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Clinically applicable deep learning for diagnosis and referral in retinal disease

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

Nature medicine, 2018-09, Vol.24 (9), p.1342-1350 [Periódico revisado por pares]

United States: Nature Publishing Group

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Título:
Clinically applicable deep learning for diagnosis and referral in retinal disease
Autor: 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
Assuntos: Aged ; Analysis ; Architecture ; Artificial intelligence ; Clinical Decision-Making ; Deep Learning ; Diagnostic imaging ; Diagnostic systems ; Female ; Humans ; Information storage and retrieval ; Machine learning ; Male ; Medical imaging equipment ; Middle Aged ; 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
É parte de: Nature medicine, 2018-09, Vol.24 (9), p.1342-1350
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: 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.
Editor: United States: Nature Publishing Group
Idioma: Inglês

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Clinically applicable deep learning for diagnosis and referral in retinal disease

United States: Nature Publishing Group

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