Evidence map›Paper›PMID 39744730›Full record

ArticleWorld journal of orthopedics2024

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

Abstract read
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Article in World journal of orthopedics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Samir GhandourDepartment of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States.
Anton LebedevDepartment of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States.
Wei-Shao TungDepartment of Orthopaedics, Massachusetts General Hospital, Boston, MA 02114, United States.
Konstantin SemianovDepartment of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States.
Artem SemjanowDepartment of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States.
Christopher W DiGiovanniDepartment of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States. cwdigiovanni@mgb.org.
Soheil Ashkani-EsfahaniDepartment of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States.
Lorena Bejarano PinedaDepartment of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPes 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.

aimTo develop deep learning algorithms that detect and classify such deformities using smartphone cameras.

methodsAn 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.

resultsThe 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 (

conclusionThis 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.

Indexed as

Artificial intelligenceComputer visionFlatfootHigh-archLower extremityThree-dimensional scanning

Identifiers

PMID39744730
PMCPMC11686530

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LicenceCC BY-NC
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.