A clinical pilot trial of an artificial intelligence-driven smart phone application of bowel preparation for colonoscopy: a randomized clinical trial.

Background: High-quality bowel preparation is paramount for a successful colonoscopy. This study aimed to explore the effect of artificial intelligence-driven smartphone software on the quality of bowel preparation.

Methods: Firstly, we utilized 3305 valid liquid dung images collected mobile phones as training data.

Role of periodontal ligament fibroblasts in periodontitis: pathological mechanisms and therapeutic potential.

Periodontal ligament fibroblasts (PDLFs) play a crucial role in the etiology of periodontitis and periodontal tissue regeneration. In healthy periodontal tissues, PDLFs maintain the homeostasis of periodontal soft and hard tissues as well as the local immune microenvironment. PDLFs also have the potential for multidirectional transdifferentiation and are involved in periodontal tissue regeneration.

Competency-based ultrasound curriculum for standardized training resident: a pre- and post-training evaluation.

Background: Ultrasound training is crucial for residents across specialties but presents challenges for residents that are not specializing in ultrasound. Investigating the effectiveness of competency-based ultrasound curricula for a wider range of medical specialties is imperative.

Methods: A total of 250 residents who attended the ultrasound curriculum between June 2023 and June 2024 were included in the analysis.

Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis.

Background: Predicting mortality in sepsis-related acute kidney injury facilitates early data-driven treatment decisions. Machine learning is predicting mortality in S-AKI in a growing number of studies. Therefore, we conducted this systematic review and meta-analysis to investigate the predictive value of machine learning for mortality in patients with septic acute kidney injury.

Development and validation of a nomogram to predict the unfavorable outcomes in elderly patients undergoing lumbar fusion surgery for degenerative disease.

Objective: Failure to understand long-term quality of life and functional outcomes hinders effective decision making and prognostication. Therefore, the study aims to predict and analyse the unfavorable outcomes (FOs) in elderly patients undergoing lumbar fusion surgery.

Methods: Consecutive 382 patients who underwent lumbar fusion surgery for lumbar degenerative disease from March 2019 to July 2022 were enrolled in this study.