Fully automated grading system for the evaluation of punctate epithelial erosions using deep neural networks
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ..
PURPOSE: The goal was to develop a fully automated grading system for the evaluation of punctate epithelial erosions (PEEs) using deep neural networks.
METHODS: A fully automated system was developed to detect corneal position and grade staining severity given a corneal fluorescein staining image. The fully automated pipeline consists of the following three steps: a corneal segmentation model extracts corneal area; five image patches are cropped from the staining image based on the five subregions of extracted cornea; a staining grading model predicts a score for each image patch from 0 to 3, and automated grading score for the whole cornea is obtained from 0 to 15. Finally, the clinical grading scores annotated by three ophthalmologists were compared with automated grading scores.
RESULTS: For corneal segmentation, the segmentation model achieved an intersection over union of 0.937. For punctate staining grading, the grading model achieved a classification accuracy of 76.5% and an area under the receiver operating characteristic curve of 0.940 (95% CI 0.932 to 0.949). For the fully automated pipeline, Pearson's correlation coefficient between the clinical and automated grading scores was 0.908 (p<0.01). Bland-Altman analysis revealed 95% limits of agreement between the clinical and automated grading scores of between -4.125 and 3.720 (concordance correlation coefficient=0.904). The average time required for processing a single stained image during pipeline was 0.58 s.
CONCLUSION: A fully automated grading system was developed to evaluate PEEs. The grading results may serve as a reference for ophthalmologists in clinical trials and residency training procedures.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:107 |
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| Enthalten in: |
The British journal of ophthalmology - 107(2023), 4 vom: 20. Apr., Seite 453-460 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Qu, Jing-Hao [VerfasserIn] |
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| Links: |
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| Themen: |
Cornea |
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| Anmerkungen: |
Date Completed 24.03.2023 Date Revised 13.04.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1136/bjophthalmol-2021-319755 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM332109674 |
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| 520 | |a © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. | ||
| 520 | |a PURPOSE: The goal was to develop a fully automated grading system for the evaluation of punctate epithelial erosions (PEEs) using deep neural networks | ||
| 520 | |a METHODS: A fully automated system was developed to detect corneal position and grade staining severity given a corneal fluorescein staining image. The fully automated pipeline consists of the following three steps: a corneal segmentation model extracts corneal area; five image patches are cropped from the staining image based on the five subregions of extracted cornea; a staining grading model predicts a score for each image patch from 0 to 3, and automated grading score for the whole cornea is obtained from 0 to 15. Finally, the clinical grading scores annotated by three ophthalmologists were compared with automated grading scores | ||
| 520 | |a RESULTS: For corneal segmentation, the segmentation model achieved an intersection over union of 0.937. For punctate staining grading, the grading model achieved a classification accuracy of 76.5% and an area under the receiver operating characteristic curve of 0.940 (95% CI 0.932 to 0.949). For the fully automated pipeline, Pearson's correlation coefficient between the clinical and automated grading scores was 0.908 (p<0.01). Bland-Altman analysis revealed 95% limits of agreement between the clinical and automated grading scores of between -4.125 and 3.720 (concordance correlation coefficient=0.904). The average time required for processing a single stained image during pipeline was 0.58 s | ||
| 520 | |a CONCLUSION: A fully automated grading system was developed to evaluate PEEs. The grading results may serve as a reference for ophthalmologists in clinical trials and residency training procedures | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
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| 700 | 1 | |a Qin, Xiao-Ran |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Chen-Di |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Rong-Mei |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, Ge-Ge |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Gu, Shao-Feng |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Hai-Kun |e verfasserin |4 aut | |
| 700 | 1 | |a Hong, Jing |e verfasserin |4 aut | |
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