MRI to digital medicine diagnosis: integrating deep learning into clinical decision-making for lumbar degenerative diseases.

Introduction: To develop an intelligent system based on artificial intelligence (AI) deep learning algorithms using deep learning tools, aiming to assist in the diagnosis of lumbar degenerative diseases by identifying lumbar spine magnetic resonance images (MRI) and improve the clinical efficiency of physicians.

Methods: The PP-YOLOv2 algorithm, a deep learning technique, was used to design a deep learning program capable of automatically identifying the spinal diseases (lumbar disc herniation or lumbar spondylolisthesis) based on the lumbar spine MR images. A retrospective analysis was conducted on lumbar spine MR images of patients who visited our hospital from January 2017 to January 2022.

Application of Metagenomic Next-Generation Sequencing in Suspected Spinal Infectious Diseases.

Objective: This study aimed to explore the clinical efficacy of metagenomic next-generation sequencing (mNGS) in diagnosing and treating suspected spinal infectious diseases.

Methods: Between October 2022 to December 2023, a retrospective analysis was performed on patient records within the Department of Spinal Surgery at Guilin People's Hospital. The analysis included comprehensive data on patients with presumed spinal infectious diseases, incorporating results from mNGS tests conducted externally, conventional pathogen detection results, laboratory examination results, and imaging findings.

Spinal disease diagnosis assistant based on MRI images using deep transfer learning methods.

Introduction: In light of the potential problems of missed diagnosis and misdiagnosis in the diagnosis of spinal diseases caused by experience differences and fatigue, this paper investigates the use of artificial intelligence technology for auxiliary diagnosis of spinal diseases.

Methods: The LableImg tool was used to label the MRIs of 604 patients by clinically experienced doctors. Then, in order to select an appropriate object detection algorithm, deep transfer learning models of YOLOv3, YOLOv5, and PP-YOLOv2 were created and trained on the Baidu PaddlePaddle framework.

Factors associated with the relapse in Ponseti treated congenital clubfoot.

Objectives: We retrospectively investigated the clinical materials to seek the factors that lead to relapse after using the Ponseti method.

Methods: We retrospectively reviewed all children with congenital clubfoot treated with the Ponseti method in our hospital from June 2008 to June 2013. The data included the following factors: age, gender, initial Pinari score, number of casts, number of feet (unilateral or bilateral), age at the first casting, age of mother, tenotomy, walking age, and compliance with using bracing.

Minimally invasive versus open transforaminal lumbar fusion: a systematic review of complications.

Purpose: The aim of this study is to compare mTLIF vs. oTLIF with regard to peri-operative complications, operative time, estimated blood loss, fluoroscopic time, and the length of hospital stay.

Methods: The PubMed and EMBASE databases were searched for relevant articles reporting patients undergoing TLIF, and a comparison between mTILF and oTLIF was performed.