Accurate detection and grading of pterygium through smartphone by a fusion training model
© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ..
BACKGROUND/AIMS: To improve the accuracy of pterygium screening and detection through smartphones, we established a fusion training model by blending a large number of slit-lamp image data with a small proportion of smartphone data.
METHOD: Two datasets were used, a slit-lamp image dataset containing 20 987 images and a smartphone-based image dataset containing 1094 images. The RFRC (Faster RCNN based on ResNet101) model for the detection model. The SRU-Net (U-Net based on SE-ResNeXt50) for the segmentation models. The open-cv algorithm measured the width, length and area of pterygium in the cornea.
RESULTS: The detection model (trained by slit-lamp images) obtained the mean accuracy of 95.24%. The fusion segmentation model (trained by smartphone and slit-lamp images) achieved a microaverage F1 score of 0.8981, sensitivity of 0.8709, specificity of 0.9668 and area under the curve (AUC) of 0.9295. Compared with the same group of patients' smartphone and slit-lamp images, the fusion model performance in smartphone-based images (F1 score of 0.9313, sensitivity of 0.9360, specificity of 0.9613, AUC of 0.9426, accuracy of 92.38%) is close to the model (trained by slit-lamp images) in slit-lamp images (F1 score of 0.9448, sensitivity of 0.9165, specificity of 0.9689, AUC of 0.9569 and accuracy of 94.29%).
CONCLUSION: Our fusion model method got high pterygium detection and grading accuracy in insufficient smartphone data, and its performance is comparable to experienced ophthalmologists and works well in different smartphone brands.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:108 |
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| Enthalten in: |
The British journal of ophthalmology - 108(2024), 3 vom: 21. Feb., Seite 336-342 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Yuwen [VerfasserIn] |
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| Links: |
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| Themen: |
Conjunctiva |
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| Anmerkungen: |
Date Completed 23.02.2024 Date Revised 06.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1136/bjo-2022-322552 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM353647853 |
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| 245 | 1 | 0 | |a Accurate detection and grading of pterygium through smartphone by a fusion training model |
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| 500 | |a Date Revised 06.03.2024 | ||
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| 520 | |a © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. | ||
| 520 | |a BACKGROUND/AIMS: To improve the accuracy of pterygium screening and detection through smartphones, we established a fusion training model by blending a large number of slit-lamp image data with a small proportion of smartphone data | ||
| 520 | |a METHOD: Two datasets were used, a slit-lamp image dataset containing 20 987 images and a smartphone-based image dataset containing 1094 images. The RFRC (Faster RCNN based on ResNet101) model for the detection model. The SRU-Net (U-Net based on SE-ResNeXt50) for the segmentation models. The open-cv algorithm measured the width, length and area of pterygium in the cornea | ||
| 520 | |a RESULTS: The detection model (trained by slit-lamp images) obtained the mean accuracy of 95.24%. The fusion segmentation model (trained by smartphone and slit-lamp images) achieved a microaverage F1 score of 0.8981, sensitivity of 0.8709, specificity of 0.9668 and area under the curve (AUC) of 0.9295. Compared with the same group of patients' smartphone and slit-lamp images, the fusion model performance in smartphone-based images (F1 score of 0.9313, sensitivity of 0.9360, specificity of 0.9613, AUC of 0.9426, accuracy of 92.38%) is close to the model (trained by slit-lamp images) in slit-lamp images (F1 score of 0.9448, sensitivity of 0.9165, specificity of 0.9689, AUC of 0.9569 and accuracy of 94.29%) | ||
| 520 | |a CONCLUSION: Our fusion model method got high pterygium detection and grading accuracy in insufficient smartphone data, and its performance is comparable to experienced ophthalmologists and works well in different smartphone brands | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a conjunctiva | |
| 650 | 4 | |a imaging | |
| 650 | 4 | |a ocular surface | |
| 650 | 4 | |a telemedicine | |
| 700 | 1 | |a Xu, Changsheng |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Shaopan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yuguang |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Xiang |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Shujia |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Zhaolin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yuqian |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Houjian |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Yuli |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Caihong |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Huping |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Qian |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Jiaoyue |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Zhiming |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Zuguo |e verfasserin |4 aut | |
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