OBJECTIVE: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low do...
Health care is undergoing a profound transformation through the integration of artificial intelligence (AI). However, the rapid integration and expansive growth of AI within health care systems present ethical and legal challenges that warrant carefu...
BACKGROUND: The significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM.
AIM: To evaluate if, and to what extent, machine learning models can capture clinically defined Stage III/IV periodontitis from self-report questionnaires and demographic data.
RESEARCH QUESTION: Can artificial intelligence identify predictors of an increased Day 5 blastocyst utilization rate (D5BUR), which is one of the most informative key performance indicators in an IVF laboratory?
STATEMENT OF PROBLEM: Dental implant systems can be identified using image classification deep learning. However, investigations on the accuracy of classifying and identifying implant design through an object detection model are lacking.
Given that mobile digital imaging analyses (DIA) are equipped to automate body composition and subsequently alter one's appearance at a given objective body fat percent (BF%), the purpose of this study was to validate the use of this tool for assessm...
OBJECTIVES: To perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASi...
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