The purpose of this study was to develop a machine learning-based model using quantitative color fundus photography (CFP) data to predict myopia risk in school-age children, based on the axial length/corneal curvature radius (AL/CR) ratio, and to ide...
BACKGROUND: Digital phenotyping refers to the objective measurement of human behavior via devices such as smartphones or watches and constitutes a promising advancement in personalized medicine. Digital phenotypes derived from heart rate, mobility, o...
BACKGROUND: Computer perception (CP) technologies hold significant promise for advancing precision mental health care systems, given their ability to leverage algorithmic analysis of continuous, passive sensing data from wearables and smartphones (eg...
BACKGROUND: Case-based learning using standardized patients is a key method for teaching communication skills in medicine. Besides logistical and financial hurdles, standardized patients portrayed by actors cannot cover the complete diversity of soci...
BACKGROUND: South Korea has the highest suicide rate among the Organisation for Economic Co-operation and Development nations, with particularly elevated figures among persons with disabilities. Research has shown a strong correlation between suicida...
While Fine needle aspiration cytology (FNAC) and mammography are both used to diagnose breast lesions, FNAC is generally more accurate than mammograms for predicting breast cancer. It is also gaining popularity as an early detection tool due to its r...
Automated electroencephalography (EEG)-based epilepsy diagnosis has reported near-perfect accuracies for almost two decades on a benchmark dataset, yet virtually no system is used in routine care. We critically re-examined this translation gap by rep...
This study proposes Scout-Dose-TCM for direct, prospective estimation of organ-level and effective doses under tube current modulation (TCM) and compares its performance with two established methods.Contrast-enhanced chest-abdomen-pelvis CT exams fro...
BACKGROUND: Artificial intelligence (AI) is rapidly advancing in healthcare and has the potential to transform patient care. This study aimed to assess the knowledge, attitudes, and practices (KAP) regarding AI among pediatricians in India.
BACKGROUND: Machine learning (ML) models show promise in predicting post-traumatic stress disorder (PTSD) treatment outcomes, but it is unknown how their predictions compare to those of clinicians. This study directly compared the accuracy of clinici...
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