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  • Code-free deep learning for multi-modality medical image classification NATURE MACHINE INTELLIGENCE Korot, E., Guan, Z., Ferraz, D., Wagner, S. K., Zhang, G., Liu, X., Faes, L., Pontikos, N., Finlayson, S. G., Khalid, H., Moraes, G., Balaskas, K., Denniston, A. K., Keane, P. A. 2021
  • A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability LANCET DIGITAL HEALTH Khan, S. M., Liu, X., Nath, S., Korot, E., Faes, L., Wagner, S. K., Keane, P. A., Sebire, N. J., Burton, M. J., Denniston, A. K. 2021; 3 (1): E51?E66
  • A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability. The Lancet. Digital health Khan, S. M., Liu, X. n., Nath, S. n., Korot, E. n., Faes, L. n., Wagner, S. K., Keane, P. A., Sebire, N. J., Burton, M. J., Denniston, A. K. 2021; 3 (1): e51?e66

    Abstract

    Health data that are publicly available are valuable resources for digital health research. Several public datasets containing ophthalmological imaging have been frequently used in machine learning research; however, the total number of datasets containing ophthalmological health information and their respective content is unclear. This Review aimed to identify all publicly available ophthalmological imaging datasets, detail their accessibility, describe which diseases and populations are represented, and report on the completeness of the associated metadata. With the use of MEDLINE, Google's search engine, and Google Dataset Search, we identified 94 open access datasets containing 507?724 images and 125 videos from 122?364 patients. Most datasets originated from Asia, North America, and Europe. Disease populations were unevenly represented, with glaucoma, diabetic retinopathy, and age-related macular degeneration disproportionately overrepresented in comparison with other eye diseases. The reporting of basic demographic characteristics such as age, sex, and ethnicity was poor, even at the aggregate level. This Review provides greater visibility for ophthalmological datasets that are publicly available as powerful resources for research. Our paper also exposes an increasing divide in the representation of different population and disease groups in health data repositories. The improved reporting of metadata would enable researchers to access the most appropriate datasets for their needs and maximise the potential of such resources.

    View details for DOI 10.1016/S2589-7500(20)30240-5

    View details for PubMedID 33735069

  • The retina revolution: signaling pathway therapies, genetic therapies, mitochondrial therapies, artificial intelligence. Current opinion in ophthalmology Wood, E. H., Korot, E., Storey, P. P., Muscat, S., Williams, G. A., Drenser, K. A. 2020

    Abstract

    PURPOSE OF REVIEW: The aim of this article is to review and discuss the history, current state, and future implications of promising biomedical offerings in the field of retina.RECENT FINDINGS: The technologies discussed are some of the more recent promising biomedical developments within the field of retina. There is a US Food and Drug Administration-approved gene therapy product and artificial intelligence device for retina, with many other offerings in the pipeline.SUMMARY: Signaling pathway therapies, genetic therapies, mitochondrial therapies, and artificial intelligence have shaped retina care as we know it and are poised to further impact the future of retina care. Retina specialists have the privilege and responsibility of shaping this future for the visual health of current and future generations.

    View details for DOI 10.1097/ICU.0000000000000656

    View details for PubMedID 32205471

  • STEM CELL THERAPIES, GENE-BASED THERAPIES, OPTOGENETICS, AND RETINAL PROSTHETICS: CURRENT STATE AND IMPLICATIONS FOR THE FUTURE RETINA-THE JOURNAL OF RETINAL AND VITREOUS DISEASES Wood, E. H., Tang, P. H., De la Huerta, I., Korot, E., Muscat, S., Palanker, D. A., Williams, G. A. 2019; 39 (5): 820?35

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