Technical Note: Neural Network Architectures for Self-Supervised Body Part Regression Models with Automated Localized Segmentation Application.

The advancement of medical image deep learning necessitates tools that can accurately identify body regions from whole-body scans to serve as an essential pre-processing step for downstream tasks. Typically, these deep learning models rely on labeled data and supervised learning, which is labor-intensive. However, the emergence of self-supervised learning is revolutionizing the field by eliminating the need for labels.

Diagnosing the Severity of Knee Osteoarthritis Using Regression Scores From Artificial Intelligence Convolution Neural Networks.

Background: This study focused on using deep learning neural networks to classify the severity of osteoarthritis in the knee. A continuous regression score of osteoarthritis severity has yet to be explored using artificial intelligence machine learning, which could offer a more nuanced assessment of osteoarthritis.

Materials And Methods: This study used 8260 radiographic images from The Osteoarthritis Initiative to develop and assess four neural network models (VGG16, EfficientNetV2 small, ResNet34, and DenseNet196).

A Systematic Review of the Usage of Lidocaine in Hip Replacement Surgery.

Hip replacement procedures, professionally known as hip arthroplasty, are one of the most common orthopedic procedures. Due to the variation in this procedure, the use and types of anesthetics differ. One such commonly used anesthetic is lidocaine.