Algorithm for Detection and Quantification of Hyperreflective Dots on Optical Coherence Tomography in Diabetic Macular Edema
Purpose: To develop an algorithm to detect and quantify hyperreflective dots (HRDs) on optical coherence tomography (OCT) in patients with diabetic macular edema (DME).Materials and Methods: Twenty OCTs (each OCT contains 128 b scans) from 20 patients diagnosed with DME were included in this study....
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Frontiers Media S.A.
2021-08-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2021.688986/full |
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| author | Haifan Huang Haifan Huang Liangjiu Zhu Weifang Zhu Tian Lin Leonoor Inge Los Chenpu Yao Xinjian Chen Haoyu Chen |
| author_facet | Haifan Huang Haifan Huang Liangjiu Zhu Weifang Zhu Tian Lin Leonoor Inge Los Chenpu Yao Xinjian Chen Haoyu Chen |
| author_sort | Haifan Huang |
| collection | DOAJ |
| description | Purpose: To develop an algorithm to detect and quantify hyperreflective dots (HRDs) on optical coherence tomography (OCT) in patients with diabetic macular edema (DME).Materials and Methods: Twenty OCTs (each OCT contains 128 b scans) from 20 patients diagnosed with DME were included in this study. Two types of HRDs, hard exudates and small HRDs (hypothesized to be activated microglia), were identified and labeled independently by two raters. An algorithm using deep learning technology was developed based on input (in total 2,560 OCT b scans) of manual labeling and differentiation of HRDs from rater 1. 4-fold cross-validation was used to train and validate the algorithm. Dice coefficient, intraclass coefficient (ICC), correlation coefficient, and Bland–Altman plot were used to evaluate agreement of the output parameters between two methods (either between two raters or between one rater and proposed algorithm).Results: The Dice coefficients of total HRDs, hard exudates, and small HRDs area of the algorithm were 0.70 ± 0.10, 0.72 ± 0.11, and 0.46 ± 0.06, respectively. The correlations between rater 1 and proposed algorithm (range: 0.95–0.99, all p < 0.001) were stronger than the correlations between the two raters (range: 0.84–0.96, all p < 0.001) for all parameters. The ICCs were higher for all the parameters between rater 1 and proposed algorithm (range: 0.972–0.997) than those between the two raters (range: 0.860–0.953).Conclusions: Our proposed algorithm is a good tool to detect and quantify HRDs and can provide objective and repeatable information of OCT for DME patients in clinical practice and studies. |
| first_indexed | 2024-12-19T15:45:30Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-12-19T15:45:30Z |
| publishDate | 2021-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-7084163ede6d472db8338e4029f4950c2022-12-21T20:15:21ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2021-08-01810.3389/fmed.2021.688986688986Algorithm for Detection and Quantification of Hyperreflective Dots on Optical Coherence Tomography in Diabetic Macular EdemaHaifan Huang0Haifan Huang1Liangjiu Zhu2Weifang Zhu3Tian Lin4Leonoor Inge Los5Chenpu Yao6Xinjian Chen7Haoyu Chen8Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, ChinaDepartment of Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, NetherlandsSchool of Electronics and Information Engineering, Soochow University, Suzhou, ChinaSchool of Electronics and Information Engineering, Soochow University, Suzhou, ChinaJoint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, ChinaDepartment of Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, NetherlandsSchool of Electronics and Information Engineering, Soochow University, Suzhou, ChinaSchool of Electronics and Information Engineering, Soochow University, Suzhou, ChinaJoint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, ChinaPurpose: To develop an algorithm to detect and quantify hyperreflective dots (HRDs) on optical coherence tomography (OCT) in patients with diabetic macular edema (DME).Materials and Methods: Twenty OCTs (each OCT contains 128 b scans) from 20 patients diagnosed with DME were included in this study. Two types of HRDs, hard exudates and small HRDs (hypothesized to be activated microglia), were identified and labeled independently by two raters. An algorithm using deep learning technology was developed based on input (in total 2,560 OCT b scans) of manual labeling and differentiation of HRDs from rater 1. 4-fold cross-validation was used to train and validate the algorithm. Dice coefficient, intraclass coefficient (ICC), correlation coefficient, and Bland–Altman plot were used to evaluate agreement of the output parameters between two methods (either between two raters or between one rater and proposed algorithm).Results: The Dice coefficients of total HRDs, hard exudates, and small HRDs area of the algorithm were 0.70 ± 0.10, 0.72 ± 0.11, and 0.46 ± 0.06, respectively. The correlations between rater 1 and proposed algorithm (range: 0.95–0.99, all p < 0.001) were stronger than the correlations between the two raters (range: 0.84–0.96, all p < 0.001) for all parameters. The ICCs were higher for all the parameters between rater 1 and proposed algorithm (range: 0.972–0.997) than those between the two raters (range: 0.860–0.953).Conclusions: Our proposed algorithm is a good tool to detect and quantify HRDs and can provide objective and repeatable information of OCT for DME patients in clinical practice and studies.https://www.frontiersin.org/articles/10.3389/fmed.2021.688986/fulldiabetic macular edemadiabetic retinopathyoptical coherence tomographydeep learning algorithmhyperreflective dots |
| spellingShingle | Haifan Huang Haifan Huang Liangjiu Zhu Weifang Zhu Tian Lin Leonoor Inge Los Chenpu Yao Xinjian Chen Haoyu Chen Algorithm for Detection and Quantification of Hyperreflective Dots on Optical Coherence Tomography in Diabetic Macular Edema Frontiers in Medicine diabetic macular edema diabetic retinopathy optical coherence tomography deep learning algorithm hyperreflective dots |
| title | Algorithm for Detection and Quantification of Hyperreflective Dots on Optical Coherence Tomography in Diabetic Macular Edema |
| title_full | Algorithm for Detection and Quantification of Hyperreflective Dots on Optical Coherence Tomography in Diabetic Macular Edema |
| title_fullStr | Algorithm for Detection and Quantification of Hyperreflective Dots on Optical Coherence Tomography in Diabetic Macular Edema |
| title_full_unstemmed | Algorithm for Detection and Quantification of Hyperreflective Dots on Optical Coherence Tomography in Diabetic Macular Edema |
| title_short | Algorithm for Detection and Quantification of Hyperreflective Dots on Optical Coherence Tomography in Diabetic Macular Edema |
| title_sort | algorithm for detection and quantification of hyperreflective dots on optical coherence tomography in diabetic macular edema |
| topic | diabetic macular edema diabetic retinopathy optical coherence tomography deep learning algorithm hyperreflective dots |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2021.688986/full |
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