Keratoconus-susceptibility gene identification by corneal thickness genome-wide association study and artificial intelligence IBM Watson.
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ABSTRACT:
SUBMITTER: Hosoda Y
PROVIDER: S-EPMC7395727 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Publications
Keratoconus-susceptibility gene identification by corneal thickness genome-wide association study and artificial intelligence IBM Watson.
Hosoda Yoshikatsu Y Miyake Masahiro M Meguro Akira A Tabara Yasuharu Y Iwai Sachiko S Ueda-Arakawa Naoko N Nakano Eri E Mori Yuki Y Yoshikawa Munemitsu M Nakanishi Hideo H Khor Chiea-Chuen CC Saw Seang-Mei SM Yamada Ryo R Matsuda Fumihiko F Cheng Ching-Yu CY Mizuki Nobuhisa N Tsujikawa Akitaka A Yamashiro Kenji K
Communications biology 20200731 1
Keratoconus is a common ocular disorder that causes progressive corneal thinning and is the leading indication for corneal transplantation. Central corneal thickness (CCT) is a highly heritable characteristic that is associated with keratoconus. In this two-stage genome-wide association study (GWAS) of CCT, we identified a locus for CCT, namely STON2 rs2371597 (P = 2.32 × 10<sup>-13</sup>), and confirmed a significant association between STON2 rs2371597 and keratoconus development (P = 0.041). A ...[more]
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Project description:IntroductionThe application of artificial intelligence (AI) systems in ophthalmology is rapidly expanding. Early detection and management of keratoconus is important for preventing disease progression and the need for corneal transplant. We review studies regarding the utility of AI in the diagnosis and management of keratoconus and other corneal ectasias.MethodsWe conducted a systematic search for relevant original, English-language research studies in the PubMed, Web of Science, Embase, and Cochrane databases from inception to October 31, 2023, using a combination of the following keywords: artificial intelligence, deep learning, machine learning, keratoconus, and corneal ectasia. Case reports, literature reviews, conference proceedings, and editorials were excluded. We extracted the following data from each eligible study: type of AI, input used for training, output, ground truth or reference, dataset size, availability of algorithm/model, availability of dataset, and major study findings.ResultsNinety-three original research studies were included in this review, with the date of publication ranging from 1994 to 2023. The majority of studies were regarding the use of AI in detecting keratoconus or subclinical keratoconus (n=61). Among studies regarding keratoconus diagnosis, the most common inputs were corneal topography, Scheimpflug-based corneal tomography, and anterior segment-optical coherence tomography. This review also summarized 16 original research studies regarding AI-based assessment of severity and clinical features, 7 studies regarding the prediction of disease progression, and 6 studies regarding the characterization of treatment response. There were only three studies regarding the use of AI in identifying susceptibility genes involved in the etiology and pathogenesis of keratoconus.DiscussionAlgorithms trained on Scheimpflug-based tomography seem promising tools for the early diagnosis of keratoconus that can be particularly applied in low-resource communities. Future studies could investigate the application of AI models trained on multimodal patient information for staging keratoconus severity and tracking disease progression.
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