The objectives of this study were to evaluate different machine learning algorithms for predicting body weight (BW) in Sujiang pigs using the following morphological traits: age, body length (BL), backfat thickness (BFT), chest circumference (CC), body height (BH), chest width (CW), and hip width (HW). Additionally, this study also investigated which machine learning algorithms could accurately and efficiently predict body weight in pigs using a limited set of morphological traits. For this purpose, morphological measurements of 365 mature (180 ± 5 days) Sujiang pigs from the Jiangsu Sujiang Pig Breeding Farm in Taizhou, Jiangsu Province, China were used.
View Article and Find Full Text PDFHirschsprung disease, a congenital disease characterized by the absence of ganglion cells, presents significant surgical challenges. Addressing a critical gap in intraoperative diagnostics, we introduce transformative artificial intelligence approach that significantly enhances the detection of ganglion cells in frozen sections. The data set comprises 366 frozen and 302 formalin-fixed-paraffin-embedded hematoxylin and eosin-stained slides obtained from 164 patients from 3 centers.
View Article and Find Full Text PDFErroneous and delayed triage in an increasingly crowded emergency department (ED). ChatGPT is an artificial intelligence model developed by OpenAI and is being trained for use in natural language processing tasks. Our study aims to determine the accuracy of patient triage using ChatGPT according to the emergency severity index (ESI) for triage in EDs.
View Article and Find Full Text PDFIntroduction: The employment of laparoscopic surgical techniques has reignited the debate on managing Meckel's Diverticulum (MD) due to its low complication rates. Nevertheless, concerns have been raised regarding completely removing any potential heterotopic mucosa. Our study aimed to compare surgical approaches in MD and assess the effectiveness of simple diverticulectomy.
View Article and Find Full Text PDFBackground: Periprosthetic joint infection (PJI) is a major complication following hip arthroplasty, leading to prolonged hospital stays, increased health care costs, and major morbidity. Diabetes mellitus is a prevalent comorbidity among hip arthroplasty patients, contributing to an increased risk of surgical complications, including infections. However, limited evidence exists regarding the microbial profiles of PJIs in diabetic patients compared to nondiabetic counterparts.
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