Fully Automated Analysis of the Anatomic and Mechanical Axes From Pediatric Standing Lower Limb Radiographs Using Convolutional Neural Networks

• Published in: Journal of pediatric orthopaedics Vol. 44; no. 4; p. 244
• Main Authors: Murad, Yousif, Chhina, Harpreet, Cooper, Anthony
• Format: Journal Article
• Language: English
• Published: United States 01.04.2024
• Subjects: Child; Femur - diagnostic imaging; Femur - surgery; Humans; Lower Extremity - diagnostic imaging; Lower Extremity - surgery; Neural Networks, Computer; Radiography; Tibia - diagnostic imaging; Tibia - surgery
• ISSN: 1539-2570
• DOI: 10.1097/BPO.0000000000002611

Lower limb alignment is the quantification of a set of parameters that are commonly measured radiographically to test for and track a wide range of skeletal pathologies. Determining limb alignment is a commonly performed yet laborious task in the pediatric orthopaedic setting and is therefore an interesting goal for automation. We employ a machine learning approach using convolutional neural networks (CNNs) to segment pediatric weight-bearing lower limb radiographs. The results are then used with custom Matlab code to extract anatomic landmarks and to determine lower limb alignment parameters. Measurements obtained from the automated workflow proposed here were compared with manual measurements performed by orthopaedic surgery fellows. Mechanical axis deviation was determined within a mean of 2.02 mm. Lateral distal femoral angle and medial proximal tibial angle were determined with a mean deviation of 1.73 and 2.90 degrees, respectively. The calculation speed for the full set of mechanical and anatomic axis parameters was found to be ~2 seconds per radiograph. The CNN-based approach proposed in this work was shown to produce results comparable to orthopaedic surgery fellows at fast calculation speed. Although further work is needed to validate these results against radiographs and measurements from other centers, we see this as a promising start and a functional path that can be employed in further research. CNNs are a promising approach to automating commonly performed, repetitive tasks, especially those pertaining to image processing. The time savings are particularly important in clinical research applications where large sets of radiographs are routinely available and require analysis. With further development of these algorithms, we anticipate significantly improved agreement with expert-measured results and the calculation speed.