A practical guide to the development and deployment of deep learning models for the orthopedic surgeon: part II

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. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy

Deep learning is a subset of artificial intelligence (AI) with enormous potential to transform orthopaedic surgery. As has already become evident with the deployment of Large Language Models (LLMs) like ChatGPT (OpenAI Inc.), deep learning can rapidly enter clinical and surgical practices. As such, it is imperative that orthopaedic surgeons acquire a deeper understanding of the technical terminology, capabilities and limitations associated with deep learning models. The focus of this series thus far has been providing surgeons with an overview of the steps needed to implement a deep learning-based pipeline, emphasizing some of the important technical details for surgeons to understand as they encounter, evaluate or lead deep learning projects. However, this series would be remiss without providing practical examples of how deep learning models have begun to be deployed and highlighting the areas where the authors feel deep learning may have the most profound potential. While computer vision applications of deep learning were the focus of Parts I and II, due to the enormous impact that natural language processing (NLP) has had in recent months, NLP-based deep learning models are also discussed in this final part of the series. In this review, three applications that the authors believe can be impacted the most by deep learning but with which many surgeons may not be familiar are discussed: (1) registry construction, (2) diagnostic AI and (3) data privacy. Deep learning-based registry construction will be essential for the development of more impactful clinical applications, with diagnostic AI being one of those applications likely to augment clinical decision-making in the near future. As the applications of deep learning continue to grow, the protection of patient information will become increasingly essential; as such, applications of deep learning to enhance data privacy are likely to become more important than ever before. Level of Evidence: Level IV.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

Prognostic value of the Walch classification for patients before and after shoulder arthroplasty performed for osteoarthritis with an intact rotator cuff

BACKGROUND

The Walch classification is commonly used by surgeons when determining the treatment of osteoarthritis (OA). However, its utility in prognosticating patient clinical state before and after TSA remains unproven. We assessed the prognostic value of the modified Walch glenoid classification on preoperative clinical state and postoperative clinical and radiographic outcomes in total shoulder arthroplasty (TSA).

METHODS

A prospectively collected, multicenter database for a single-platform TSA system was queried for patients with rotator cuff-intact OA and minimum 2 year follow-up after anatomic (aTSA) and reverse TSA (rTSA). Differences in patient-reported outcome scores (Simple Shoulder Test, American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form, Shoulder Pain and Disability Index, visual analog scale for pain, Shoulder Function score), combined patient-reported and clinical-input scores (Constant, University of California-Los Angeles shoulder score, Shoulder Arthroplasty Smart Score), active range of motion values (forward elevation [FE], abduction, external rotation [ER], internal rotation [IR], and radiographic outcomes (humeral and glenoid radiolucency line rates, scapula notching rate) were stratified and compared by glenoid deformity type per the Walch classification for aTSA and rTSA cohorts. Comparisons were performed to assess the ability of the Walch classification to predict the preoperative, postoperative, and improved state after TSA.

RESULTS

1008 TSAs were analyzed including 576 aTSA and 432 rTSA. Comparison of outcomes between Walch glenoid types resulted in 15 pairwise comparisons of 12 clinical outcome metrics, yielding 180 total Walch glenoid pairwise comparisons for each clinical state (preoperative, postoperative, improvement). Of the 180 possible pairwise Walch glenoid type and metric comparisons studied for aTSA and rTSA cohorts, <6% and <2% significantly differed in aTSA and rTSA cohorts, respectively. Significant differences based on Walch type were seen after adjustment for multiple pairwise comparisons in the aTSA cohort for FE and ER preoperatively, the Constant score postoperatively, and for abduction, FE, ER, Constant score, and SAS score for pre- to postoperative improvement. In the rTSA cohort, significant differences were only seen in abduction and Constant score both postoperatively and for pre- to postoperative improvement. There were no statistically significant differences in humeral lucency rate, glenoid lucency rate (aTSA), scapular notching rate (rTSA), complication rates, or revision rates between Walch glenoid types after TSA.

CONCLUSION

Although useful for describing degenerative changes to the glenohumeral joint, we demonstrate a weak association between preoperative glenoid morphology according to the Walch classification and clinical state when evaluating patients undergoing TSA for rotator cuff-intact OA. Alternative glenoid classification systems or predictive models should be considered to provide more precise prognoses for patients undergoing TSA for rotator cuff-intact OA.