Mickley, John P.

Abstract Deep learning is revolutionizing medical imaging analysis by enabling the classification of various pathoanatomical conditions at scale. Unfortunately, there have been a limited number of accurate and efficient machine learning (ML) algorithms that have been developed for the diagnostic workup of morphological hip pathologies, including developmental dysplasia of the hip and femoroacetabular impingement. The current study reports on the performance of a novel ML model with YOLOv5 and ConvNeXt-Tiny architecture in predicting the morphological features of these conditions, including cam deformity, ischial spine sign, dysplastic appearance, and other abnormalities. The model achieved 78.0% accuracy for detecting cam deformity, 87.2% for ischial spine sign, 76.6% for dysplasia, and 71.6% for all abnormalities combined. The model achieved an Area under the Receiver Operating Curve of 0.89 for ischial spine sign, 0.80 for cam deformity, 0.80 for dysplasia, and 0.81 for all abnormalities combined. Inter-rater agreement among surgeons, assessed using Gwet’s AC1, was substantial for dysplasia (0.83) and all abnormalities (0.88), and moderate for ischial spine sign (0.75) and cam deformity (0.61).

Background: Arthroscopic diagnosis and treatment of femoroacetabular pathology has experienced significant growth in the last 30 years; nevertheless, reduced utilization of orthopaedic procedures has been observed among the underrepresented population. Purpose/Hypothesis: The purpose of this study was to examine racial differences in case incidence rates, outcomes, and complications in patients undergoing hip arthroscopy. It was hypothesized that racial and ethnic minority patients would undergo hip arthroscopy at a decreased rate compared with their White counterparts but that there would be no differences in clinical outcomes. Study Design: Cross-sectional study. Methods: The State Ambulatory Surgery and Services Database and the State Emergency Department Database of New York were queried for patients undergoing hip arthroscopy between 2011 and 2017. Patients were stratified into White and racial and ethnic minority races, and intergroup comparisons were performed for utilization over time, total charges billed per encounter, 90-day emergency department (ED) visits, and revision hip arthroscopy. Temporal trends in the utilization of hip arthroscopy were identified, and racial differences in secondary outcomes were analyzed with a semiparametric method known as targeted maximum likelihood estimation (TMLE) backed by a library of machine learning algorithms. Results: A total of 9745 patients underwent hip arthroscopy during the study period, with 1081 patients of minority race (11.1%). White patients underwent hip arthroscopy at 5.68 (95% CI, 4.98-6.48) times the incidence rate of racial and ethnic minority patients; these incidence rates grew annually at a ratio of 1.11 in White patients compared with 1.03 in racial and ethnic minority patients ( P < .001). Based on the TMLE, racial and ethnic minority patients were significantly more likely to incur higher costs ( P < .001) and visit the ED within 90 days ( P = .049) but had negligible differences in reoperation rates at a 2-year follow-up ( P = .53). Subgroup analysis identified that higher likelihood for 90-day ED admissions among racial and ethnic minority patients compared with White patients was associated with Medicare insurance ( P = .002), median income in the lowest quartile ( P = .012), and residence in low-income neighborhoods ( P = .006). Conclusion: Irrespective of insurance status, racial and ethnic minority patients undergo hip arthroscopy at a lower incidence and incur higher costs per surgical encounter.

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.

Objectives: Background: Arthroscopic diagnosis and treatment of femoroacetabular pathology has been increasingly used in the past thirty years with interventions resulting in improved hip function and ultimate delay of hip arthroplasty in a minimally invasive manner. Unfortunately, previous investigations have observed decreased rates of access, utilization of, and outcomes following orthopedic interventions such as hip arthroplasty in underrepresented patients. The purpose of this study is to examine racial differences in procedural rates, outcomes, and complications in patients undergoing hip arthroscopy. Methods: The State Ambulatory Surgery and Services Database (SASD) and State Emergency Department Database (SEDD) of New York were queried for patients undergoing hip arthroscopy from 2011 to 2017. The primary outcomes investigated were utilization over time, total charges billed per encounter, 90-day emergency department visits, and revision hip arthroscopy. Patients were stratified into White and non-White race, and intergroup differences were evaluated with descriptive statistics. Subgroup analysis was performed with linear mixed-effects models to identify significant interactions between race and individual variables that contributed to any differences in the outcomes of interest. Temporal trends in utilization of hip arthroscopy and concomitant procedures between the two groups were analyzed with Poisson regression modeling. Finally, targeted maximum likelihood estimation (TMLE) was performed to provide nonparametric estimates of the specific differences in the outcomes studied using machine learning ensembles while controlling for patient risk factors. Results: A total of 9,745 patients underwent hip arthroscopy during the study period, with 1,081 patients of non-White race (11.9%). Results of Poisson regression demonstrated an annual increase of 1.11 in the incidence rate of hip arthroscopy among White patients, compared to 1.03 for non-White patients (p<0.001), with this disparity projected to increase by 2040. Based on TMLE utilizing an ensemble of machine learning models, non-White patients were significantly more likely to incur higher costs (OR: 1.30, 95% CI: 1.24-1.37, p<0.001) and visit the emergency department within 90-days (OR: 1.09, 95% CI: 1.01, 1.18, p=0.05), but had negligible differences in reoperation rates at 90 days to 2 years (OR: 1.13, 95% CI: 0.78-1.63, p=0.53). Subgroup analysis identified higher likelihood for 90-day emergency department admissions among non-White patients compared to White patients, which were significantly compounded by Medicare insurance (OR: 2.95, 95% CI 1.46-5.95, p=0.002), median income in the lowest quartile (OR: 1.84, 95% CI: 1.2-2.61, p=0.012), and residence in low-income neighborhoods (OR: 2.05, 95% CI: 1.31-3.2, p=0.006). Subgroup analysis for charges billed and reoperation did not identify significant findings. Conclusions: Hip arthroscopy remains an increasingly utilized surgical technique for the treatment of a myriad of hip disorders. Unfortunately, racial disparities exist and are worsening over time. Irrespective of insurance status, non-white patients undergo hip arthroscopy at a lower rate, incur higher costs, and more frequently experience unexpected returns to the emergency department. Improved initiatives to improve the disparity in access to and outcomes following hip arthroscopy must be addressed to further its utility for all patients.