AKIRA: Deep learning tool for image standardization, implant detection and arthritis grading to establish a radiographic registry in patients with anterior cruciate ligament injuries.

Purpose: Developing large-scale, standardized radiographic registries for anterior cruciate ligament (ACL) injuries with artificial intelligence (AI) tools can enhance personalized orthopaedics. We propose deploying Artificial Intelligence for Knee Imaging Registration and Analysis (AKIRA), a trio of deep learning (DL) algorithms, to automatically classify and annotate radiographs. We hypothesize that algorithms can efficiently organize radiographs based on laterality, projection, identify implants and classify osteoarthritis (OA) grade.

A Deep Learning Tool for Minimum Joint Space Width Calculation on Antero-posterior Knee Radiographs.

Background: Minimum joint space width (mJSW) is an important continuous quantitative metric of osteoarthritis progression in the knee. The purpose of this study was to develop an automated measurement algorithm for mJSW in the medial and lateral compartments of the knee that can flexibly handle native knees and knees after arthroplasty.

Methods: We developed an end-to-end algorithm consisting of a deep learning segmentation model plus a computer vision algorithm to measure mJSW in the medial and lateral compartments of the knee.

TKA-AID: An Uncertainty-Aware Deep Learning Classifier to Identify Total Knee Arthroplasty Implants.

Background: A drastic increase in the volume of primary total knee arthroplasties (TKAs) performed nationwide will inevitably lead to higher volumes of revision TKAs in which the primary knee implant must be removed. An important step in preoperative planning for revision TKA is implant identification, which is time-consuming and difficult even for experienced surgeons. We sought to develop a deep learning algorithm to automatically identify the most common models of primary TKA implants.

Impact of Leg Position on Measurements Used to Detect Femoral Component Subsidence in THA.

Background: A fully automated artificial intelligence-based tool was developed to detect and quantify femoral component subsidence between serial radiographs. However, it did not account for measurement errors due to leg position differences, such as rotation or flexion, between comparative radiographs. If there are small differences in rotation or flexion of the leg between comparative radiographs, the impact on subsidence measurement is unclear.

Uncertainty-Aware Deep Learning Characterization of Knee Radiographs for Large-Scale Registry Creation.

Background: We present an automated image ingestion pipeline for a knee radiography registry, integrating a multilabel image-semantic classifier with conformal prediction-based uncertainty quantification and an object detection model for knee hardware.

Methods: Annotators retrospectively classified 26,000 knee images detailing presence, laterality, prostheses, and radiographic views. They further annotated surgical construct locations in 11,841 knee radiographs.

The Impact of Leg Length and Offset Change on Dislocation Risk Following Primary Total Hip Arthroplasty.

Background: Soft tissue management in total hip arthroplasty includes appropriate restoration and/or alteration of leg length (LL) and offset to re-establish natural hip biomechanics. The purpose of this study was to evaluate the effect of LL and offset-derived variables in a multivariable survival model for dislocation.

Methods: Clinical, surgical, and radiographic data was retrospectively acquired for 12,582 patients undergoing primary total hip arthroplasty at a single institution from 1998 to 2018.

A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence.

The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive.

Preoperative Frontal and Parietal Bone Thickness Assessment to Predict Blood Loss and Transfusion During Extended Suturectomy for Isolated Sagittal Craniosynostosis.

Objective: To predict the morbidity of sagittal suturectomy using preoperative computer tomographic measurement of frontal and parietal bone thickness in osteotomy sites.

Design: Retrospective analysis.

Setting: Tertiary children's hospital.

Perioperative Morbidity of Secondary Frontal Orbital Advancement After Initial Frontal Orbital Advancement.

Frontal orbital advancement (FOA) is frequently performed for patients with syndromic and/or multisuture craniosynostosis. A small proportion of patients who undergo FOA have unfavorable growth and subsequently require a second FOA later in life; however, the perioperative risks associated with this second procedure are not well studied. We report results from a retrospective review of FOAs conducted from 2007 to 2022 at a single site with the same craniofacial surgeon.