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Arbelaez, María Clara; Versaci, Francesco; Vestri, Gabriele; Barboni, Piero; Savini, Giacomo

doi:10.1016/j.ophtha.2013.04.023

Cited by 1 publication

(1 citation statement)

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“…These encompass screening and diagnosing a wide array of conditions (e.g., keratoconus, IK, corneal opacity) and cataract, and preoperative planning for refractive surgery, to making automated clinical decisions for various diseases (Rampat et al, 2021). Studies have shown that the diagnostic accuracy of several AI algorithms can be as high as 92-97% in detecting keratoconus and preclinical keratoconus or forme fruste keratoconus (Arbelaez et al, 2012;Smadja et al, 2013;Hidalgo et al, 2016;Issarti et al, 2019;Lavric and Valentin, 2019;Ting et al, 2021b). Automated assessment of the corneal endothelial cell density in normal and diseased eyes as well as corneal guttata, based on AI-assisted algorithms using specular microscopy images and/or retroillumination slit-lamp photographs, have been developed to improve the management and follow-up in patients with corneal endothelial diseases and post-endothelial keratoplasty (Joseph et al, 2020;Vigueras-Guillén et al, 2020;Shilpashree et al, 2021;Soh et al, 2021;Karmakar et al, 2022).…”

Section: Integration Of Big Data and Artificial Intelligencementioning

confidence: 99%

Big data in corneal diseases and cataract: Current applications and future directions

et al. 2023

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The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of “5 Vs”—variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.

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