Semi-AI and Full-AI digitizer: The ways to digitalize visual field big data. (August 2021) - Kim, Tong Min; Choi, Wonseo; Choi, In-Young; Park, Sang Jun; Yoon, Kun-Ho; Chang, Dong-Jin
Highlights: Semi-AI detects VF text area with actual coordinates and Full-AI with Faster R-CNN. Semi-AI is better than Full-AI in terms of system speed since Full-AI takes extra 0.258 s per sheet in prediction process. Semi-AI is better than Full-AI in terms of and human labor labeling since Full-AI needs 3128 more labeling process than Semi-AI. Full-AI can detect text area regardless of resolution and size of the VF sheets. Abstract: 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.