Hussain A, Lee C, Hu E, Amirouche F. Deep learning automation of radiographic patterns for hallux valgus diagnosis. World J Orthop 2024; 15(2): 105-109 [PMID: 38464350 DOI: 10.5312/wjo.v15.i2.105]
Corresponding Author of This Article
Farid Amirouche, PhD, Professor, Department of Orthopedics Surgery, University of Illinois at Chicago, 835 S. Wolcott Ave, Room E270, Chicago, IL 60612, United States. amirouch@uic.edu
Research Domain of This Article
Mathematical & Computational Biology
Article-Type of This Article
Editorial
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. Feb 18, 2024; 15(2): 105-109 Published online Feb 18, 2024. doi: 10.5312/wjo.v15.i2.105
Deep learning automation of radiographic patterns for hallux valgus diagnosis
Angela Hussain, Cadence Lee, Eric Hu, Farid Amirouche
Angela Hussain, Cadence Lee, Eric Hu, Department of Orthopaedic Surgery, University of Illinois College of Medicine, Chicago, IL 60612, United States
Farid Amirouche, Department of Orthopaedics Surgery, University of Illinois at Chicago, Chicago, IL 60612, United States
Farid Amirouche, Department of Orthopaedic Surgery, Northshore University Health System, Skokie, IL 6007, United States
Co-first authors: Angela Hussain and Cadence Lee.
Author contributions: Hussain A and Lee C contributed equally to this work as co-first authors; Hussain A, Lee C, Hu E, and Amirouche F contributed to this paper; Amirouche F and Lee C designed the concept and outline; Hussain A, Hu E, and Lee C contributed to the writing and review of literature; Amirouche F was responsible for oversight and coordination; and all authors contributed to the editing of the manuscript.
Conflict-of-interest statement: The authors have declared that no conflicts of interest exist.
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: Farid Amirouche, PhD, Professor, Department of Orthopedics Surgery, University of Illinois at Chicago, 835 S. Wolcott Ave, Room E270, Chicago, IL 60612, United States. amirouch@uic.edu
Received: November 30, 2023 Peer-review started: November 30, 2023 First decision: December 7, 2023 Revised: December 22, 2023 Accepted: January 4, 2024 Article in press: January 4, 2024 Published online: February 18, 2024 Processing time: 68 Days and 5.4 Hours
Abstract
Artificial intelligence (AI) and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine. Deep learning has already been utilized for automated detection of pneumonia from chest radiographs, diabetic retinopathy, breast cancer, skin carcinoma classification, and metastatic lymphadenopathy detection, with diagnostic reliability akin to medical experts. In the World Journal of Orthopedics article, the authors apply an automated and AI-assisted technique to determine the hallux valgus angle (HVA) for assessing HV foot deformity. With the U-net neural network, the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior high-resolution radiographs. The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability. The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency. Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies, this work adds to the growing evidence supporting AI as a powerful diagnostic tool.
Core Tip: This editorial summarizes and outlines the original paper “Automated decision support for Hallux valgus treatment options using anteroposterior foot radiographs”. We summarize the scope of the deep learning process and compare it to existing artificial intelligence studies used in clinical diagnostic studies. We additionally describe its limitations and impact in the field of automated diagnostic tools.
