Evaluation of a Weightbearing CT Artificial Intelligence-Based Automatic Measurement for the M1-M2 Intermetatarsal Angle in Hallux Valgus

Abstract

Background:

Weightbearing cone beam computed tomography (WBCT) has been gaining traction as a useful imaging modality in the diagnosis and follow-up of foot and ankle musculoskeletal pathologies due to the ability to perform quick, low-dose, 3-dimensional (3D) scans. However, the resulting wealth of 3D data renders daily clinical use time-consuming. The aim of this study was to evaluate a new artificial intelligence (AI)–based automatic measurement for the M1-M2 intermetatarsal angle (IMA) in hallux valgus (HV). We hypothesized that automatic and manual measurements would have a strong correlation, and that the AI software would yield better reproducibility and would be faster compared with manual measurements.

Methods:

This was a multicenter retrospective comparative case-control study in which a total of 128 feet were included from 93 patients who underwent WBCT scans as part of their routine follow-up: 59 feet with symptomatic HV and 69 controls. The IMA was measured automatically using the AI software and manually on digitally reconstructed radiographs (DRRs). The AI software produced both an automatic 2D (auto 2D) and 3D (auto 3D) measurement.

Results:

There were strong intermethod correlations between the DRR IMA and the auto 2D (HV, r = 0.61; control, r = 0.60; all P < .0001) and auto 3D (HV, r = 0.63; control, r = 0.52; all P < .0001) measurements, respectively. The intrasoftware reproducibility was very close to 100%. Measurements took 23.6 ± 2.31 seconds and 14.5 ± 1.18 seconds, respectively, when taken manually on DRRs and automatically. Controls demonstrated a mean DRR IMA of 8.6 (95% CI, 8.1-9.1), mean auto 2D of 11.2 (95% CI, 10.7-11.7), and mean auto 3D IMA of 11.0 (95% CI, 10.5-11.5). The HV group demonstrated significantly increased IMA compared with controls (P < .0001), with a mean DRR IMA of 15.4 (95% CI, 14.8-16.1), mean auto 2D of 17.8 (95% CI, 17.2-18.4), and mean auto 3D IMA of 16.8 (95% CI, 16.8-17.4).

Conclusion:

Measurements generated by the WBCT AI-based automatic measurement system for IMA demonstrated strong correlations with manual measurements, with near-perfect reproducibility. Further developments are warranted in order to make this tool more usable in daily practice, particularly with respect to its use in the presence of hardware in the foot.

Level of Evidence:

Level III, retrospective comparative study.

Keywords

weightbearing CT WBCT artificial intelligence intermetatarsal angle IMA hallux valgus machine learning

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