Basic Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Orthop. Nov 18, 2023; 14(11): 800-812
Published online Nov 18, 2023. doi: 10.5312/wjo.v14.i11.800
Automated decision support for Hallux Valgus treatment options using anteroposterior foot radiographs
Konrad Kwolek, Artur Gądek, Kamil Kwolek, Radek Kolecki, Henryk Liszka
Konrad Kwolek, Radek Kolecki, Department of Orthopedics and Traumatology, University Hospital, Kraków 30-688, Małopolska, Poland
Artur Gądek, Henryk Liszka, Department of Orthopedics and Physiotherapy, Jagiellonian University Collegium Medicum, Kraków 30-688, Małopolska, Poland
Kamil Kwolek, Department of Spine Disorders and Orthopedics, Gruca Orthopedic and Trauma Teaching Hospital, Otwock 05-400, Poland
Author contributions: Kwolek Ko, Liszka H, Kwolek Ka designed research; Kwolek Ko, Kwolek Ka performed research; Kwolek Ko, Kwolek Ka elaborated analytic tools, Kwolek Ko, Liszka H, Gądek A, Kwolek Ka analyzed data; Kwolek Ko, Liszka H, Kolecki R, Kwolek Ka wrote the paper.
Institutional review board statement: This study protocol got an official statement that human and animal studies received waiver of the approval requirement from the ethics committee.
Informed consent statement: This study did not involve human experiments and does not require the signing of an informed consent form.
Conflict-of-interest statement: The authors have no conflict of interest concerning the materials or methods used in this study or the findings specified in this article.
Data sharing statement: No additional data are available.
ARRIVE guidelines statement: The authors have read the ARRIVE guidelines, and the manuscript was prepared and revised according to the ARRIVE guidelines.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Henryk Liszka, MD, PhD, Academic Research, Professor, Research Scientist, Surgeon, Department of Orthopedics and Physiotherapy, Jagiellonian University Collegium Medicum, Macieja Jakubowskiego 2, Kraków 30-688, Poland. liszkah@gmail.com
Received: August 25, 2023
Peer-review started: August 25, 2023
First decision: September 28, 2023
Revised: October 11, 2023
Accepted: October 30, 2023
Article in press: October 30, 2023
Published online: November 18, 2023
Processing time: 82 Days and 9.4 Hours
ARTICLE HIGHLIGHTS
Research background

Recent advances in artificial intelligence and deep learning has spurred innovations in medical imaging modalities, resulting in enhanced visualisation possibilities. Additionally, there is a growing interest in the automation of regular diagnostic procedures alongside orthopedic measurements.

Research motivation

So far, no reliable and automated method has been developed for measuring angles of foot bones in significant deformities of the big toe from radiographs according to AOFAS. Likewise, there is no system for automated preoperative decision-making.

Research objectives

The aim of our research was to develop a robust automated method for measuring angles of hallux valgus on radiographs according to AOFAS guidelines, to determine the accuracy of this method, to compare it against expert clinician measurements, and to develop a preoperative decision-making systems.

Research methods

The bones which are necessary to determine the angles of hallux valgus, obtained on anteroposterior weight-bearing feet radiograms were segmented by a U-Net. The bone axes were determined, and then the reference points for determining the hallux valgus angles (HVA) and intermetatarsal angles (IMA) were found. The interclass correlation coefficient and standard error for single measurements were used to calculate the agreement between manual and automatic measurements. Finally, the correlation between the decisions of our algorithm and clinical adjudication for preoperative planning of hallux valgus was investigated.

Research results

The key foot bones were segmented from anteroposterior feet radiograms by the U-Net neural network with high accuracy (average Sørensen–Dice index larger than 97%). Such a precise segmentation enabled the accurate determination of bone axes and the required reference points. Excellent agreement was achieved between manual and automated measurements of both angles. For HVA, absolute agreement interclass correlation coefficient (AA-ICC) and consistency ICC (C-ICC) were 0.97, and standard error of measurement (SEM) was 0.32. For IMA, AA-ICC was 0.75, C-ICC was 0.89, and SEM was 0.21. The proposed hallux valgus treatments based on HVA and IMA measured automatically correlated well with those proposed by orthopedic surgeons performing manual angle measurements.

Research conclusions

The proposed artificial intelligence powered automation for evaluating angles of hallux valgus through deep learning is a precise, yielding measurements akin to those conducted manually by experienced clinicians. This offers promising clinical applications such as facilitating the automated determination of angles of hallux valgus from X-ray images, categorizing the extent of deformity, and recommending a specific protocol for corrective surgery.

Research perspectives

Future research will focus on automating the measurements of remaining angles and parameters of forefoot deformation along its greater clinical implementation to further enhance diagnostic accuracy and improve patient outcomes.