In this video from Orthopedics Today Hawaii, Adolph V. Lombardi Jr., MD, FACS, spoke about use of an artificial intelligence prediction model for knee arthroplasty. The study assessed the viability of an AI knee arthroplasty prediction model that used three-view radiographs to determine if patients with knee pain were candidates for total knee arthroplasty, unicompartmental knee arthroplasty or no knee arthroplasty surgery. He said results of the study show the AI prediction model was highly accurate. Researchers found the model predicted if patients had UKA or TKA 93% of the time, and surgery or no surgery 98% of the time. "With three basic X-rays, there is an artificial intelligence prediction model that works," Lombardi said.

Knee pain is undoubtedly the most common musculoskeletal symptom that impairs quality of life, confines mobility and functionality across all ages. Knee pain is clinically evaluated by routine radiographs, where the widespread adoption of radiographic images and their availability at low cost, make them the principle component in the assessment of knee pain and knee pathologies, such as arthritis, trauma, and sport injuries. However, interpretation of the knee radiographs is still highly subjective, and overlapping structures within the radiographs and the large volume of images needing to be analyzed on a daily basis, make interpretation challenging for both naive and experienced practitioners. There is thus a need to implement an artificial intelligence strategy to objectively and automatically interpret knee radiographs, facilitating triage of abnormal radiographs in a timely fashion. The current work proposes an accurate and effective pipeline for autonomous detection, localization, and classification of knee joint area in plain radiographs combining the You Only Look Once (YOLO v3) deep convolutional neural network with a large and fully-annotated knee radiographs dataset. The present work is expected to stimulate more interest from the deep learning computer vision community to this pragmatic and clinical application.

Artificial intelligence (AI) in healthcare is rapidly growing and offers novel options of data analysis. Machine learning (ML) represents a distinct application of AI, which is capable of generating predictions and has already been tested in different medical specialties with various approaches such as diagnostic applications, cost predictions or identification of risk factors. In orthopaedics, this technology has only recently been introduced and the literature on ML in knee arthroplasty is scarce. In this review, we aim to investigate which predictions are already feasible using ML models in knee arthroplasty to identify prerequisites for the effective use of this novel approach. For this reason, we conducted a systematic review of ML algorithms for outcome prediction in knee arthroplasty.

Sharon Regional Medical Center is offering advanced robotic technology to help give patients who need surgery faster recovery times following their procedures. The hospital offers the advanced robotics technologies to patients in Lawrence and Mercer counties, including two individual robotics systems used for general surgery as well as for knee arthroplasty or knee replacement surgery. The new technology includes the da Vinci XI Surgical System with Integrated Table Motion system, which can be used across a spectrum of minimally invasive surgeries including gynecology, urology, thoracic, cardiac and general surgery procedures. The second system is called the NAVIO Surgical System, which is used for knee arthroplasty procedures. Dr. Randy Hofius, a board-certified general surgeon who specializes in hernia and gallbladder surgeries, and Dr. Shateel Nijhawan, a board-certified general and bariatric surgeon, are performing procedures with the da Vinci system; Dr. Stephen Hand, a board-certified orthopedist, will perform the region's first robotic-assisted total knee arthroplasty in the Mercer and Lawrence county areas in the coming weeks.