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
Abstract
BACKGROUND

Assessment of the potential utility of deep learning with subsequent image analysis to automate the measurement of hallux valgus and intermetatarsal angles from radiographs to serve as a preoperative aid in establishing hallux valgus severity for clinical decision-making.

AIM

To investigate the accuracy of automated measurements of angles of hallux valgus from radiographs for further integration with the preoperative planning process.

METHODS

The data comprises 265 consecutive digital anteroposterior weightbearing foot radiographs. 181 radiographs were utilized for training (161) and validating (20) a U-Net neural network to achieve a mean Sørensen–Dice index > 97% on bone segmentation. 84 test radiographs were used for manual (computer assisted) and automated measurements of hallux valgus severity determined by hallux valgus (HVA) and intermetatarsal angles (IMA). The reliability of manual and computer-based measurements was calculated using the interclass correlation coefficient (ICC) and standard error of measurement (SEM). Inter- and intraobserver reliability coefficients were also compared. An operative treatment recommendation was then applied to compare results between automated and manual angle measurements.

RESULTS

Very high reliability was achieved for HVA and IMA between the manual measurements of three independent clinicians. For HVA, the ICC between manual measurements was 0.96-0.99. For IMA, ICC was 0.78-0.95. Comparing manual against automated computer measurement, the reliability was high as well. For HVA, absolute agreement ICC and consistency ICC were 0.97, and SEM was 0.32. For IMA, absolute agreement ICC was 0.75, consistency ICC was 0.89, and SEM was 0.21. Additionally, a strong correlation (0.80) was observed between our approach and traditional clinical adjudication for preoperative planning of hallux valgus, according to an operative treatment algorithm proposed by EFORT.

CONCLUSION

The proposed automated, artificial intelligence assisted determination of hallux valgus angles based on deep learning holds great potential as an accurate and efficient tool, with comparable accuracy to manual measurements by expert clinicians. Our approach can be effectively implemented in clinical practice to determine the angles of hallux valgus from radiographs, classify the deformity severity, streamline preoperative decision-making prior to corrective surgery.

Keywords: Computer-aided diagnosis; Artificial intelligence in orthopedics; Automated preoperative decision support; Deep learning; Medical imaging

Core Tip: This study presents an accurate method for automated assessment of angles of hallux valgus on high-resolution weight-bearing anteroposterior feet radiographs. Reference points are estimated according to the AOFAS standard on automatically segmented bones of the foot. The proposed method accurately calculates angles even in the case of significant toe deformity automating preoperative decision-making. Experimental results revealed high reliability of hallux valgus angle and intermetatarsal angle measurements between the proposed algorithm and medical doctors, achieving a correlation of almost 80%.