A Deep Learning-Based Framework for Accurate Evaluation of Corneal Treatment Zone After Orthokeratology
Purpose: Given the robust effectiveness of inhibiting myopia progression, orthokeratology has gained increasing popularity worldwide. However, identifying the boundary and the center of reshaped corneal area (i.e., treatment zone) is the main challenging task in evaluating the performance of orthokeratology. Here we present automated deep learning algorithms to solve the challenges.
Methods: A total of 6328 corneal topographical maps, including 2996 axial subtractive maps and 3332 tangential subtractive maps, were collected from 2044 myopic patients who received orthokeratology. The boundary and the center of the treatment zones were annotated by experts as ground truths using axial subtractive maps and tangential subtractive maps, respectively. The algorithms based on neural network structures of fully convolutional networks (FCNs) and convolutional neural networks (CNNs) were developed to automatically identify the boundary and the center of the treatment zone, respectively.
Results: The algorithm of FCNs identified the treatment zone boundaries with an accuracy intersection over union (IoU) of 0.90 ± 0.06 (mean ± SD; range, 0.60-0.97). The algorithm of CNNs also identified the treatment zone centers with an average deviation of 0.22 ± 0.22 mm (range, 0.01-1.66 mm).
Conclusions: These results show that a deep learning-based solution is able to provide an automatic and accurate tool to accomplish the two main challenges of orthokeratology.
Translational Relevance: Deep learning in orthokeratology can shorten the time while maintaining accurate results in clinical practice, which enables clinicians to help more patients daily.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Translational vision science & technology - 10(2021), 14 vom: 01. Dez., Seite 21 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tang, Yong [VerfasserIn] |
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| Anmerkungen: |
Date Completed 26.01.2022 Date Revised 26.01.2022 published: Print Citation Status MEDLINE |
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| doi: |
10.1167/tvst.10.14.21 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM33468126X |
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| 500 | |a Date Revised 26.01.2022 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Purpose: Given the robust effectiveness of inhibiting myopia progression, orthokeratology has gained increasing popularity worldwide. However, identifying the boundary and the center of reshaped corneal area (i.e., treatment zone) is the main challenging task in evaluating the performance of orthokeratology. Here we present automated deep learning algorithms to solve the challenges | ||
| 520 | |a Methods: A total of 6328 corneal topographical maps, including 2996 axial subtractive maps and 3332 tangential subtractive maps, were collected from 2044 myopic patients who received orthokeratology. The boundary and the center of the treatment zones were annotated by experts as ground truths using axial subtractive maps and tangential subtractive maps, respectively. The algorithms based on neural network structures of fully convolutional networks (FCNs) and convolutional neural networks (CNNs) were developed to automatically identify the boundary and the center of the treatment zone, respectively | ||
| 520 | |a Results: The algorithm of FCNs identified the treatment zone boundaries with an accuracy intersection over union (IoU) of 0.90 ± 0.06 (mean ± SD; range, 0.60-0.97). The algorithm of CNNs also identified the treatment zone centers with an average deviation of 0.22 ± 0.22 mm (range, 0.01-1.66 mm) | ||
| 520 | |a Conclusions: These results show that a deep learning-based solution is able to provide an automatic and accurate tool to accomplish the two main challenges of orthokeratology | ||
| 520 | |a Translational Relevance: Deep learning in orthokeratology can shorten the time while maintaining accurate results in clinical practice, which enables clinicians to help more patients daily | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Chen, Zhao |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Weijia |e verfasserin |4 aut | |
| 700 | 1 | |a Wen, Longbo |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Linjing |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Mao |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Fan |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, He |e verfasserin |4 aut | |
| 700 | 1 | |a Lan, Weizhong |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Zhikuan |e verfasserin |4 aut | |
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