A fully automatic system to assess foot collapse on lateral weight-bearing foot radiographs: A pilot study.

Background: Foot collapse is primarily diagnosed and monitored using lateral weight-bearing foot x-ray images. There are several well-validated measurements which aid assessment. However, these are subject to inter- and intra-user variability.

Objective: To develop and validate a software system for the fully automatic assessment of radiographic changes associated with foot collapse; automatically generating measurements for calcaneal tilt, cuboid height and Meary's angle.

Methods: This retrospective study was approved by the Health Research Authority (IRAS 244852). The system was developed using lateral weight-bearing foot x-ray images, and evaluated against manual measurements from five clinical experts. The system has two main components: (i) a Random Forest-based point-finder to outline the bones of interest; and (ii) a geometry-calculator to generate the measurements based on the point positions from the point-finder. The performance of the point-finder was assessed using the point-to-point error (i.e. the mean absolute distance between each found point and the equivalent ground truth point, averaged over all points per image). For assessing the performance of the geometry-calculator, linear mixed models were fitted to estimate clinical inter-observer agreement and to compare the performance of the software system to that of the clinical experts.

Results: A total of 200 images were collected from 79 subjects (mean age: 56.4 years ±12.9 SD, 30/49 females/males). There was good agreement among all clinical experts with intraclass correlation estimates between 0.78 and 0.86. The point-finder achieved a median point-to-point error of 2.2 mm. There was no significant difference between the clinical and automatically generated measurements using the point-finder points, suggesting that the fully automatically obtained measurements are in agreement with the manually obtained measurements.

Conclusions: The proposed system can be used to support and automate radiographic image assessment for diagnosing and managing foot collapse, saving clinician time, and improving patient outcomes.