Clinically applicable deep learning for diagnosis and referral in retinal disease

The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of...

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Vydáno v:	Nature medicine Ročník 24; číslo 9; s. 1342 - 1350
Hlavní autoři:	De Fauw, Jeffrey, Ledsam, Joseph R., Romera-Paredes, Bernardino, Nikolov, Stanislav, Tomasev, Nenad, Blackwell, Sam, Askham, Harry, Glorot, Xavier, O’Donoghue, Brendan, Visentin, Daniel, van den Driessche, George, Lakshminarayanan, Balaji, Meyer, Clemens, Mackinder, Faith, Bouton, Simon, Ayoub, Kareem, Chopra, Reena, King, Dominic, Karthikesalingam, Alan, Hughes, Cían O., Raine, Rosalind, Hughes, Julian, Sim, Dawn A., Egan, Catherine, Tufail, Adnan, Montgomery, Hugh, Hassabis, Demis, Rees, Geraint, Back, Trevor, Khaw, Peng T., Suleyman, Mustafa, Cornebise, Julien, Keane, Pearse A., Ronneberger, Olaf
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Nature Publishing Group US 01.09.2018 Nature Publishing Group
Témata:	631/114/1305 692/1807/1482 692/700/139 692/700/1421/2025 Aged Analysis Architecture Artificial intelligence Biomedical and Life Sciences Biomedicine Cancer Research Clinical Decision-Making Deep Learning Diagnostic imaging Diagnostic systems Female Humans Infectious Diseases Information storage and retrieval Machine learning Male Medical imaging equipment Metabolic Diseases Middle Aged Molecular Medicine Neurosciences Optical Coherence Tomography Referral and Consultation Retina Retina - diagnostic imaging Retina - pathology Retinal diseases Retinal Diseases - diagnosis Retinal Diseases - diagnostic imaging Tomography Tomography, Optical Coherence Training United Kingdom
ISSN:	1078-8956, 1546-170X, 1546-170X
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Popis
Shrnutí:	The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting. A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1078-8956 1546-170X 1546-170X
DOI:	10.1038/s41591-018-0107-6