Orthopedics

Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks.

PURPOSE: To standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.

Supervised and semi-supervised 3D organ localisation in CT images combining reinforcement learning with imitation learning.

Computer aided diagnostics often requires analysis of a region of interest (ROI) within a radiology scan, and the ROI may be an organ or a suborgan. Although deep learning algorithms have the ability to outperform other methods, they rely on the availability of a large amount of annotated data. Motivated by the need to address this limitation, an approach to localisation and detection of multiple ...

Effects of ground robot manipulation on hen floor egg reduction, production performance, stress response, bone quality, and behavior.

Reducing floor eggs in cage-free (CF) housing systems is among primary concerns for egg producers. The objective of this research was to evaluate the ...

Robotic Spine Surgery: Past, Present, and Future.

STUDY DESIGN: Systematic review.

Contact Compliance Based Visual Feedback for Tool Alignment in Robot Assisted Bone Drilling.

In recent decades, robot-assisted surgery has been proven superior at providing more accurate outcomes than the conventional one, particularly in mini...

Robot-Assisted Minimally Invasive Sacroiliac Joint Fusion for Sacroiliac Joint Dysfunction: 2-Dimensional Operative Video.

Deep learning algorithm to evaluate cervical spondylotic myelopathy using lateral cervical spine radiograph.

BACKGROUND: Deep learning (DL) is an advanced machine learning approach used in different areas such as image analysis, bioinformatics, and natural la...

Segmentation Performance Comparison Considering Regional Characteristics in Chest X-ray Using Deep Learning.

Chest radiography is one of the most widely used diagnostic methods in hospitals, but it is difficult to read clearly because several human organ tiss...

Vision Transformer for femur fracture classification.

INTRODUCTION: In recent years, the scientific community focused on developing Computer-Aided Diagnosis (CAD) tools that could improve clinicians' bone...

A method to classify bone marrow cells with rejected option.

Bone marrow cell morphology has always been an important tool for the diagnosis of blood diseases. Still, it requires years of experience from a suita...

Joint optic disk and cup segmentation for glaucoma screening using a region-based deep learning network.

OBJECTIVES: To develop and validate an end-to-end region-based deep convolutional neural network (R-DCNN) to jointly segment the optic disc (OD) and o...

Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using 3D deep learning model.

The most common cause of long-term disability and death in young adults is a traumatic brain injury. The decision for surgical intervention for cranio...

Diagnosis of Lumbar Spondylolisthesis Using Optimized Pretrained CNN Models.

Spondylolisthesis refers to the slippage of one vertebral body over the adjacent one. It is a chronic condition that requires early detection to preve...

Automatic diagnosis and grading of patellofemoral osteoarthritis from the axial radiographic view: a deep learning-based approach.

BACKGROUND: Patellofemoral osteoarthritis (PFOA) has a high prevalence and is assessed on axial radiography of the patellofemoral joint (PFJ). A deep ...

Can We Geographically Validate a Natural Language Processing Algorithm for Automated Detection of Incidental Durotomy Across Three Independent Cohorts From Two Continents?

BACKGROUND: Incidental durotomy is an intraoperative complication in spine surgery that can lead to postoperative complications, increased length of s...

Identification of osteoporosis using ensemble deep learning model with panoramic radiographs and clinical covariates.

Osteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lac...

Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.

OBJECTIVES: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to ra...

Effect of prostate and bony pelvic dimensions measured by preoperative magnetic resonance imaging on robot-assisted radical prostatectomy.

Bone pelvic dimensions and body habitus may have effects on robot-assisted radical prostatectomy (RARP). In this study, we examined the effects of bod...

Deep learning-based algorithm improved radiologists' performance in bone metastases detection on CT.

OBJECTIVES: To develop and evaluate a deep learning-based algorithm (DLA) for automatic detection of bone metastases on CT.

Prediction of femoral strength of elderly men based on quantitative computed tomography images using machine learning.

Hip fracture is the most common complication of osteoporosis, and its major contributor is compromised femoral strength. This study aimed to develop p...