Automatic glaucoma diagnosis through medical imaging informatics

• Main Authors: Liu, Jiang, Zhang, Zhuo, Wong, Damon Wing Kee, Xu, Yanwu, Yin, Fengshou, Cheng, Jun, Tan, Ngan Meng, Kwoh, Chee Keong, Xu, Dong, Tham, Yih Chung, Aung, Tin, Wong, Tien Yin
• Other Authors: School of Computer Engineering
• Format: Article
• Language: English
• Published: 2016
• Subjects: Patient data; Medical Retinal Image; Medical imaging informatics; Genome information; Multiple kernel learning; Glaucoma
• Online Access: https://hdl.handle.net/10356/81944; http://hdl.handle.net/10220/41055
• Institution: Nanyang Technological University

Background - Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease. Objective - To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient's genome information for screening. Materials and methods - 2258 cases from a population study were used to evaluate the screening software. These cases were attributed with patient personal data, retinal images and quality controlled genome data. Utilizing the multiple kernel learning-based classifier, AGLAIA-MII, combined patient personal data, major image features, and important genome single nucleotide polymorphism (SNP) features. Results and discussion - Receiver operating characteristic curves were plotted to compare AGLAIA-MII's performance with classifiers using patient personal data, images, and genome SNP separately. AGLAIA-MII was able to achieve an area under curve value of 0.866, better than 0.551, 0.722 and 0.810 by the individual personal data, image and genome information components, respectively. AGLAIA-MII also demonstrated a substantial improvement over the current glaucoma screening approach based on intraocular pressure. Conclusions - AGLAIA-MII demonstrates for the first time the capability of integrating patients' personal data, medical retinal image and genome information for automatic glaucoma diagnosis and screening in a large dataset from a population study. It paves the way for a holistic approach for automatic objective glaucoma diagnosis and screening.