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.
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CITATION STYLE
Hua, C., Shi, Y., Hu, M., & Wu, Y. (2022). Intelligent Data Extraction System for RNFL Examination Reports. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13606 LNAI, pp. 537–542). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-20503-3_45
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