Orthopedics

Constructing a Deep Learning Radiomics Model Based on X-ray Images and Clinical Data for Predicting and Distinguishing Acute and Chronic Osteoporotic Vertebral Fractures: A Multicenter Study.

RATIONALE AND OBJECTIVES: To construct and validate a deep learning radiomics (DLR) model based on X-ray images for predicting and distinguishing acute and chronic osteoporotic vertebral fractures (OVFs).

Construct validation of machine learning for accurately predicting the risk of postoperative surgical site infection following spine surgery.

BACKGROUND: This study aimed to evaluate the risk factors for machine learning (ML) algorithms in predicting postoperative surgical site infection (SSI) following spine surgery.

Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review.

PURPOSE: This systematic review aimed to investigate the reliability of AI predictive models of intraoperative implant sizing in total knee arthroplas...

Automated Detection of the Thoracic Ossification of the Posterior Longitudinal Ligament Using Deep Learning and Plain Radiographs.

Ossification of the ligaments progresses slowly in the initial stages, and most patients are unaware of the disease until obvious myelopathy symptoms ...

Role of arachidonic acid metabolism in intervertebral disc degeneration: identification of potential biomarkers and therapeutic targets via multi-omics analysis and artificial intelligence strategies.

BACKGROUND: Intervertebral disc degeneration (IVDD) is widely recognized as the primary etiological factor underlying low back pain, often necessitati...

Cerebrospinal fluid flow artifact reduction with deep learning to optimize the evaluation of spinal canal stenosis on spine MRI.

PURPOSE: The aim of study was to employ the Cycle Generative Adversarial Network (CycleGAN) deep learning model to diminish the cerebrospinal fluid (C...

Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty.

BACKGROUND: The radiographic assessment of bone morphology impacts implant selection and fixation type in total hip arthroplasty (THA) and is importan...

Assessing the Potential of a Deep Learning Tool to Improve Fracture Detection by Radiologists and Emergency Physicians on Extremity Radiographs.

RATIONALE AND OBJECTIVES: To evaluate the standalone performance of a deep learning (DL) based fracture detection tool on extremity radiographs and as...

Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy.

OBJECTIVE: Recent advancements in augmented reality led to planning and navigation systems for orthopedic surgery. However little is known about mixed...

Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology.

Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, s...

Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot.

This study proposed a strategy for a quick fault recovery response when an actuator failure problem occurred while a humanoid robot with 7-DOF anthrop...

Practical Applications of Artificial Intelligence in Spine Imaging: A Review.

Artificial intelligence (AI), a transformative technology with unprecedented potential in medical imaging, can be applied to various spinal pathologie...

Answer to the letter to the editor of H. Daungsupawong et al. concerning "Large language models: Are artificial intelligence-based chatbots a reliable source of patient information for spinal surgery?" by R. Stroop et al. (Eur Spine J [2023]; doi:10.1007/s00586-023-07975-z).

Quantitative analysis of optic disc changes in school-age children with ametropia based on artificial intelligence.

AIM: To explore changes in the optic disc and peripapillary atrophy (PPA) in school-age children with ametropia using color fundus photography combine...

Classification of rib fracture types from postmortem computed tomography images using deep learning.

Human or time resources can sometimes fall short in medical image diagnostics, and analyzing images in full detail can be a challenging task. With rec...

Weakly supervised deep learning for diagnosis of multiple vertebral compression fractures in CT.

OBJECTIVE: This study aims to develop a weakly supervised deep learning (DL) model for vertebral-level vertebral compression fracture (VCF) classifica...

Letter to the editor concerning "Large language models: Are artificial intelligence-based chatbots a reliable source of patient information for spinal surgery?" by R. Stroop et al. (Eur Spine J [2023]; doi:10.1007/s00586-023-07975-z).

Prediction and related genes of cancer distant metastasis based on deep learning.

Cancer metastasis is one of the main causes of cancer progression and difficulty in treatment. Genes play a key role in the process of cancer metastas...

A deep learning model adjusting for infant gender, age, height, and weight to determine whether the individual infant suit ultrasound examination of developmental dysplasia of the hip (DDH).

OBJECTIVE: To examine the correlation between specific indicators and the quality of hip joint ultrasound images in infants and determine whether the ...

MRI-based automated multitask deep learning system to evaluate supraspinatus tendon injuries.

OBJECTIVE: To establish an automated, multitask, MRI-based deep learning system for the detailed evaluation of supraspinatus tendon (SST) injuries.