of knowledge about human performance prevents a comparative evaluation of AI and an accurate assessment of its impact on clinical decision-making. The ob...
We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunction (CLAD) with lung ventilation single-photon emission computed tomography (SPECT) images acquired briefly using a convolutional neural network (CNN)...
OBJECTIVE: Recently, we developed a first artificial intelligence (AI)-based digital pathology classifier for focal cortical dysplasia (FCD) as defined by the ILAE classification. Herein, we tested the usefulness of the classifier in a retrospective ...
Ecotoxicology and environmental safety
Oct 23, 2024
BACKGROUND: Cardiovascular disease (CVD) remains a leading cause of mortality globally. Environmental pollutants, specifically volatile organic compounds (VOCs), have been identified as significant risk factors. This study aims to develop a machine l...
Medical decision making : an international journal of the Society for Medical Decision Making
Oct 23, 2024
BACKGROUND: Machine learning (ML) methods can identify complex patterns of treatment effect heterogeneity. However, before ML can help to personalize decision making, transparent approaches must be developed that draw on clinical judgment. We develop...
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