Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective
clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of …

Background In recent years, there has been considerable research on the use of artificial
intelligence to estimate age and disease status from medical images. However, age …

Background Chest radiographs are widely available and cost-effective; however, their
usefulness as a biomarker of ageing using multi-institutional data remains underexplored …

Purpose To train an explainable deep learning model for patient reidentification in chest
radiograph datasets and assess changes in model-perceived patient identity as a marker for …

Purpose Medical imaging can be used to estimate a patient's biological age, which may
provide complementary information to clinicians compared to chronological age. In this …

Aims Age-related changes in cardiac structure and function are well recognized and make
the clinical determination of abnormal left ventricular (LV) diastolic dysfunction (LVDD) …

Objective Adult age estimation (AAE) is a challenging task. Deep learning (DL) could be a
supportive tool. This study aimed to develop DL models for AAE based on CT images and …

Sex estimation is very important in forensic applications as part of individual identification.
Morphological sex estimation methods predominantly focus on anatomical measurements …

Purpose To develop and validate a deep learning model for detection of nasogastric tube
(NGT) malposition on chest radiographs and assess model impact as a clinical decision …

Objectives Chest X-rays (CXRs) convey much illegible physiological information that deep
learning model (DLM) has been reported interpreting successfully. Since the …