Assessing outcomes of ear molding therapy by health care providers and convolutional neural network

Abstract Ear molding therapy is a nonsurgical technique to correct certain congenital auricular deformities. While the advantages of nonsurgical treatments over otoplasty are well-described, few studies have assessed aesthetic outcomes. In this study, we compared assessments of outcomes of ear moldi...

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Autores principales:	Rami R. Hallac, Sarah A. Jackson, Jessica Grant, Kaylyn Fisher, Sarah Scheiwe, Elizabeth Wetz, Jeyna Perez, Jeon Lee, Krishna Chitta, James R. Seaward, Alex A. Kane
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4e4b79fe73e94bf381e75966ce7c9bff
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spelling	oai:doaj.org-article:4e4b79fe73e94bf381e75966ce7c9bff2021-12-02T14:58:48ZAssessing outcomes of ear molding therapy by health care providers and convolutional neural network10.1038/s41598-021-97310-72045-2322https://doaj.org/article/4e4b79fe73e94bf381e75966ce7c9bff2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97310-7https://doaj.org/toc/2045-2322Abstract Ear molding therapy is a nonsurgical technique to correct certain congenital auricular deformities. While the advantages of nonsurgical treatments over otoplasty are well-described, few studies have assessed aesthetic outcomes. In this study, we compared assessments of outcomes of ear molding therapy for 283 ears by experienced healthcare providers and a previously developed deep learning CNN model. 2D photographs of ears were obtained as a standard of care in our onsite photography studio. Physician assistants (PAs) rated the photographs using a 5-point Likert scale ranging from 1(poor) to 5(excellent) and the CNN assessment was categorical, classifying each photo as either “normal” or “deformed”. On average, the PAs classified 75.6% of photographs as good to excellent outcomes (scores 4 and 5). Similarly, the CNN classified 75.3% of the photographs as normal. The inter-rater agreement between the PAs ranged between 72 and 81%, while there was a 69.6% agreement between the machine model and the inter-rater majority agreement between at least two PAs (i.e., when at least two PAs gave a simultaneous score < 4 or ≥ 4). This study shows that noninvasive ear molding therapy has excellent outcomes in general. In addition, it indicates that with further training and validation, machine learning techniques, like CNN, have the capability to accurately mimic provider assessment while removing the subjectivity of human evaluation making it a robust tool for ear deformity identification and outcome evaluation.Rami R. HallacSarah A. JacksonJessica GrantKaylyn FisherSarah ScheiweElizabeth WetzJeyna PerezJeon LeeKrishna ChittaJames R. SeawardAlex A. KaneNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rami R. Hallac Sarah A. Jackson Jessica Grant Kaylyn Fisher Sarah Scheiwe Elizabeth Wetz Jeyna Perez Jeon Lee Krishna Chitta James R. Seaward Alex A. Kane Assessing outcomes of ear molding therapy by health care providers and convolutional neural network
description	Abstract Ear molding therapy is a nonsurgical technique to correct certain congenital auricular deformities. While the advantages of nonsurgical treatments over otoplasty are well-described, few studies have assessed aesthetic outcomes. In this study, we compared assessments of outcomes of ear molding therapy for 283 ears by experienced healthcare providers and a previously developed deep learning CNN model. 2D photographs of ears were obtained as a standard of care in our onsite photography studio. Physician assistants (PAs) rated the photographs using a 5-point Likert scale ranging from 1(poor) to 5(excellent) and the CNN assessment was categorical, classifying each photo as either “normal” or “deformed”. On average, the PAs classified 75.6% of photographs as good to excellent outcomes (scores 4 and 5). Similarly, the CNN classified 75.3% of the photographs as normal. The inter-rater agreement between the PAs ranged between 72 and 81%, while there was a 69.6% agreement between the machine model and the inter-rater majority agreement between at least two PAs (i.e., when at least two PAs gave a simultaneous score < 4 or ≥ 4). This study shows that noninvasive ear molding therapy has excellent outcomes in general. In addition, it indicates that with further training and validation, machine learning techniques, like CNN, have the capability to accurately mimic provider assessment while removing the subjectivity of human evaluation making it a robust tool for ear deformity identification and outcome evaluation.
format	article
author	Rami R. Hallac Sarah A. Jackson Jessica Grant Kaylyn Fisher Sarah Scheiwe Elizabeth Wetz Jeyna Perez Jeon Lee Krishna Chitta James R. Seaward Alex A. Kane
author_facet	Rami R. Hallac Sarah A. Jackson Jessica Grant Kaylyn Fisher Sarah Scheiwe Elizabeth Wetz Jeyna Perez Jeon Lee Krishna Chitta James R. Seaward Alex A. Kane
author_sort	Rami R. Hallac
title	Assessing outcomes of ear molding therapy by health care providers and convolutional neural network
title_short	Assessing outcomes of ear molding therapy by health care providers and convolutional neural network
title_full	Assessing outcomes of ear molding therapy by health care providers and convolutional neural network
title_fullStr	Assessing outcomes of ear molding therapy by health care providers and convolutional neural network
title_full_unstemmed	Assessing outcomes of ear molding therapy by health care providers and convolutional neural network
title_sort	assessing outcomes of ear molding therapy by health care providers and convolutional neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4e4b79fe73e94bf381e75966ce7c9bff
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Assessing outcomes of ear molding therapy by health care providers and convolutional neural network

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