Orthopedics

Predicting postoperative coronal imbalance in Lenke 1/2 adolescent idiopathic scoliosis: A machine learning model with clinical interpretability.

OBJECTIVES: Selective posterior thoracic fusion (sPTF) for Lenke 1/2 adolescent idiopathic scoliosis (AIS) aims to reconcile multi-planar correction with motion preservation. Nevertheless, postoperative coronal imbalance (CIB) frequently compromises these objectives. This study developed an interpretable machine learning architecture to stratify CIB risk and identify key predictors. METHODS: Data ...

Serum IL-19 and Biglycan as Candidate Biomarkers by Proteomic Approach in Patients with Severe Low Back Pain-a Pilot Study.

OBJECTIVE: Low back pain (LBP) is common. Although imaging studies are widely used to evaluate back pain, the link between pain and intervertebral disc degeneration visualized on images is unclear. This study aims to discover novel biomarkers in patients with severe back pain. DESIGN: Levels of 1512 serum proteins from 29 patients with LBP and 11 healthy controls were compared. Machine learning an...

Deep learning-based automated assessment of alveolar bone loss in CBCT for periodontitis.

OBJECTIVES: This study aimed to develop and validate a CBCT-based automated system for assessing radiographic alveolar bone loss (RBL) to improve the ...

An interpretable machine learning model integrating ultrasound and clinical variables for predicting osteoporosis in patients with rheumatoid arthritis.

OBJECTIVES: Rheumatoid arthritis (RA) significantly increases the risk of osteoporosis (OP) and fractures, yet dual-energy X-ray absorptiometry (DXA) ...

Enhancing medical imaging teaching on scoliosis: Efficacy of learning method integrating 3D printing and artificial intelligence technologies.

BACKGROUND: In scoliosis imaging education, the traditional lecture-based learning model can lead to low student engagement and present challenges in ...

Multi-parameter scoliosis evaluation from dual-view x-rays via a local sine-based projection model.

OBJECTIVE: To develop a robust and accurate multi-parameter assessment framework for adolescent idiopathic scoliosis (AIS) based on spinal X-ray image...

Development and multicenter validation of an AI-driven detection system for DLBCL cells in bone marrow smears.

The presence of diffuse large B-cell lymphoma (DLBCL) cells in bone marrow (BM) smears is recognised as a key morphological basis for diagnosing BM in...

Keypoint detection and functional evaluation of human lower limbs based on YOLOv8 and HRNet.

With the development of artificial intelligence and deep learning technology, human pose estimation has been widely applied in fields such as medical ...

Deep learning-based automatic measurement of spinal alignment and implant detection in scoliosis radiographs.

Deep learning-based automated analysis offers the potential to streamline workflows, improve reproducibility, and reduce clinician workload. We develo...

The write-cost bottleneck: energy constraints on continual learning in brains and machines.

In recent decades, neural energy-budget research has extensively quantified signaling costs-the "read" side of computation-including action potentials...

Development and validation of a routine blood test-based model to predict in-hospital postoperative pulmonary infection in older patients with hip fracture.

BACKGROUND: Postoperative pulmonary infection (PPI) is a common and serious complication in older adults undergoing hip fracture surgery, leading to p...

Applying artificial intelligence to automate clinical data extraction from veterinary electronic health records: A practical guidance and comparative scoping review.

Veterinary electronic health records are often stored as unstructured free text, and structuring this information into analyzable formats is essential...

Diagnostic performance of deep-learning algorithms in radiographic grading of knee osteoarthritis: a systematic review and meta-analysis.

BackgroundDeep learning (DL) has been increasingly applied to grade knee osteoarthritis (KOA) on radiographs, but reported diagnostic performance vari...

Machine learning-based prediction of hospital-associated complications after tibial fracture surgery in older patients: a nationwide Japanese database study.

BACKGROUND: Older adults requiring emergency surgery for acute tibial fractures are vulnerable to hospital-associated complications (HACs), but admiss...

Publicly Accessible Large Language Model Responses to Frequently Asked Questions About Spondylodiscitis: Preliminary Expert Evaluation.

BACKGROUND: Patients increasingly use large language models (LLMs) to obtain medical information, but the quality of LLM-generated information on comp...

Development of Convolutional Neural Networks for Classification and Characterisation of Proximal Humerus Fractures on Computed Tomography.

BACKGROUND: Agreement between surgeons on classification, characterization and choice of treatment for proximal humerus fractures (PHFs) is poor, lead...

Two-stage deep learning for circular landmark detection in hip radiographs.

This study develops a high-resolution anatomical landmark detection method for hip anteroposterior radiographs, designed to enhance the accuracy of fe...

Chest Radiograph-based Artificial Intelligence for Osteoporosis: Accuracy and Associations With Fracture and Mortality.

OBJECTIVES: To evaluate an AI tool for opportunistic osteoporosis screening using chest radiographs (CXRs), focusing on its diagnostic accuracy agains...

Graphene Oxide Encapsulated Natural Plant Essential Oil as Ti6Al4V-UHMWPE Joint Prostheses Biolubricant: Tribological Behavior and Machine Learning-Assisted Mechanism Analysis.

It is important to enhance the overall efficacy of artificial joint systems by addressing friction and oxidation issues at the joint prosthesis interf...

Arthroscopy-Validated Diagnostic Performance of a Deep Learning Reconstruction Pipeline for Rapid 7-Minute Five-Sequence 3-T Knee MRI.

Background. Deep learning reconstruction (DLR) methods can enhance image quality and reduce scan time of knee MRI compared with conventional approache...