Trends in Utilization of Visual Field Tests for Glaucoma Patients: A Nationwide Study Using the Korean Health Insurance Review and Assessment Database

Purpose

To analyze 10-year trends in utilization of visual field tests for adult glaucoma or glaucoma-suspect patients using the Korean Health Insurance Review and Assessment data.

Methods

Health claims for the years 2010 to 2019, as recorded via Korea’s Health Insurance Review and Assessment service, were accessed. We identified glaucoma patients using the glaucoma diagnostic codes H40 (glaucoma) and H42 (glaucoma in other diseases classified elsewhere). For verification of the glaucoma diagnosis, information on any antiglaucoma medication prescriptions and ocular surgery history also was obtained. Visual field testing data was isolated using procedural codes E6690 (kinetic perimetry) and E6691 (standard automated perimetry [SAP]) performed in tertiary hospitals. Any changes in visual field test utilization were identified using regression trend analysis.

Results

From 2010 to 2019, the total number of SAP procedures performed in tertiary hospitals for either glaucoma or glaucoma-suspect patients increased gradually from 93,459 to 216,433. With regard to kinetic perimetry examinations, the total number decreased gradually from 6,364 to 3,792. The yearly average SAP number per patient showed a slight increase, from 1.168 to 1.248 (β = 0.008, R² = 0.669, p = 0.004). Meanwhile, the yearly average number of kinetic perimeter examinations per patient showed a significant decrease, from 1.093 to 0.940 (β = −0.013, R² = 0.580, p = 0.010).

Conclusions

Between 2010 and 2019, the yearly average number of SAP procedures performed per glaucoma or glaucoma-suspect patient increased in Korea. Meanwhile, the yearly average number of kinetic perimetry examinations per patient significantly decreased.

Semi-AI and Full-AI digitizer: The ways to digitalize visual field big data

BACKGROUND AND OBJECTIVE

Glaucoma is one of the major diseases that cause blindness, which is incurable and irreversible, and it is essential to detect glaucoma vision deficits in treatment and check the progression of vision disorders in advance. In order to minimize the risk of glaucoma, it is necessary not only to diagnose and observe glaucoma but also to predict prognosis via indicators from Visual Field (VF) tests. However, information from the VF test cannot be directly used in clinical studies because most medical institutions store VF test sheets in Portable Document Format (PDF) or image files in different standards.

METHODS

We developed AI-based real-time VF big data digitizing systems that digitalize VF test images in real-time in two ways; Semi-AI and Full-AI digitizer. The Semi-AI digitizer detects the VF text area with actual coordinates derived from mouse handler system. Full-AI digitizer detects the VF text area with Faster Region Based Convolutional Neural Networks (RCNN). After detecting the text area, both systems extract texts with Recurrent Neural Network based Optical Character Recognition. Semi-AI and Full-AI digitizer post-processes the extracted text results with in-system algorithm and out-of-system algorithm, respectively.

RESULTS

Both systems used 325,310 VF test sheets from a tertiary hospital and extracted a total of 5,530,270 texts. From the 100 randomly selected VF sheets, 3,400 texts were used for the validation. Semi-AI and Full-AI digitizer showed 0.993 and 0.983 of accuracy, respectively.

CONCLUSION

This study demonstrates the effectiveness of AI applications in detecting text areas and the different implementation methodologies of the post-processing process. In detecting text area, Semi-AI may be better than Full-AI digitizer in terms of system speed and human labor labeling if the number of types to be classified is small. However, Full-AI digitizer is recommended because it allows detecting text area regardless of resolution and size of the VF sheets, as the types of real-world VF test sheets cannot be predicted, and the types become more unpredictable when extended to multi-hospital studies. For Post-preprocessing, Semi-AI methodology is recommended because Semi-AI produced higher results with less effort and considered the convenience of researchers by implementing them as in-system.