J Endourol
Division of Urology, University of Montreal Hospital Center, Montreal, Canada.
Published: April 2023
Previous systematic reviews related to machine learning (ML) in urology often overlooked the literature related to endourology. Therefore, we aim to conduct a more focused systematic review examining the use of ML algorithms for the management of benign prostatic hyperplasia (BPH) or urolithiasis. In addition, we are the first group to evaluate these articles using the Standardized Reporting of Machine Learning Applications in Urology (STREAM-URO) framework. Searches of MEDLINE, Embase, and the Cochrane CENTRAL databases were conducted from inception through July 12, 2021. Keywords included those related to ML, endourology, urolithiasis, and BPH. Two reviewers screened the citations that were eligible for title, abstract, and full-text screening, with conflicts resolved by a third reviewer. Two reviewers extracted information from the studies, with discrepancies resolved by a third reviewer. The data collected were then qualitatively synthesized by consensus. Two reviewers evaluated each article according to the STREAM-URO checklist with discrepancies resolved by a third reviewer. After identifying 459 unique citations, 63 articles were retained for data extraction. Most articles consisted of tabular ( = 32) and computer vision ( = 23) tasks. The two most common problem types were classification ( = 40) and regression ( = 12). In general, most studies utilized neural networks as their ML algorithm ( = 36). Among the 63 studies retrieved, 58 were related to urolithiasis and 5 focused on BPH. The urolithiasis studies were designed for outcome prediction ( = 20), stone classification ( = 18), diagnostics ( = 17), and therapeutics ( = 3). The BPH studies were designed for outcome prediction ( = 2), diagnostics ( = 2), and therapeutics ( = 1). On average, the urolithiasis and BPH articles met 13.8 (standard deviation 2.6), and 13.4 (4.1) of the 26 STREAM-URO framework criteria, respectively. The majority of the retrieved studies effectively helped with outcome prediction, diagnostics, and therapeutics for both urolithiasis and BPH. While ML shows great promise in improving patient care, it is important to adhere to the recently developed STREAM-URO framework to ensure the development of high-quality ML studies.
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http://dx.doi.org/10.1089/end.2022.0311 | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.
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JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFJMIR AI
January 2025
Department of Radiology, Children's National Hospital, Washington, DC, United States.
J Chem Theory Comput
January 2025
Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, School of Pharmacy, Guizhou Medical University, Guiyang, Guizhou 550025, P. R. China.
Traditional machine learning methods face significant challenges in predicting the properties of highly symmetric molecules. In this study, we developed a machine learning model based on graph neural networks (GNNs) to accurately and swiftly predict the thermodynamic and photochemical properties of fullerenols, such as C(OH) ( = 1 to 30). First, we established a global method for generating fullerenol isomers through isomer fingerprinting, which can generate all possible isomers or produce diverse structural types on demand.
View Article and Find Full Text PDFPLoS One
January 2025
Dirección General de Minería, República Dominicana.
This study investigates the geochemical characteristics of rare earth elements (REEs) in highland karstic bauxite deposits located in the Sierra de Bahoruco, Pedernales Province, Dominican Republic. These deposits, formed through intense weathering of volcanic material, represent a potentially valuable REE resource for the nation. Surface and subsurface soil samples were analyzed using portable X-ray fluorescence (pXRF) and a NixPro 2 color sensor validated with inductively coupled plasma optical emission spectrometry (ICP-OES).
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