A two-step neural network-based guiding system for obtaining reliable radiographs for critical shoulder angle measurement

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Background: The critical shoulder angle (CSA) has been reported to be highly associated with rotator cuff tears (RCTs) and an increased risk of RCT re-tears. However, the measurement of the CSA is greatly affected by the malpositioning of the shoulder. To address this issue, a two-step neural network-based guiding system was developed to obtain reliable CSA radiographs, and its feasibility and accuracy was evaluated. Methods: A total of 1,754 shoulder anteroposterior (AP) radiographs were retrospectively acquired to train and validate a two-step neural network-based guiding system to obtain reliable CSA radiographs. The study included patients aged 18 years or older who underwent X-rays and/or computed tomography (CT) scans of the shoulder. Patients who had undergone shoulder surgery, had a confirmed fracture, or were diagnosed with a musculoskeletal tumor or glenoid defect were excluded from the study. The system consisted of a two-step neural network that in the first step, localized the region of interest of the shoulder, and in the second step, classified the radiography according to type [i.e., ‘forward’ when the non-overlapping coracoid process is above the glenoid rim, ‘backward’ when the non-overlapping coracoid process is below or aligned with the glenoid rim, a ratio of the transverse to longitudinal diameter of the glenoid projection (RTL) ≤0.25, or a RTL >0.25]. The performance of the model was assessed in an offline, prospective manner, focusing on the sensitivity and specificity for the forward, backward, RTL ≤0.25, or RTL >0.25 types (denoted as SensF, B, −, + and SpecF, B, −, +, respectively), and Cohen’s kappa was also reported. Results: Of 273 cases in the offline prospective test, the SensF, SensB, Sens−, and Sens+ were 88.88% [95% confidence interval (CI): 50.67–99.41%], 94.11% (95% CI: 82.77–98.47%), 96.96% (95% CI: 91.94–99.02%), and 95.06% (95% CI: 87.15–98.40%), respectively. The SpecF, SpecB, Spec−, and Spec+ were 98.48% (95% CI: 95.90–99.51%), 99.55% (95% CI: 97.12–99.97%), 95.04% (95% CI: 89.65–97.81%), and 97.39% (93.69–99.03%), respectively. A high classification rate (93.41%; 95% CI: 89.14–96.24%) and almost perfect agreement (Cohen’s kappa: 0.903, 95% CI: 0.86–0.95) were achieved. Conclusions: The guiding system can rapidly and accurately classify the types of AP shoulder radiography, thereby guiding the adjustment of patient positioning. This will facilitate the rapid obtainment of reliable CSA radiography to measure the CSA on proper AP radiographs.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.