Archive | 2021
Multimodal Primary Open Angle Glaucoma Early Diagnosing Program Based on Clinical Process
Abstract
\n Background: Glaucoma is one of the leading causes of blinding disease. Early detection can improve patients’ quality of vision. Effectively identifying primary open angle glaucoma (POAG) using structural and functional examination is critical. Computer aided diagnosis of glaucoma requires multimodal data to find an accurate model for early glaucoma diagnosis. Methods: This study collected 87 early POAG eyes, 85 suspected POAG eyes, and 129 healthy eyes from the ophthalmology department at Second Affiliated Hospital of Harbin Medical University. Retinal nerve fiber layer thickness (RNFLt), intraocular pressure (IOP) value, visual field examination parameters and age were obtained. A powerful deep learning network segmented the FP and extracted optic nerve head (ONH) features. Machine learning classifiers (MLCs) were adopted to get the final classification results and compared with the diagnosis results of glaucoma specialists and general non-glaucoma ophthalmologists. Result: The program diagnosing early POAG, suspected POAG, and healthy eyes made overall Area Under the Curve of 0.97. Dice of optic disc and optic cup segmentation is 0.9631, 0.8435 respectively. Accuracy of the program (0.9004) is higher than general ophthalmologists (0.8195). Specificity of the program (0.9635) is higher than glaucoma specialists (0.9366).Conclusions: The program delivers superior results in diagnosing early POAG. This study’s hybrid deep learning-machine learning framework can assist with clinical decision for early POAG effectively.