Ghandour S, Lebedev A, Tung WS, Semianov K, Semjanow A, DiGiovanni CW, Ashkani-Esfahani S, Pineda LB. Utilization of artificial intelligence in the diagnosis of pes planus and pes cavus with a smartphone camera. World J Orthop 2024; 15(12): 1146-1154 [DOI: 10.5312/wjo.v15.i12.1146]
Corresponding Author of This Article
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
Research Domain of This Article
Computer Science, Artificial Intelligence
Article-Type of This Article
Case Control 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. 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
Abstract
BACKGROUND
Pes planus (flatfoot) and pes cavus (high arch foot) are common foot deformities, often requiring clinical and radiographic assessment for diagnosis and potential subsequent management. Traditional diagnostic methods, while effective, pose limitations such as cost, radiation exposure, and accessibility, particularly in underserved areas.
AIM
To develop deep learning algorithms that detect and classify such deformities using smartphone cameras.
METHODS
An algorithm that integrated a deep convolutional neural network (CNN) into a smartphone camera was utilized to detect pes planus and pes cavus deformities. This case control study was conducted at a tertiary hospital with participants recruited from two orthopaedic foot and ankle clinics. The CNN was trained and tested using photographs of the medial aspect of participants’ feet, taken under standardized conditions. Participants included subjects with standard foot alignment, pes planus, or pes cavus determined by an expert clinician using the foot posture index. The model’s performance was assessed in comparison to clinical assessment and radiographic measurements, specifically lateral tarsal-first metatarsal angle and calcaneal inclination angle.
RESULTS
The CNN model demonstrated high accuracy in diagnosing both pes planus and pes cavus, with an optimized area under the curve of 0.90 for pes planus and 0.90 for pes cavus. It showed a specificity and sensitivity of 84% and 87% for pes planus detection, respectively; and 97% and 70% for pes cavus, respectively. The model’s prediction correlated moderately with radiographic lateral Meary’s angle measurements, indicating the model’s excellent reliability in assessing food arch deformity (P < 0.05).
CONCLUSION
This study highlights the potential of using a smartphone-based CNN model as a screening tool that is reliable and accessible for the detection of pes planus and pes cavus deformities, which is especially beneficial for underserved communities and patients with pain generated by subtle foot arch deformities.
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.
