Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks
BackgroundMicrotia is a congenital abnormality varying from slightly structural abnormalities to the complete absence of the external ear. However, there is no gold standard for assessing the severity of microtia.ObjectivesThe purpose of this study was to develop and test models of artificial intell...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Surgery |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fsurg.2022.929110/full |
| _version_ | 1798002138203291648 |
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| author | Dawei Wang Xue Chen Yiping Wu Hongbo Tang Pei Deng |
| author_facet | Dawei Wang Xue Chen Yiping Wu Hongbo Tang Pei Deng |
| author_sort | Dawei Wang |
| collection | DOAJ |
| description | BackgroundMicrotia is a congenital abnormality varying from slightly structural abnormalities to the complete absence of the external ear. However, there is no gold standard for assessing the severity of microtia.ObjectivesThe purpose of this study was to develop and test models of artificial intelligence to assess the severity of microtia using clinical photographs.MethodsA total of 800 ear images were included, and randomly divided into training, validation, and test set. Nine convolutional neural networks (CNNs) were trained for classifying the severity of microtia. The evaluation metrics, including accuracy, precision, recall, F1 score, receiver operating characteristic curve, and area under the curve (AUC) values, were used to evaluate the performance of the models.ResultsEight CNNs were tested with accuracy greater than 0.8. Among them, Alexnet and Mobilenet achieved the highest accuracy of 0.9. Except for Mnasnet, all CNNs achieved high AUC values higher than 0.9 for each grade of microtia. In most CNNs, the grade I microtia had the lowest AUC values and the normal ear had the highest AUC values.ConclusionCNN can classify the severity of microtia with high accuracy. Artificial intelligence is expected to provide an objective, automated assessment of the severity of microtia. |
| first_indexed | 2024-04-11T11:47:23Z |
| format | Article |
| id | doaj.art-30a0909f194e457781cf0cfe694b1acf |
| institution | Directory Open Access Journal |
| issn | 2296-875X |
| language | English |
| last_indexed | 2024-04-11T11:47:23Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Surgery |
| spelling | doaj.art-30a0909f194e457781cf0cfe694b1acf2022-12-22T04:25:30ZengFrontiers Media S.A.Frontiers in Surgery2296-875X2022-09-01910.3389/fsurg.2022.929110929110Artificial intelligence for assessing the severity of microtia via deep convolutional neural networksDawei WangXue ChenYiping WuHongbo TangPei DengBackgroundMicrotia is a congenital abnormality varying from slightly structural abnormalities to the complete absence of the external ear. However, there is no gold standard for assessing the severity of microtia.ObjectivesThe purpose of this study was to develop and test models of artificial intelligence to assess the severity of microtia using clinical photographs.MethodsA total of 800 ear images were included, and randomly divided into training, validation, and test set. Nine convolutional neural networks (CNNs) were trained for classifying the severity of microtia. The evaluation metrics, including accuracy, precision, recall, F1 score, receiver operating characteristic curve, and area under the curve (AUC) values, were used to evaluate the performance of the models.ResultsEight CNNs were tested with accuracy greater than 0.8. Among them, Alexnet and Mobilenet achieved the highest accuracy of 0.9. Except for Mnasnet, all CNNs achieved high AUC values higher than 0.9 for each grade of microtia. In most CNNs, the grade I microtia had the lowest AUC values and the normal ear had the highest AUC values.ConclusionCNN can classify the severity of microtia with high accuracy. Artificial intelligence is expected to provide an objective, automated assessment of the severity of microtia.https://www.frontiersin.org/articles/10.3389/fsurg.2022.929110/fullartificial intelligencemicrotiaseverityconvolutional neural networksobjective |
| spellingShingle | Dawei Wang Xue Chen Yiping Wu Hongbo Tang Pei Deng Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks Frontiers in Surgery artificial intelligence microtia severity convolutional neural networks objective |
| title | Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks |
| title_full | Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks |
| title_fullStr | Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks |
| title_full_unstemmed | Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks |
| title_short | Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks |
| title_sort | artificial intelligence for assessing the severity of microtia via deep convolutional neural networks |
| topic | artificial intelligence microtia severity convolutional neural networks objective |
| url | https://www.frontiersin.org/articles/10.3389/fsurg.2022.929110/full |
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