Deep Learning for Automated Classification of Hip Hardware on Radiographs.

Purpose: To develop a deep learning model for automated classification of orthopedic hardware on pelvic and hip radiographs, which can be clinically implemented to decrease radiologist workload and improve consistency among radiology reports.

Materials And Methods: Pelvic and hip radiographs from 4279 studies in 1073 patients were retrospectively obtained and reviewed by musculoskeletal radiologists. Two convolutional neural networks, EfficientNet-B4 and NFNet-F3, were trained to perform the image classification task into the following most represented categories: no hardware, total hip arthroplasty (THA), hemiarthroplasty, intramedullary nail, femoral neck cannulated screws, dynamic hip screw, lateral blade/plate, THA with additional femoral fixation, and post-infectious hip.

Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports.

Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting.

Definitive Chemoradiation With Full-dose Gemcitabine for Unresectable Pancreatic Cancer: Efficacy of Involved-Field Radiotherapy.

Objectives: Definitive chemoradiotherapy for unresectable pancreatic cancer has traditionally involved 5-fluorouracil-based chemotherapy. Our institution has a long history of combining gemcitabine and radiotherapy (RT), and performed a retrospective review of all patients treated in this manner.

Materials And Methods: We reviewed the records of 180 patients treated from 1999 to 2012.