OBJECTIVE: To investigate the potential of deep learning for automatically delineating (segmenting) laryngeal cancer superficial extent on endoscopic images and videos.
We aimed to determine the effect of optic disc tilt on deep learning-based optic disc classification. A total of 2507 fundus photographs were acquired from 2236 eyes of 1809 subjects (mean age of 46 years; 53% men). Among all photographs, 1010 (40.3%...
OBJECTIVE: This study used random forest model to explore the feasibility of radial artery calcification in prediction of coronary artery calcification in hemodialysis patients.
OBJECTIVES: Development and validation of a computed tomography urography (CTU)-based machine learning (ML) model for prediction of preoperative pathology grade of upper urinary tract urothelial carcinoma (UTUC).
IEEE journal of biomedical and health informatics
Jan 4, 2024
Novel multimode thermal therapy by freezing before radio-frequency heating has achieved a desirable therapeutic effect in liver cancer. Compared with surgical resection, ablation treatment has a relatively high risk of tumor recurrence. To monitor tu...
The British journal of oral & maxillofacial surgery
Jan 3, 2024
Three-dimensional (3D) ultrasound can assess the margins of resected tongue carcinoma during surgery. Manual segmentation (MS) is time-consuming, labour-intensive, and subject to operator variability. This study aims to investigate use of a 3D deep l...
Objective This study assessed the efficacy of machine learning in predicting thyrotoxicosis and hypothyroidism [thyroid-stimulating hormone >10.0 mIU/L] by leveraging age and sex as variables and integrating biochemical test parameters used by the Ja...
OBJECTIVES: The aim of our study was to examine how breast radiologists would be affected by high cancer prevalence and the use of artificial intelligence (AI) for decision support.
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.