Automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning

Abstract Craniofacial anomaly including deformational plagiocephaly as a result of deformities in head and facial bones evolution is a serious health problem in newbies. The impact of such condition on the affected infants is profound from both medical and social viewpoint. Indeed, timely diagnosing...

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Autores principales:	Seyed Amir Hossein Tabatabaei, Patrick Fischer, Sonja Wattendorf, Fatemeh Sabouripour, Hans-Peter Howaldt, Martina Wilbrand, Jan-Falco Wilbrand, Keywan Sohrabi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6faa086a9f9b43649c7558303afbfeac
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spelling	oai:doaj.org-article:6faa086a9f9b43649c7558303afbfeac2021-12-02T19:12:27ZAutomatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning10.1038/s41598-021-96821-72045-2322https://doaj.org/article/6faa086a9f9b43649c7558303afbfeac2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96821-7https://doaj.org/toc/2045-2322Abstract Craniofacial anomaly including deformational plagiocephaly as a result of deformities in head and facial bones evolution is a serious health problem in newbies. The impact of such condition on the affected infants is profound from both medical and social viewpoint. Indeed, timely diagnosing through different medical examinations like anthropometric measurements of the skull or even Computer Tomography (CT) image modality followed by a periodical screening and monitoring plays a vital role in treatment phase. In this paper, a classification model for detecting and monitoring deformational plagiocephaly in affected infants is presented. The presented model is based on a deep learning network architecture. The given model achieves high accuracy of 99.01% with other classification parameters. The input to the model are the images captured by commonly used smartphone cameras which waives the requirement to sophisticated medical imaging modalities. The method is deployed into a mobile application which enables the parents/caregivers and non-clinical experts to monitor and report the treatment progress at home.Seyed Amir Hossein TabatabaeiPatrick FischerSonja WattendorfFatemeh SabouripourHans-Peter HowaldtMartina WilbrandJan-Falco WilbrandKeywan SohrabiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Seyed Amir Hossein Tabatabaei Patrick Fischer Sonja Wattendorf Fatemeh Sabouripour Hans-Peter Howaldt Martina Wilbrand Jan-Falco Wilbrand Keywan Sohrabi Automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning
description	Abstract Craniofacial anomaly including deformational plagiocephaly as a result of deformities in head and facial bones evolution is a serious health problem in newbies. The impact of such condition on the affected infants is profound from both medical and social viewpoint. Indeed, timely diagnosing through different medical examinations like anthropometric measurements of the skull or even Computer Tomography (CT) image modality followed by a periodical screening and monitoring plays a vital role in treatment phase. In this paper, a classification model for detecting and monitoring deformational plagiocephaly in affected infants is presented. The presented model is based on a deep learning network architecture. The given model achieves high accuracy of 99.01% with other classification parameters. The input to the model are the images captured by commonly used smartphone cameras which waives the requirement to sophisticated medical imaging modalities. The method is deployed into a mobile application which enables the parents/caregivers and non-clinical experts to monitor and report the treatment progress at home.
format	article
author	Seyed Amir Hossein Tabatabaei Patrick Fischer Sonja Wattendorf Fatemeh Sabouripour Hans-Peter Howaldt Martina Wilbrand Jan-Falco Wilbrand Keywan Sohrabi
author_facet	Seyed Amir Hossein Tabatabaei Patrick Fischer Sonja Wattendorf Fatemeh Sabouripour Hans-Peter Howaldt Martina Wilbrand Jan-Falco Wilbrand Keywan Sohrabi
author_sort	Seyed Amir Hossein Tabatabaei
title	Automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning
title_short	Automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning
title_full	Automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning
title_fullStr	Automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning
title_full_unstemmed	Automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning
title_sort	automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6faa086a9f9b43649c7558303afbfeac
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Automatic detection and monitoring of abnormal skull shape in children with deformational plagiocephaly using deep learning

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