Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images
Background and aimA pterygium is a common ocular surface disease, which not only affects facial appearance but can also grow into the tissue layer, causing astigmatism and vision loss. In this study, an artificial intelligence model was developed for detecting the pterygium that requires surgical tr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.1084118/full |
| _version_ | 1798004213535473664 |
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| author | Fan Gan Fan Gan Wan-Yun Chen Hui Liu Yu-Lin Zhong |
| author_facet | Fan Gan Fan Gan Wan-Yun Chen Hui Liu Yu-Lin Zhong |
| author_sort | Fan Gan |
| collection | DOAJ |
| description | Background and aimA pterygium is a common ocular surface disease, which not only affects facial appearance but can also grow into the tissue layer, causing astigmatism and vision loss. In this study, an artificial intelligence model was developed for detecting the pterygium that requires surgical treatment. The model was designed using ensemble deep learning (DL).MethodsA total of 172 anterior segment images of pterygia were obtained from the Jiangxi Provincial People’s Hospital (China) between 2017 and 2022. They were divided by a senior ophthalmologist into the non-surgery group and the surgery group. An artificial intelligence model was then developed based on ensemble DL, which was integrated with four benchmark models: the Resnet18, Alexnet, Googlenet, and Vgg11 model, for detecting the pterygium that requires surgical treatment, and Grad-CAM was used to visualize the DL process. Finally, the performance of the ensemble DL model was compared with the classical Resnet18 model, Alexnet model, Googlenet model, and Vgg11 model.ResultsThe accuracy and area under the curve (AUC) of the ensemble DL model was higher than all of the other models. In the training set, the accuracy and AUC of the ensemble model was 94.20% and 0.978, respectively. In the testing set, the accuracy and AUC of the ensemble model was 94.12% and 0.980, respectively.ConclusionThis study indicates that this ensemble DL model, coupled with the anterior segment images in our study, might be an automated and cost-saving alternative for detection of the pterygia that require surgery. |
| first_indexed | 2024-04-11T12:20:04Z |
| format | Article |
| id | doaj.art-1ded9a580bb643afa36038661bede49b |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-11T12:20:04Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-1ded9a580bb643afa36038661bede49b2022-12-22T04:24:06ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-12-011610.3389/fnins.2022.10841181084118Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment imagesFan Gan0Fan Gan1Wan-Yun Chen2Hui Liu3Yu-Lin Zhong4Medical College of Nanchang University, Nanchang, ChinaDepartment of Ophthalmology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, ChinaDepartment of Ophthalmology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, ChinaDepartment of Ophthalmology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, ChinaDepartment of Ophthalmology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, ChinaBackground and aimA pterygium is a common ocular surface disease, which not only affects facial appearance but can also grow into the tissue layer, causing astigmatism and vision loss. In this study, an artificial intelligence model was developed for detecting the pterygium that requires surgical treatment. The model was designed using ensemble deep learning (DL).MethodsA total of 172 anterior segment images of pterygia were obtained from the Jiangxi Provincial People’s Hospital (China) between 2017 and 2022. They were divided by a senior ophthalmologist into the non-surgery group and the surgery group. An artificial intelligence model was then developed based on ensemble DL, which was integrated with four benchmark models: the Resnet18, Alexnet, Googlenet, and Vgg11 model, for detecting the pterygium that requires surgical treatment, and Grad-CAM was used to visualize the DL process. Finally, the performance of the ensemble DL model was compared with the classical Resnet18 model, Alexnet model, Googlenet model, and Vgg11 model.ResultsThe accuracy and area under the curve (AUC) of the ensemble DL model was higher than all of the other models. In the training set, the accuracy and AUC of the ensemble model was 94.20% and 0.978, respectively. In the testing set, the accuracy and AUC of the ensemble model was 94.12% and 0.980, respectively.ConclusionThis study indicates that this ensemble DL model, coupled with the anterior segment images in our study, might be an automated and cost-saving alternative for detection of the pterygia that require surgery.https://www.frontiersin.org/articles/10.3389/fnins.2022.1084118/fullanterior segment imagesartificial intelligencedeep learningpterygiumsurgery |
| spellingShingle | Fan Gan Fan Gan Wan-Yun Chen Hui Liu Yu-Lin Zhong Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images Frontiers in Neuroscience anterior segment images artificial intelligence deep learning pterygium surgery |
| title | Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images |
| title_full | Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images |
| title_fullStr | Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images |
| title_full_unstemmed | Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images |
| title_short | Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images |
| title_sort | application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images |
| topic | anterior segment images artificial intelligence deep learning pterygium surgery |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.1084118/full |
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