Blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long-term use of orthokeratology
PurposeUsing deep learning model to observe the blinking characteristics and evaluate the changes and their correlation with tear film characteristics in children with long-term use of orthokeratology (ortho-K).Methods31 children (58 eyes) who had used ortho-K for more than 1 year and 31 age and gen...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Cell and Developmental Biology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcell.2024.1517240/full |
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| author | Yue Wu Siyuan Wu Yinghai Yu Yinghai Yu Xiaojun Hu Ting Zhao Yan Jiang Bilian Ke |
| author_facet | Yue Wu Siyuan Wu Yinghai Yu Yinghai Yu Xiaojun Hu Ting Zhao Yan Jiang Bilian Ke |
| author_sort | Yue Wu |
| collection | DOAJ |
| description | PurposeUsing deep learning model to observe the blinking characteristics and evaluate the changes and their correlation with tear film characteristics in children with long-term use of orthokeratology (ortho-K).Methods31 children (58 eyes) who had used ortho-K for more than 1 year and 31 age and gender-matched controls were selected for follow-up in our ophthalmology clinic from 2021/09 to 2023/10 in this retrospective case-control study. Both groups underwent comprehensive ophthalmological examinations, including Ocular Surface Disease Index (OSDI) scoring, Keratograph 5M, and LipiView. A deep learning system based on U-Net and Swim-Transformer was proposed for the observation of blinking characteristics. The frequency of incomplete blinks (IB), complete blinks (CB) and incomplete blinking rate (IBR) within 20 s, as well as the duration of the closing, closed, and opening phases in the blink wave were calculated by our deep learning system. Relative IPH% was proposed and defined as the ratio of the mean of IPH% within 20 s to the maximum value of IPH% to indicate the extent of incomplete blinking. Furthermore, the accuracy, precision, sensitivity, specificity, F1 score of the overall U-Net-Swin-Transformer model, and its consistency with built-in algorithm were evaluated as well. Independent t-test and Mann-Whitney test was used to analyze the blinking patterns and tear film characteristics between the long-term ortho-K wearer group and the control group. Spearman’s rank correlation was used to analyze the relationship between blinking patterns and tear film stability.ResultsOur deep learning system demonstrated high performance (accuracy = 98.13%, precision = 96.46%, sensitivity = 98.10%, specificity = 98.10%, F1 score = 0.9727) in the observation of blinking patterns. The OSDI scores, conjunctival redness, lipid layer thickness (LLT), and tear meniscus height did not change significantly between two groups. Notably, the ortho-K group exhibited shorter first (11.75 ± 7.42 s vs. 14.87 ± 7.93 s, p = 0.030) and average non-invasive tear break-up times (NIBUT) (13.67 ± 7.0 s vs. 16.60 ± 7.24 s, p = 0.029) compared to the control group. They demonstrated a higher IB (4.26 ± 2.98 vs. 2.36 ± 2.55, p < 0.001), IBR (0.81 ± 0.28 vs. 0.46 ± 0.39, p < 0.001), relative IPH% (0.3229 ± 0.1539 vs. 0.2233 ± 0.1960, p = 0.004) and prolonged eye-closing phase (0.18 ± 0.08 s vs. 0.15 ± 0.07 s, p = 0.032) and opening phase (0.35 ± 0.12 s vs. 0.28 ± 0.14 s, p = 0.015) compared to controls. In addition, Spearman’s correlation analysis revealed a negative correlation between incomplete blinks and NIBUT (for first-NIBUT, r = −0.292, p = 0.004; for avg-NIBUT, r = −0.3512, p < 0.001) in children with long-term use of ortho-K.ConclusionThe deep learning system based on U-net and Swim-Transformer achieved optimal performance in the observation of blinking characteristics. Children with long-term use of ortho-K presented an increase in the frequency and rate of incomplete blinks and prolonged eye closing phase and opening phase. The increased frequency of incomplete blinks was associated with decreased tear film stability, indicating the importance of monitoring children’s blinking patterns as well as tear film status in clinical follow-up. |
| format | Article |
| id | doaj-art-cf2720541ceb4a42a7e9b4e4bd30c3f5 |
| institution | Kabale University |
| issn | 2296-634X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj-art-cf2720541ceb4a42a7e9b4e4bd30c3f52025-01-28T06:41:26ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2025-01-011210.3389/fcell.2024.15172401517240Blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long-term use of orthokeratologyYue Wu0Siyuan Wu1Yinghai Yu2Yinghai Yu3Xiaojun Hu4Ting Zhao5Yan Jiang6Bilian Ke7Department of Ophthalmology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Ophthalmology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai, ChinaSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai, ChinaDepartment of Ophthalmology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Ophthalmology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Ophthalmology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaPurposeUsing deep learning model to observe the blinking characteristics and evaluate the changes and their correlation with tear film characteristics in children with long-term use of orthokeratology (ortho-K).Methods31 children (58 eyes) who had used ortho-K for more than 1 year and 31 age and gender-matched controls were selected for follow-up in our ophthalmology clinic from 2021/09 to 2023/10 in this retrospective case-control study. Both groups underwent comprehensive ophthalmological examinations, including Ocular Surface Disease Index (OSDI) scoring, Keratograph 5M, and LipiView. A deep learning system based on U-Net and Swim-Transformer was proposed for the observation of blinking characteristics. The frequency of incomplete blinks (IB), complete blinks (CB) and incomplete blinking rate (IBR) within 20 s, as well as the duration of the closing, closed, and opening phases in the blink wave were calculated by our deep learning system. Relative IPH% was proposed and defined as the ratio of the mean of IPH% within 20 s to the maximum value of IPH% to indicate the extent of incomplete blinking. Furthermore, the accuracy, precision, sensitivity, specificity, F1 score of the overall U-Net-Swin-Transformer model, and its consistency with built-in algorithm were evaluated as well. Independent t-test and Mann-Whitney test was used to analyze the blinking patterns and tear film characteristics between the long-term ortho-K wearer group and the control group. Spearman’s rank correlation was used to analyze the relationship between blinking patterns and tear film stability.ResultsOur deep learning system demonstrated high performance (accuracy = 98.13%, precision = 96.46%, sensitivity = 98.10%, specificity = 98.10%, F1 score = 0.9727) in the observation of blinking patterns. The OSDI scores, conjunctival redness, lipid layer thickness (LLT), and tear meniscus height did not change significantly between two groups. Notably, the ortho-K group exhibited shorter first (11.75 ± 7.42 s vs. 14.87 ± 7.93 s, p = 0.030) and average non-invasive tear break-up times (NIBUT) (13.67 ± 7.0 s vs. 16.60 ± 7.24 s, p = 0.029) compared to the control group. They demonstrated a higher IB (4.26 ± 2.98 vs. 2.36 ± 2.55, p < 0.001), IBR (0.81 ± 0.28 vs. 0.46 ± 0.39, p < 0.001), relative IPH% (0.3229 ± 0.1539 vs. 0.2233 ± 0.1960, p = 0.004) and prolonged eye-closing phase (0.18 ± 0.08 s vs. 0.15 ± 0.07 s, p = 0.032) and opening phase (0.35 ± 0.12 s vs. 0.28 ± 0.14 s, p = 0.015) compared to controls. In addition, Spearman’s correlation analysis revealed a negative correlation between incomplete blinks and NIBUT (for first-NIBUT, r = −0.292, p = 0.004; for avg-NIBUT, r = −0.3512, p < 0.001) in children with long-term use of ortho-K.ConclusionThe deep learning system based on U-net and Swim-Transformer achieved optimal performance in the observation of blinking characteristics. Children with long-term use of ortho-K presented an increase in the frequency and rate of incomplete blinks and prolonged eye closing phase and opening phase. The increased frequency of incomplete blinks was associated with decreased tear film stability, indicating the importance of monitoring children’s blinking patterns as well as tear film status in clinical follow-up.https://www.frontiersin.org/articles/10.3389/fcell.2024.1517240/fullorthokeratologychildrenblinking patterntear filmdeep learning system |
| spellingShingle | Yue Wu Siyuan Wu Yinghai Yu Yinghai Yu Xiaojun Hu Ting Zhao Yan Jiang Bilian Ke Blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long-term use of orthokeratology Frontiers in Cell and Developmental Biology orthokeratology children blinking pattern tear film deep learning system |
| title | Blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long-term use of orthokeratology |
| title_full | Blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long-term use of orthokeratology |
| title_fullStr | Blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long-term use of orthokeratology |
| title_full_unstemmed | Blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long-term use of orthokeratology |
| title_short | Blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long-term use of orthokeratology |
| title_sort | blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long term use of orthokeratology |
| topic | orthokeratology children blinking pattern tear film deep learning system |
| url | https://www.frontiersin.org/articles/10.3389/fcell.2024.1517240/full |
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