Visual field prediction based on temporal-spatial feature learning_Journal of Biomedical Engineering

Authors：

WANG Wo ^1,2 ,  ZHENG Xiujuan ^1,2 , LYU Zhiqing ³ , LI Ni ³ , CHEN Jun ³

1. Department of Automation, College of Electrical Engineering, Sichuan University, Chengdu 610065, P. R. China;
2. Key Laboratory of Information and Automation Technology in Sichuan Province, Chengdu 610065, P. R. China;
3. Department of Ophthalmology, West China Hospital of Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

ZHENG Xiujuan, Email: xiujuanzheng@scu.edu.cn

Keywords：

Visual field prediction; Glaucoma; Temporal-spatial feature; Convolutional long-short term memory model

DOI：

10.7507/1001-5515.202310072

Video：

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Glaucoma stands as the leading irreversible cause of blindness worldwide. Regular visual field examinations play a crucial role in both diagnosing and treating glaucoma. Predicting future visual field changes can assist clinicians in making timely interventions to manage the progression of this disease. To integrate temporal and spatial features from past visual field examination results and enhance visual field prediction, a convolutional long short-term memory (ConvLSTM) network was employed to construct a predictive model. The predictive performance of the ConvLSTM model was validated and compared with other methods using a dataset of perimetry tests from the Humphrey field analyzer at the University of Washington (UWHVF). Compared to traditional methods, the ConvLSTM model demonstrated higher prediction accuracy. Additionally, the relationship between visual field series length and prediction performance was investigated. In predicting the visual field using the previous three visual field results of past 1.5~6.0 years, it was found that the ConvLSTM model performed better, achieving a mean absolute error of 2.255 dB, a root mean squared error of 3.457 dB, and a coefficient of determination of 0.960. The experimental results show that the proposed method effectively utilizes existing visual field examination results to achieve more accurate visual field prediction for the next 0.5~2.0 years. This approach holds promise in assisting clinicians in diagnosing and treating visual field progression in glaucoma patients.

Citation： WANG Wo, ZHENG Xiujuan, LYU Zhiqing, LI Ni, CHEN Jun. Visual field prediction based on temporal-spatial feature learning. Journal of Biomedical Engineering, 2024, 41(5): 1003-1011. doi: 10.7507/1001-5515.202310072 Copy

1.	Ju W K, Perkins G A, Kim K Y, et al. Glaucomatous optic neuropathy: mitochondrial dynamics, dysfunction and protection in retinal ganglion cells. Progress in Retinal and Eye Research, 2023, 95: 101136.
2.	顾志恒, 付威威, 姚康, 等. 基于超声成像的眼压非接触测量方法. 医用生物力学, 2023, 38(4): 683-689.
3.	Cheng C Y, Wang N, Wong T Y, et al. Prevalence and causes of vision loss in East Asia in 2015: magnitude, temporal trends and projections. British Journal of Ophthalmology, 2020, 104(5): 616-622.
4.	Weinreb R N, Aung T, Medeiros F A. The pathophysiology and treatment of glaucoma: a review. JAMA, 2014, 311(18): 1901-1911.
5.	Shin J, Kim S, Kim J, et al. Visual field inference from optical coherence tomography using deep learning algorithms: a comparison between devices. Translational Vision Science & Technology, 2021, 10(7): 4.
6.	Saunders L J, Russell R A, Kirwan J F, et al. Examining visual field loss in patients in glaucoma clinics during their predicted remaining lifetime. Investigative Ophthalmology & Visual Science, 2014, 55(1): 102-109.
7.	Gardiner S K, Ren R, Yang H, et al. A method to estimate the amount of neuroretinal rim tissue in glaucoma: comparison with current methods for measuring rim area. American Journal of Ophthalmology, 2014, 157(3): 540-549.
8.	中华医学会眼科学分会青光眼学组, 中国医师协会眼科医师分会青光眼学组. 中国青光眼指南(2020年). 中华眼科杂志, 2020, 56(8): 573-586.
9.	Aref A A, Budenz D L. Detecting visual field progression. Ophthalmology, 2017, 124(12): S51-S56.
10.	Tanna A P, Bandi J R, Budenz D L, et al. Interobserver agreement and intraobserver reproducibility of the subjective determination of glaucomatous visual field progression. Ophthalmology, 2011, 118(1): 60-65.
11.	Shin J W, Sung K R, Lee G C, et al. Ganglion cell–inner plexiform layer change detected by optical coherence tomography indicates progression in advanced glaucoma. Ophthalmology, 2017, 124(10): 1466-1474.
12.	Esfandiari H, Shah P, Torkian P, et al. Five-year clinical outcomes of combined phacoemulsification and trabectome surgery at a single glaucoma center. Graefe's Archive for Clinical and Experimental Ophthalmology, 2019, 257(2): 357-362.
13.	Hu R, Racette L, Chen K S, et al. Functional assessment of glaucoma: uncovering progression. Survey of Ophthalmology, 2020, 65(6): 639-661.
14.	Kummet C M, Zamba K D, Doyle C K, et al. Refinement of pointwise linear regression criteria for determining glaucoma progression. Investigative Ophthalmology & Visual Science, 2013, 54(9): 6234-6241.
15.	Dixit A, Yohannan J, Boland M V. Assessing glaucoma progression using machine learning trained on longitudinal visual field and clinical data. Ophthalmology, 2021, 128(7): 1016-1026.
16.	Vesti E, Johnson C A, Chauhan B C. Comparison of different methods for detecting glaucomatous visual field progression. Investigative Opthalmology & Visual Science, 2003, 44(9): 3873-3879.
17.	Rabiolo A, Morales E, Mohamed L, et al. Comparison of methods to detect and measure glaucomatous visual field progression. Translational Vision Science & Technology, 2019, 8(5): 2.
18.	Saeedi O J, Elze T, D’Acunto L, et al. Agreement and predictors of discordance of 6 visual field progression algorithms. Ophthalmology, 2019, 126(6): 822-828.
19.	Kamalipour A, Moghimi S, Khosravi P, et al. Deep learning estimation of 10-2 visual field map based on circumpapillary retinal nerve fiber layer thickness measurements. American Journal of Ophthalmology, 2023, 246: 163-173.
20.	Taketani Y, Murata H, Fujino Y, et al. How many visual fields are required to precisely predict future test results in glaucoma patients when using different trend analyses. Investigative Ophthalmology & Visual Science, 2015, 56(6): 4076-4082.
21.	Wen J C, Lee C S, Keane P A, et al. Forecasting future Humphrey visual fields using deep learning. PloS one, 2019, 14(4): e0214875.
22.	Park K, Kim J, Lee J. Visual field prediction using recurrent neural network. Scientific Reports, 2019, 9(1): 8385.
23.	Shi Xingjian, Chen Zhourong, Wang Hao, et al. Convolutional LSTM Network: a machine learning approach for precipitation nowcasting// NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, Canada: MIT Press, 2015, 1: 802-810.
24.	杨鑫, 张建云, 周建中, 等. 基于ConvLSTM网络的多源降雨融合方法. 华中科技大学学报(自然科学版), 2022, 50(8): 33-39.
25.	何毅, 姚圣, 陈毅, 等. ConvLSTM神经网络的时序InSAR地面沉降时空预测. 武汉大学学报(信息科学版), (2023-07-04)[2024-09-19]. DOI: 10.13203/j.whugis20220657.
26.	Moishin M, Deo R C, Prasad R, et al. Designing deep-based learning flood forecast model with ConvLSTM hybrid algorithm. IEEE Access, 2021, 9: 50982-50993.
27.	Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735-1780.
28.	王文刀, 王润泽, 魏鑫磊, 等. 基于堆叠式双向LSTM的心电图自动识别算法. 计算机科学, 2020, 47(7): 118-124.
29.	Heijl A, Leske M C, Bengtsson B, et al. Reduction of intraocular pressure and glaucoma progression: results from the early manifest glaucoma trial. Archives of Ophthalmology, 2002, 120(10): 1268-1279.
30.	Montesano G, Chen A, Lu R, et al. UWHVF: a real-world, open source dataset of perimetry tests from the Humphrey field analyzer at the University of Washington. Translational Vision Science & Technology, 2022, 11(1): 2.

1. Ju W K, Perkins G A, Kim K Y, et al. Glaucomatous optic neuropathy: mitochondrial dynamics, dysfunction and protection in retinal ganglion cells. Progress in Retinal and Eye Research, 2023, 95: 101136.
2. 顾志恒, 付威威, 姚康, 等. 基于超声成像的眼压非接触测量方法. 医用生物力学, 2023, 38(4): 683-689.
3. Cheng C Y, Wang N, Wong T Y, et al. Prevalence and causes of vision loss in East Asia in 2015: magnitude, temporal trends and projections. British Journal of Ophthalmology, 2020, 104(5): 616-622.
4. Weinreb R N, Aung T, Medeiros F A. The pathophysiology and treatment of glaucoma: a review. JAMA, 2014, 311(18): 1901-1911.
5. Shin J, Kim S, Kim J, et al. Visual field inference from optical coherence tomography using deep learning algorithms: a comparison between devices. Translational Vision Science & Technology, 2021, 10(7): 4.
6. Saunders L J, Russell R A, Kirwan J F, et al. Examining visual field loss in patients in glaucoma clinics during their predicted remaining lifetime. Investigative Ophthalmology & Visual Science, 2014, 55(1): 102-109.
7. Gardiner S K, Ren R, Yang H, et al. A method to estimate the amount of neuroretinal rim tissue in glaucoma: comparison with current methods for measuring rim area. American Journal of Ophthalmology, 2014, 157(3): 540-549.
8. 中华医学会眼科学分会青光眼学组, 中国医师协会眼科医师分会青光眼学组. 中国青光眼指南(2020年). 中华眼科杂志, 2020, 56(8): 573-586.
9. Aref A A, Budenz D L. Detecting visual field progression. Ophthalmology, 2017, 124(12): S51-S56.
10. Tanna A P, Bandi J R, Budenz D L, et al. Interobserver agreement and intraobserver reproducibility of the subjective determination of glaucomatous visual field progression. Ophthalmology, 2011, 118(1): 60-65.
11. Shin J W, Sung K R, Lee G C, et al. Ganglion cell–inner plexiform layer change detected by optical coherence tomography indicates progression in advanced glaucoma. Ophthalmology, 2017, 124(10): 1466-1474.
12. Esfandiari H, Shah P, Torkian P, et al. Five-year clinical outcomes of combined phacoemulsification and trabectome surgery at a single glaucoma center. Graefe's Archive for Clinical and Experimental Ophthalmology, 2019, 257(2): 357-362.
13. Hu R, Racette L, Chen K S, et al. Functional assessment of glaucoma: uncovering progression. Survey of Ophthalmology, 2020, 65(6): 639-661.
14. Kummet C M, Zamba K D, Doyle C K, et al. Refinement of pointwise linear regression criteria for determining glaucoma progression. Investigative Ophthalmology & Visual Science, 2013, 54(9): 6234-6241.
15. Dixit A, Yohannan J, Boland M V. Assessing glaucoma progression using machine learning trained on longitudinal visual field and clinical data. Ophthalmology, 2021, 128(7): 1016-1026.
16. Vesti E, Johnson C A, Chauhan B C. Comparison of different methods for detecting glaucomatous visual field progression. Investigative Opthalmology & Visual Science, 2003, 44(9): 3873-3879.
17. Rabiolo A, Morales E, Mohamed L, et al. Comparison of methods to detect and measure glaucomatous visual field progression. Translational Vision Science & Technology, 2019, 8(5): 2.
18. Saeedi O J, Elze T, D’Acunto L, et al. Agreement and predictors of discordance of 6 visual field progression algorithms. Ophthalmology, 2019, 126(6): 822-828.
19. Kamalipour A, Moghimi S, Khosravi P, et al. Deep learning estimation of 10-2 visual field map based on circumpapillary retinal nerve fiber layer thickness measurements. American Journal of Ophthalmology, 2023, 246: 163-173.
20. Taketani Y, Murata H, Fujino Y, et al. How many visual fields are required to precisely predict future test results in glaucoma patients when using different trend analyses. Investigative Ophthalmology & Visual Science, 2015, 56(6): 4076-4082.
21. Wen J C, Lee C S, Keane P A, et al. Forecasting future Humphrey visual fields using deep learning. PloS one, 2019, 14(4): e0214875.
22. Park K, Kim J, Lee J. Visual field prediction using recurrent neural network. Scientific Reports, 2019, 9(1): 8385.
23. Shi Xingjian, Chen Zhourong, Wang Hao, et al. Convolutional LSTM Network: a machine learning approach for precipitation nowcasting// NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, Canada: MIT Press, 2015, 1: 802-810.
24. 杨鑫, 张建云, 周建中, 等. 基于ConvLSTM网络的多源降雨融合方法. 华中科技大学学报(自然科学版), 2022, 50(8): 33-39.
25. 何毅, 姚圣, 陈毅, 等. ConvLSTM神经网络的时序InSAR地面沉降时空预测. 武汉大学学报(信息科学版), (2023-07-04)[2024-09-19]. DOI: 10.13203/j.whugis20220657.
26. Moishin M, Deo R C, Prasad R, et al. Designing deep-based learning flood forecast model with ConvLSTM hybrid algorithm. IEEE Access, 2021, 9: 50982-50993.
27. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735-1780.
28. 王文刀, 王润泽, 魏鑫磊, 等. 基于堆叠式双向LSTM的心电图自动识别算法. 计算机科学, 2020, 47(7): 118-124.
29. Heijl A, Leske M C, Bengtsson B, et al. Reduction of intraocular pressure and glaucoma progression: results from the early manifest glaucoma trial. Archives of Ophthalmology, 2002, 120(10): 1268-1279.
30. Montesano G, Chen A, Lu R, et al. UWHVF: a real-world, open source dataset of perimetry tests from the Humphrey field analyzer at the University of Washington. Translational Vision Science & Technology, 2022, 11(1): 2.

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