An in-depth discussion of cholesteatoma, middle ear Inflammation, and langerhans cell histiocytosis of the temporal bone, based on diagnostic results
ObjectiveThis study aimed to conduct an in-depth investigation of the learning framework used for deriving diagnostic results of temporal bone diseases, including cholesteatoma and Langerhans cell histiocytosis (LCH). In addition, middle ear inflammation (MEI) was diagnosed by CT scanning of the tem...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Pediatrics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2022.809523/full |
| _version_ | 1811322709284487168 |
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| author | Bo Duan Li-Li Pan Wen-Xia Chen Zhong-Wei Qiao Zheng-Min Xu |
| author_facet | Bo Duan Li-Li Pan Wen-Xia Chen Zhong-Wei Qiao Zheng-Min Xu |
| author_sort | Bo Duan |
| collection | DOAJ |
| description | ObjectiveThis study aimed to conduct an in-depth investigation of the learning framework used for deriving diagnostic results of temporal bone diseases, including cholesteatoma and Langerhans cell histiocytosis (LCH). In addition, middle ear inflammation (MEI) was diagnosed by CT scanning of the temporal bone in pediatric patients.DesignA total of 119 patients were included in this retrospective study; among them, 40 patients had MEI, 38 patients had histology-proven cholesteatoma, and 41 patients had histology-proven LCH of the temporal bone. Each of the 119 patients was matched with one-third of the disease labels. The study included otologists and radiologists, and the reference criteria were histopathology results (70% of cases for training and 30% of cases for validation). A multilayer perceptron artificial neural network (VGG16_BN) was employed and classified, based on radiometrics. This framework structure was compared and analyzed by clinical experts according to CT images and performance.ResultsThe deep learning framework results vs. a physician’s diagnosis, respectively, in multiclassification tasks, were as follows. Receiver operating characteristic (ROC) (cholesteatoma): (0.98 vs. 0.91), LCH (0.99 vs. 0.98), and MEI (0.99 vs. 0.85). Accuracy (cholesteatoma): (0.99 vs. 0.89), LCH (0.99 vs. 0.97), and MEI (0.99 vs. 0.89). Sensitivity (cholesteatoma): (0.96 vs. 0.97), LCH (0.99 vs. 0.98), and MEI (1 vs. 0.69). Specificity (cholesteatoma): (1 vs. 0.89), LCH (0.99 vs. 0.97), and MEI (0.99 vs. 0.89).ConclusionThis article presents a research and learning framework for the diagnosis of cholesteatoma, MEI, and temporal bone LCH in children, based on CT scans. The research framework performed better than the clinical experts. |
| first_indexed | 2024-04-13T13:40:57Z |
| format | Article |
| id | doaj.art-1c547242d9c64a649f909810384d567a |
| institution | Directory Open Access Journal |
| issn | 2296-2360 |
| language | English |
| last_indexed | 2024-04-13T13:40:57Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj.art-1c547242d9c64a649f909810384d567a2022-12-22T02:44:38ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602022-08-011010.3389/fped.2022.809523809523An in-depth discussion of cholesteatoma, middle ear Inflammation, and langerhans cell histiocytosis of the temporal bone, based on diagnostic resultsBo Duan0Li-Li Pan1Wen-Xia Chen2Zhong-Wei Qiao3Zheng-Min Xu4Department of Otolaryngology-Head and Neck Surgery, Children’s Hospital of Fudan University, Shanghai, ChinaDepartment of Radiology, Children’s Hospital of Fudan University, Shanghai, ChinaDepartment of Otolaryngology-Head and Neck Surgery, Children’s Hospital of Fudan University, Shanghai, ChinaDepartment of Radiology, Children’s Hospital of Fudan University, Shanghai, ChinaDepartment of Otolaryngology-Head and Neck Surgery, Children’s Hospital of Fudan University, Shanghai, ChinaObjectiveThis study aimed to conduct an in-depth investigation of the learning framework used for deriving diagnostic results of temporal bone diseases, including cholesteatoma and Langerhans cell histiocytosis (LCH). In addition, middle ear inflammation (MEI) was diagnosed by CT scanning of the temporal bone in pediatric patients.DesignA total of 119 patients were included in this retrospective study; among them, 40 patients had MEI, 38 patients had histology-proven cholesteatoma, and 41 patients had histology-proven LCH of the temporal bone. Each of the 119 patients was matched with one-third of the disease labels. The study included otologists and radiologists, and the reference criteria were histopathology results (70% of cases for training and 30% of cases for validation). A multilayer perceptron artificial neural network (VGG16_BN) was employed and classified, based on radiometrics. This framework structure was compared and analyzed by clinical experts according to CT images and performance.ResultsThe deep learning framework results vs. a physician’s diagnosis, respectively, in multiclassification tasks, were as follows. Receiver operating characteristic (ROC) (cholesteatoma): (0.98 vs. 0.91), LCH (0.99 vs. 0.98), and MEI (0.99 vs. 0.85). Accuracy (cholesteatoma): (0.99 vs. 0.89), LCH (0.99 vs. 0.97), and MEI (0.99 vs. 0.89). Sensitivity (cholesteatoma): (0.96 vs. 0.97), LCH (0.99 vs. 0.98), and MEI (1 vs. 0.69). Specificity (cholesteatoma): (1 vs. 0.89), LCH (0.99 vs. 0.97), and MEI (0.99 vs. 0.89).ConclusionThis article presents a research and learning framework for the diagnosis of cholesteatoma, MEI, and temporal bone LCH in children, based on CT scans. The research framework performed better than the clinical experts.https://www.frontiersin.org/articles/10.3389/fped.2022.809523/fullPyTorchcholesteatomalangerhans cell histiocytosiscomputed tomography (CT) scandeep learning |
| spellingShingle | Bo Duan Li-Li Pan Wen-Xia Chen Zhong-Wei Qiao Zheng-Min Xu An in-depth discussion of cholesteatoma, middle ear Inflammation, and langerhans cell histiocytosis of the temporal bone, based on diagnostic results Frontiers in Pediatrics PyTorch cholesteatoma langerhans cell histiocytosis computed tomography (CT) scan deep learning |
| title | An in-depth discussion of cholesteatoma, middle ear Inflammation, and langerhans cell histiocytosis of the temporal bone, based on diagnostic results |
| title_full | An in-depth discussion of cholesteatoma, middle ear Inflammation, and langerhans cell histiocytosis of the temporal bone, based on diagnostic results |
| title_fullStr | An in-depth discussion of cholesteatoma, middle ear Inflammation, and langerhans cell histiocytosis of the temporal bone, based on diagnostic results |
| title_full_unstemmed | An in-depth discussion of cholesteatoma, middle ear Inflammation, and langerhans cell histiocytosis of the temporal bone, based on diagnostic results |
| title_short | An in-depth discussion of cholesteatoma, middle ear Inflammation, and langerhans cell histiocytosis of the temporal bone, based on diagnostic results |
| title_sort | in depth discussion of cholesteatoma middle ear inflammation and langerhans cell histiocytosis of the temporal bone based on diagnostic results |
| topic | PyTorch cholesteatoma langerhans cell histiocytosis computed tomography (CT) scan deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fped.2022.809523/full |
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