Case Control Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Orthop. Dec 18, 2024; 15(12): 1146-1154
Published online Dec 18, 2024. doi: 10.5312/wjo.v15.i12.1146
Utilization of artificial intelligence in the diagnosis of pes planus and pes cavus with a smartphone camera
Samir Ghandour, Anton Lebedev, Wei-Shao Tung, Konstantin Semianov, Artem Semjanow, Christopher W DiGiovanni, Soheil Ashkani-Esfahani, Lorena Bejarano Pineda
Samir Ghandour, Anton Lebedev, Konstantin Semianov, Artem Semjanow, Christopher W DiGiovanni, Soheil Ashkani-Esfahani, Lorena Bejarano Pineda, Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States
Wei-Shao Tung, Department of Orthopaedics, Massachusetts General Hospital, Boston, MA 02114, United States
Co-first authors: Samir Ghandour and Anton Lebedev.
Author contributions: Ghandour S, Lebedev A, Tung WS, Semianov K, Semjanow A, DiGiovanni CW, Ashkani-Esfahani S, and Pineda LB have all contributed equally to the conceptualization, study design, data collection, data analysis, manuscript writing, and manuscript revision; all of the authors read and approved the final version of the manuscript to be published.
Institutional review board statement: This project (No. 2022P001722) has been reviewed and approved by the Mass General Brigham Institutional Review Board.
Informed consent statement: All participants have been provided with a consent form which they have signed prior to their participation in the study.
Conflict-of-interest statement: The authors declare no conflict of interest.
Data sharing statement: The authors authorize fully the use and sharing of the data provided in this manuscript.
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.
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: Christopher W DiGiovanni, MD, Chief of the Foot and Ankle Services, Department of Orthopaedic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, United States. cwdigiovanni@mgb.org
Received: July 19, 2024
Revised: October 21, 2024
Accepted: November 26, 2024
Published online: December 18, 2024
Processing time: 150 Days and 17.9 Hours
Core Tip

Core Tip: The study demonstrates the integration of a deep convolutional neural network (CNN) with smartphone cameras to diagnose pes planus and pes cavus, common foot deformities, with high accuracy. By utilizing a non-invasive and accessible screening tool, this method eliminates the need for traditional radiographic assessments, making it particularly beneficial for underserved communities. The CNN model showed high specificity and sensitivity, suggesting its potential for early detection and management of foot arch deformities, ultimately enhancing patient care and reducing healthcare costs.