G
Gabriella Moraes
Researcher at Moorfields Eye Hospital
Publications - 19
Citations - 1470
Gabriella Moraes is an academic researcher from Moorfields Eye Hospital. The author has contributed to research in topics: Macular degeneration & Deep learning. The author has an hindex of 9, co-authored 18 publications receiving 686 citations. Previous affiliations of Gabriella Moraes include Government of the United Kingdom & University College London.
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Journal ArticleDOI
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
Xiaoxuan Liu,Livia Faes,Aditya Kale,Siegfried K Wagner,Dun Jack Fu,Alice Bruynseels,Thushika Mahendiran,Gabriella Moraes,Mohith Shamdas,Christoph Kern,Christoph Kern,Joseph R. Ledsam,Martin Schmid,Konstantinos Balaskas,Konstantinos Balaskas,Eric J. Topol,Lucas M. Bachmann,Pearse A. Keane,Alastair K Denniston +18 more
TL;DR: A major finding of the review is that few studies presented externally validated results or compared the performance of deep learning models and health-care professionals using the same sample, which limits reliable interpretation of the reported diagnostic accuracy.
Journal ArticleDOI
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
Livia Faes,Siegfried K Wagner,Siegfried K Wagner,Dun Jack Fu,Xiaoxuan Liu,Xiaoxuan Liu,Xiaoxuan Liu,Edward Korot,Joseph R. Ledsam,Trevor Back,Reena Chopra,Reena Chopra,Nikolas Pontikos,Christoph Kern,Christoph Kern,Gabriella Moraes,Martin Schmid,Dawn A Sim,Dawn A Sim,Konstantinos Balaskas,Konstantinos Balaskas,Lucas M. Bachmann,Alastair K Denniston,Pearse A. Keane,Pearse A. Keane +24 more
TL;DR: All models, except the automated deep learning model trained on the multilabel classification task of the NIH CXR14 dataset, showed comparable discriminative performance and diagnostic properties to state-of-the-art performing deep learning algorithms.
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Predicting conversion to wet age-related macular degeneration using deep learning
Jason Yim,Reena Chopra,Terry Spitz,Jim Winkens,Annette Obika,Christopher Kelly,Harry Askham,Marko Lukic,Josef Huemer,Katrin Fasler,Gabriella Moraes,Clemens Meyer,Marc Wilson,Jonathan Mark Dixon,Cian Hughes,Geraint Rees,Peng T. Khaw,Alan Karthikesalingam,Dominic King,Demis Hassabis,Mustafa Suleyman,Trevor Back,Joseph R. Ledsam,Pearse A. Keane,Jeffrey De Fauw +24 more
TL;DR: In individuals diagnosed with age-related macular degeneration in one eye, a deep learning model can predict progression to the ‘wet’, sight-threatening form of the disease in the second eye within a 6-month time frame, and demonstrates the potential of using AI to predict disease progression.
Journal ArticleDOI
Code-free deep learning for multi-modality medical image classification
Edward Korot,Edward Korot,Edward Korot,Zeyu Guan,Daniel Ferraz,Daniel Ferraz,Siegfried K Wagner,Gongyu Zhang,Xiaoxuan Liu,Xiaoxuan Liu,Xiaoxuan Liu,Livia Faes,Nikolas Pontikos,Samuel G. Finlayson,Hagar Khalid,Hagar Khalid,Gabriella Moraes,Gabriella Moraes,Konstantinos Balaskas,Konstantinos Balaskas,Alastair K Denniston,Pearse A. Keane,Pearse A. Keane +22 more
TL;DR: This study comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models and demonstrated uniformly higher classification performance with the optical coherence tomography modality.
Journal ArticleDOI
Quantitative Analysis of OCT for Neovascular Age-Related Macular Degeneration Using Deep Learning.
Gabriella Moraes,Dun Jack Fu,Marc Wilson,Hagar Khalid,Siegfried K Wagner,Edward Korot,Daniel Ferraz,Livia Faes,Christopher Kelly,Terry Spitz,Praveen J Patel,Konstantinos Balaskas,Tiarnan D L Keenan,Pearse A. Keane,Reena Chopra,Reena Chopra +15 more
TL;DR: Large-scale automated quantification of a novel range of baseline features in neovascular AMD is reported and enhanced, automated OCT segmentation may assist personalization of real-world care and the detection of novel structure–function correlations.