Abstract
Glaucoma is the collective term for a group of diseases that cause damage to the optic nerve. Retina nerve fiber layer (RNFL) thickness is an indicative reference for evaluating the progression of glaucoma. In this demo paper, we proposed an intelligent data extraction system for RNFL examination report, which can extract the RNFL thickness data from the report photo. The system consists of two procedures viz. target area segmentation and structure data extraction. This system can reduce the amount of data that needs to be entered manually, thus reducing the manual workload in electronic health records (EHRs) system. The demo video of the proposed system is available at: https://doi.org/10.6084/m9.figshare.20098865.v1.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Easyocr homepage. https://www.jaided.ai/easyocr/
Baxter, S.L., Marks, C., Kuo, T.T., Ohno-Machado, L., Weinreb, R.N.: Machine learning-based predictive modeling of surgical intervention in glaucoma using systemic data from electronic health records. Am. J. Ophthalmol. 208, 30–40 (2019). https://doi.org/10.1016/j.ajo.2019.07.005
Cheng, J., Dong, L., Lapata, M.: Long short-term memory-networks for machine reading, pp. 551–561, January 2016. https://doi.org/10.18653/v1/D16-1053
Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. In: ICML 2006, pp. 369–376. Association for Computing Machinery, New York (2006). https://doi.org/10.1145/1143844.1143891
Hamid, M.S., Valicevic, A., Brenneman, B., Niziol, L.M., Stein, J.D., Newman-Casey, P.A.: Text parsing-based identification of patients with poor glaucoma medication adherence in the electronic health record. Am. J. Ophthalmol. 222, 54–59 (2021). https://doi.org/10.1016/j.ajo.2020.09.008
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017). https://doi.org/10.1109/CVPR.2017.243
Jhaveri, R., John, J., Rosenman, M.: Electronic health record network research in infectious diseases. Clin. Ther. 43(10), 1668–1681 (2021). https://doi.org/10.1016/j.clinthera.2021.09.002
Lee, E.B., Hu, W., Singh, K., Wang, S.Y.: The association among blood pressure, blood pressure medications, and glaucoma in a nationwide electronic health records database. Ophthalmology 129(3), 276–284 (2022)
Leung, C.K., et al.: Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: analysis of the retinal nerve fiber layer map for glaucoma detection. Ophthalmology 117(9), 1684–1691 (2010). https://doi.org/10.1016/j.ophtha.2010.01.026
Li, F., et al.: A deep-learning system predicts glaucoma incidence and progression using retinal photographs. J. Clin. Investigation 132(11), June 2022. https://doi.org/10.1172/JCI157968
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936–944 (2017). https://doi.org/10.1109/CVPR.2017.106
Pandit, R.R., Boland, M.V.: The impact of an electronic health record transition on a glaucoma subspecialty practice. Ophthalmology 120(4), 753–760 (2013). https://doi.org/10.1016/j.ophtha.2012.10.002
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv e-prints (2018)
Robbins, C.C., et al.: An initiative to improve follow-up of patients with glaucoma. Ophthalmol. Sci. 1(4), 100059 (2021). https://doi.org/10.1016/j.xops.2021.100059
Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2017). https://doi.org/10.1109/tpami.2016.2646371
Singer, E., et al.: Characterizing sleep disorders in an autism-specific collection of electronic health records. Sleep Med. 92, 88–95 (2022). https://doi.org/10.1016/j.sleep.2022.03.009
Taxter, A.J., Natter, M.D.: Using the electronic health record to enhance care in pediatric rheumatology. Rheumatic Disease Clinics North Am. 48(1), 245–258 (2022). https://doi.org/10.1016/j.rdc.2021.08.004
Tuulonen, A., Airaksinen, P.J.: Initial glaucomatous optic disk and retinal nerve fiber layer abnormalities and their progression. Am. J. Ophthalmol. 111(4), 485–490 (1991). https://doi.org/10.1016/S0002-9394(14)72385-2
Wang, S.Y., Tseng, B., Hernandez-Boussard, T.: Deep learning approaches for predicting glaucoma progression using electronic health records and natural language processing. Ophthalmol. Sci. 2(2), 100127 (2022). https://doi.org/10.1016/j.xops.2022.100127
Yao, C., Tang, J., Hu, M., Wu, Y., Guo, W., Li, Q., Zhang, X.P.: Claw u-net: a unet variant network with deep feature concatenation for scleral blood vessel segmentation. Lecture Notes in Computer Science, pp. 67–78 (2021). https://doi.org/10.1007/978-3-030-93049-3_6
Yuen, H., Princen, J., Illingworth, J., Kittler, J.: Comparative study of hough transform methods for circle finding. Image Vis. Comput. 8(1), 71–77 (1990). https://doi.org/10.1016/0262-8856(90)90059-E
Zhang, L., Wu, Y., Hu, M., Guo, W.: Automatic image analysis of episcleral hemangioma applied to the prognosis prediction of trabeculotomy in sturge-weber syndrome. Displays 71, 102118 (2022). https://doi.org/10.1016/j.displa.2021.102118
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hua, C., Shi, Y., Hu, M., Wu, Y. (2022). Intelligent Data Extraction System for RNFL Examination Reports. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13606. Springer, Cham. https://doi.org/10.1007/978-3-031-20503-3_45
Download citation
DOI: https://doi.org/10.1007/978-3-031-20503-3_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20502-6
Online ISBN: 978-3-031-20503-3
eBook Packages: Computer ScienceComputer Science (R0)