Kwolek K, Gądek A, Kwolek K, Lechowska-Liszka A, Malczak M, Liszka H. Artificial intelligence-based diagnosis of hallux valgus interphalangeus using anteroposterior foot radiographs. World J Orthop 2025; 16(6): 103832 [DOI: 10.5312/wjo.v16.i6.103832]
Corresponding Author of This Article
Henryk Liszka, MD, PhD, Professor, Department of Orthopedics and Physiotherapy, Jagiellonian University Collegium Medicum, Macieja Jakubowskiego 2, Kraków 30-688, Małopolska, Poland. liszkah@gmail.com
Research Domain of This Article
Orthopedics
Article-Type of This Article
Observational Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Orthop. Jun 18, 2025; 16(6): 103832 Published online Jun 18, 2025. doi: 10.5312/wjo.v16.i6.103832
Artificial intelligence-based diagnosis of hallux valgus interphalangeus using anteroposterior foot radiographs
Konrad Kwolek, Artur Gądek, Kamil Kwolek, Agnieszka Lechowska-Liszka, Michał Malczak, Henryk Liszka
Konrad Kwolek, Michał Malczak, 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 Orthopedics and Rheumoorthopedics, Professor Adam Gruca Orthopedic and Trauma Teaching Hospital, Otwock 05-400, Poland
Agnieszka Lechowska-Liszka, Institute of Applied Sciences, University of Physical Education in Krakow, Kraków 31-571, Małopolska, Poland
Author contributions: Kwolek K and Kwolek K elaborated analytic tools; Kwolek K, Kwolek K, Malczak M, and Liszka H wrote the paper; Kwolek K, Kwolek K, and Liszka H designed research and performed research; Kwolek K, Gądek A, Kwolek K, Lechowska-Liszka A, Malczak M, and Liszka H analyzed data; all of the authors read and approved the final version of the manuscript to be published.
Institutional review board statement: This study protocol was reviewed and approved by authors’ institution.
Informed consent statement: The informed consent statement has been provided.
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.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: No additional data are available.
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, Professor, Department of Orthopedics and Physiotherapy, Jagiellonian University Collegium Medicum, Macieja Jakubowskiego 2, Kraków 30-688, Małopolska, Poland. liszkah@gmail.com
Received: December 2, 2024 Revised: March 13, 2025 Accepted: May 13, 2025 Published online: June 18, 2025 Processing time: 198 Days and 11.7 Hours
Abstract
BACKGROUND
A recently developed method enables automated measurement of the hallux valgus angle (HVA) and the first intermetatarsal angle (IMA) from weight-bearing foot radiographs. This approach employs bone segmentation to identify anatomical landmarks and provides standardized angle measurements based on established guidelines. While effective for HVA and IMA, preoperative radiograph analysis remains complex and requires additional measurements, such as the hallux interphalangeal angle (IPA), which has received limited research attention.
AIM
To expand the previous method, which measured HVA and IMA, by incorporating the automatic measurement of IPA, evaluating its accuracy and clinical relevance.
METHODS
A preexisting database of manually labeled foot radiographs was used to train a U-Net neural network for segmenting bones and identifying landmarks necessary for IPA measurement. Of the 265 radiographs in the dataset, 161 were selected for training and 20 for validation. The U-Net neural network achieves a high mean Sørensen-Dice index (> 0.97). The remaining 84 radiographs were used to assess the reliability of automated IPA measurements against those taken manually by two orthopedic surgeons (OA and OB) using computer-based tools. Each measurement was repeated to assess intraobserver (OA1 and OA2) and interobserver (OA2 and OB) reliability. Agreement between automated and manual methods was evaluated using the Intraclass Correlation Coefficient (ICC), and Bland-Altman analysis identified systematic differences. Standard error of measurement (SEM) and Pearson correlation coefficients quantified precision and linearity, and measurement times were recorded to evaluate efficiency.
RESULTS
The artificial intelligence (AI)-based system demonstrated excellent reliability, with ICC3.1 values of 0.92 (AI vsOA2) and 0.88 (AI vs OB), both statistically significant (P < 0.001). For manual measurements, ICC values were 0.95 (OA2vs OA1) and 0.95 (OA2vs OB), supporting both intraobserver and interobserver reliability. Bland-Altman analysis revealed minimal biases of: (1) 1.61° (AI vs OA2); and (2) 2.54° (AI vs OB), with clinically acceptable limits of agreement. The AI system also showed high precision, as evidenced by low SEM values: (1) 1.22° (OA2vs OB); (2) 1.77° (AI vs OA2); and (3) 2.09° (AI vs OB). Furthermore, Pearson correlation coefficients confirmed strong linear relationships between automated and manual measurements, with r = 0.85 (AI vs OA2) and r = 0.90 (AI vs OB). The AI method significantly improved efficiency, completing all 84 measurements 8 times faster than manual methods, reducing the time required from an average 36 minutes to just 4.5 minutes.
CONCLUSION
The proposed AI-assisted IPA measurement method shows strong clinical potential, effectively corresponding with manual measurements. Integrating IPA with HVA and IMA assessments provides a comprehensive tool for automated forefoot deformity analysis, supporting hallux valgus severity classification and preoperative planning, while offering substantial time savings in high-volume clinical settings.
Core Tip: This study presents an automated method for evaluating hallux interphalangeus angle using high-resolution, weight-bearing anteroposterior foot radiographs. Reference points are identified based on defined criteria applied to the automatically segmented bones of the hallux. Despite the anatomical complexity of the distal phalanx, the proposed technique reliably calculates the interphalangeal angle. Experimental findings show high consistency between the algorithm's measurements and those performed by clinicians.
