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Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach

Positional cranial deformities are relatively common conditions, characterized by asymmetry and changes in skull shape. Although three-dimensional (3D) scanning is the gold standard for diagnosing such deformities, it requires expensive laser scanners and skilled maneuvering. We therefore developed...

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Autores principales: Callejas Pastor, Cecilia A., Jung, Il-Young, Seo, Shinhye, Kwon, Soon Bin, Ku, Yunseo, Choi, Jayoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400331/
https://www.ncbi.nlm.nih.gov/pubmed/32707742
http://dx.doi.org/10.3390/diagnostics10070495
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author Callejas Pastor, Cecilia A.
Jung, Il-Young
Seo, Shinhye
Kwon, Soon Bin
Ku, Yunseo
Choi, Jayoung
author_facet Callejas Pastor, Cecilia A.
Jung, Il-Young
Seo, Shinhye
Kwon, Soon Bin
Ku, Yunseo
Choi, Jayoung
author_sort Callejas Pastor, Cecilia A.
collection PubMed
description Positional cranial deformities are relatively common conditions, characterized by asymmetry and changes in skull shape. Although three-dimensional (3D) scanning is the gold standard for diagnosing such deformities, it requires expensive laser scanners and skilled maneuvering. We therefore developed an inexpensive, fast, and convenient screening method to classify cranial deformities in infants, based on single two-dimensional vertex cranial images. In total, 174 measurements from 80 subjects were recorded. Our screening software performs image processing and machine learning-based estimation related to the deformity indices of the cranial ratio (CR) and cranial vault asymmetry index (CVAI) to determine the severity levels of brachycephaly and plagiocephaly. For performance evaluations, the estimated CR and CVAI values were compared to the reference data obtained using a 3D cranial scanner. The CR and CVAI correlation coefficients obtained via support vector regression were 0.85 and 0.89, respectively. When the trained model was evaluated using the unseen test data for the three CR and three CVAI classes, an 86.7% classification accuracy of the proposed method was obtained for both brachycephaly and plagiocephaly. The results showed that our method for screening cranial deformities in infants could aid clinical evaluations and parental monitoring of the progression of deformities at home.
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spelling pubmed-74003312020-08-23 Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach Callejas Pastor, Cecilia A. Jung, Il-Young Seo, Shinhye Kwon, Soon Bin Ku, Yunseo Choi, Jayoung Diagnostics (Basel) Article Positional cranial deformities are relatively common conditions, characterized by asymmetry and changes in skull shape. Although three-dimensional (3D) scanning is the gold standard for diagnosing such deformities, it requires expensive laser scanners and skilled maneuvering. We therefore developed an inexpensive, fast, and convenient screening method to classify cranial deformities in infants, based on single two-dimensional vertex cranial images. In total, 174 measurements from 80 subjects were recorded. Our screening software performs image processing and machine learning-based estimation related to the deformity indices of the cranial ratio (CR) and cranial vault asymmetry index (CVAI) to determine the severity levels of brachycephaly and plagiocephaly. For performance evaluations, the estimated CR and CVAI values were compared to the reference data obtained using a 3D cranial scanner. The CR and CVAI correlation coefficients obtained via support vector regression were 0.85 and 0.89, respectively. When the trained model was evaluated using the unseen test data for the three CR and three CVAI classes, an 86.7% classification accuracy of the proposed method was obtained for both brachycephaly and plagiocephaly. The results showed that our method for screening cranial deformities in infants could aid clinical evaluations and parental monitoring of the progression of deformities at home. MDPI 2020-07-19 /pmc/articles/PMC7400331/ /pubmed/32707742 http://dx.doi.org/10.3390/diagnostics10070495 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Callejas Pastor, Cecilia A.
Jung, Il-Young
Seo, Shinhye
Kwon, Soon Bin
Ku, Yunseo
Choi, Jayoung
Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach
title Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach
title_full Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach
title_fullStr Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach
title_full_unstemmed Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach
title_short Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach
title_sort two-dimensional image-based screening tool for infants with positional cranial deformities: a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400331/
https://www.ncbi.nlm.nih.gov/pubmed/32707742
http://dx.doi.org/10.3390/diagnostics10070495
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