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ARTICLE
AUTOMATED RADIOGRAPHIC ASSESSMENT OF THE WEIGHT-BEARING FOOT: A DEEP LEARNING APPROACH TO ENHANCING MEASUREMENT RELIABILITY
Issue Vol. 1 No. 01 (2024): Volume 01 Issue 01 --- Section Articles --- Published Date: 2024-12-28
Abstract
Background: The clinical evaluation and management of common foot and ankle deformities, such as hallux valgus, are critically dependent on precise measurements of specific angles from weight-bearing radiographs [19]. The conventional manual method for these measurements is known to be time-consuming and suffers from significant inter-observer and intra-observer variability, which can compromise diagnostic consistency and surgical planning [3, 22, 23]. Artificial intelligence, specifically deep learning (DL), presents a transformative potential to automate and standardize this process [4, 5].
Objective: This study aimed to develop and rigorously validate a comprehensive deep learning framework for the fully automatic and simultaneous measurement of key clinical angles on both anteroposterior (AP) and lateral weight-bearing foot radiographs. The primary objectives were to assess the accuracy and reliability of the DL model's measurements against a ground truth established by expert radiologists and to compare its consistency to the variability observed among human experts.
Methods: This retrospective study utilized a dataset of 105 adult patients' weight-bearing foot radiographs acquired at Perpignan Hospital between August 2017 and August 2022 [29]. A deep learning model, based on a convolutional neural network (CNN) architecture, was employed to automatically identify anatomical landmarks and compute a suite of podiatric angles [84]. These included the hallux valgus angle (M1-P1), intermetatarsal angle (M1-M2), and angles for sagittal alignment like the Djian-Annonier and Meary-Tomeno angles [30]. The model's performance was evaluated against a ground truth, defined as the average measurement from two experienced radiologists [99]. Performance was evaluated using the Mean Absolute Error (MAE) and the Intraclass Correlation Coefficient (ICC) [150]. Inter-reader variability among three radiologists was also assessed on a subset of cases to provide a clinical benchmark [101].
Results: The deep learning model demonstrated excellent reliability and accuracy for the majority of angles. For the frontal view, the MAE was lowest for the M1-M2 angle (0.96°) and ICCs indicated excellent agreement for M1-P1, M1-M2, and M1-M5 [32, 33]. For the lateral view, the MAE for the calcaneal pitch was 0.92° and the Meary-Tomeno angle was 2.83°, with all lateral angles showing excellent ICCs (≥0.93) [34]. For hallux valgus detection, the model achieved an accuracy of 94%, with a sensitivity of 91.1% and a specificity of 97.2% [35]. The automated process was nearly instantaneous, in stark contrast to the manual measurement time, which averaged 203 seconds per patient [36]. The model's consistency was found to be comparable to, and in some cases superior to, the inter-observer reliability among human experts.
Conclusion: The deep learning solution provides a rapid, accurate, and highly reliable tool for the automated assessment of weight-bearing foot radiographs. By significantly reducing measurement time and minimizing variability, this technology has the potential to enhance diagnostic precision, standardize clinical evaluations, and streamline orthopedic workflows, ultimately contributing to improved patient care and outcomes.
Keywords
References
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