Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have
shown great potential in enhancing diagnosis and prognosis prediction in patients with …

Electronic health records (EHRs) security is a critical challenge in the implementation and
administration of Internet of Medical Things (IoMT) systems within the healthcare sector's …

In this study, we evaluate the performance of the Segment Anything Model (SAM) model in
clinical radiotherapy. We collected real clinical cases from four regions at the Mayo Clinic …

Background We aimed to propose a deep learning-based approach to automatically
measure eyelid morphology in patients with thyroid-associated ophthalmopathy (TAO) …

Machine learning (ML) has been proposed for lesion segmentation in acute ischemic stroke
(AIS). This study aimed to provide a systematic review and meta-analysis of the overall …

Background The preoperative differentiation between benign parotid gland tumors (BPGTs)
and malignant parotid gland tumors (MPGTs) is of great significance for therapeutic decision …

Background Automatic segmentation of temporal bone computed tomography (CT) images
is fundamental to image-guided otologic surgery and the intelligent analysis of CT images in …

Background The differential diagnosis of eyelid basal cell carcinoma (BCC) and sebaceous
carcinoma (SC) is highly dependent on pathologist's experience. Herein, we proposed a …

Purpose The purpose of this study was to evaluate and explore the difference between an
atlas-based and deep learning (DL)-based auto-segmentation scheme for organs at risk …

This study investigates, through a narrative review, the transformative impact of deep
learning (DL) in the field of radiotherapy, particularly in light of the accelerated …