Japan eye genetics consortium; a platform of genes, phenotypes, and application of AI
Authors: Fujinami Kaoru , Fujinami‐Yokokawa Yu 
Published: December 2022

Conflict of Interest Disclosures: All authors have completed Disclosure of Potential Conflicts of Interest. Individual investigators who participate in the sponsored project(s) are not directly compensated by the sponsor but may receive salary or other support from the institution to support their effort on the project(s).

Kaoru Fujinami is a paid consultant of Astellas Pharma Inc, Kubota Pharmaceutical Holdings Co., Ltd, and Acucela Inc. and NightstaRx Limited. Kaoru Fujinami reports personal fees from Astellas Pharma Inc, personal fees from Kubota Pharmaceutical Holdings Co., Ltd., personal fees from Acucela Inc., personal fees from NightstaRx Limited., personal fees from SANTEN Company Limited, personal fees from Foundation Fighting Blindness, personal fees from Foundation Fighting Blindness Clinical Research Institute, personal fees from Japanese Ophthalmology Society, personal fees from Japan Retinitis Pigmentosa Society. Laboratory of Visual Physiology, Division for Vision Research, National Institute of Sensory Organs, National Hospital Organization, Tokyo Medical Center, Tokyo, Japan is supported by grants from Astellas Pharma Inc (NCT03281005), outside the submitted work.

Kaoru Fujinami is supported by grants from Grant‐in‐Aid for Young Scientists (A) of the Ministry of Education, Culture, Sports, Science and Technology, Japan (16H06269), grants from Grant‐in‐Aid for Scientists to support international collaborative studies of the Ministry of Education, Culture, Sports, Science and Technology, Japan (16KK01930002), grants from National Hospital Organization Network Research Fund (H30‐NHO‐Sensory Organs‐03), grants from FOUNDATION FIGHTING BLINDNESS ALAN LATIES CAREER DEVELOPMENT PROGRAM (CF‐CL‐0416‐0696‐UCL), grants from Health Labour Sciences Research Grant, The Ministry of Health Labour and Welfare (201711107A), and grants from Great Britain Sasakawa Foundation Butterfield Awards.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Inherited retinal disease (IRD) is a leading cause of blindness both in children and adults of working age in Japan. The Japan Eye Genetics Consortium (JEGC) was to establish a large cohort of Japanese patients with IRD in multicenter studies. The clinical and genetic data are stored in JEGC online database (>3000 subjects). This research network expands to the East Asian countries, and the East Asin IRD society (EAIRDs) develops international large cohort studies with large‐scale data sharing.

The provision of an accurate diagnosis of IRD is still difficult or unavailable in East Asia due to the limited access to multidisciplinary teams of specialists who can perform specific clinical investigations, including retinal imaging as well as genetic testing, interpretation of genetic results (i.e., genetic diagnosis), and counselling. There are currently few approved treatment approaches for IRD. Thus, it is widely recognized that the development of accurate gene‐specific diagnosis and novel therapeutic interventions is crucial to meet the urgent unmet needs of people suffering from blindness due to IRD.

Recently, deep learning techniques have been successfully applied in various medical fields, and the utilization of machine learning‐assisted diagnosis has been extensively promoted. Deep learning based on clinical images has been rapidly developed to predict diagnosis of common ocular disorders such as diabetic retinopathy and age‐related macular degeneration. However, AI‐oriented bioinformatic engineering has never been applied to the clinic of ophthalmic orphan diseases.

Therefore, we aim to create a platform of comprehensive patient information and AI‐based diagnostic support in the JEGC/EAIRDs/Japan national hospital organization network. Here, a study design and preliminary data of an AI‐based diagnostic support system are illustrated.