INTRODUCTION AND HYPOTHESIS: Data examining the effect of diabetes mellitus (DM) on prolapse recurrence after sacrocolpopexy (SCP) is limited. The primary objective of this study was to determine if DM affects prolapse recurrence after robotic SCP.
Zhonghua wai ke za zhi [Chinese journal of surgery]
37088542
To analyze the short-term clinical effects of robot-assisted and laparoscopic repair of the hiatal hernia. The clinical data of 56 patients underwent minimally invasive hiatal hernia repair from January 2021 to January 2022 in the Department of Min...
BACKGROUND: It is challenging to predict retear after arthroscopic rotator cuff repair (ARCR). The usefulness of arthroscopic intraoperative images as predictors of the ARCR prognosis has not been analyzed.
OBJECTIVE: Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines discourage ongoing access salvage attempts after two interventions prior to successful use or more than three interventions per year overall. The goal was to develop a tool for ...
BACKGROUND AND AIMS: Image-enhanced endoscopy has attracted attention as a method for detecting inflammation and predicting outcomes in patients with ulcerative colitis (UC); however, the procedure requires specialist endoscopists. Artificial intelli...
Patient-derived organoids have proven to be a highly relevant model for evaluating of disease mechanisms and drug efficacies, as they closely recapitulate in vivo physiology. Colorectal cancer organoids, specifically, exhibit a diverse range of morph...
BACKGROUND: Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors' engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individ...
BACKGROUND: Vestibular migraine (VM) and Menière's disease (MD) are two common causes of recurrent spontaneous vertigo. Using history, video-nystagmography and audiovestibular tests, we developed machine learning models to separate these two disorder...
OBJECTIVES: To develop an interpretable deep learning model of lupus nephritis (LN) relapse prediction based on dynamic multivariable time-series data.
OBJECTIVE: We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance.