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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Veröffentlicht in:Nature medicine Jg. 24; H. 9; S. 1342 - 1350
Hauptverfasser: 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
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Nature Publishing Group US 01.09.2018
Nature Publishing Group
Schlagworte:
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
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
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ISSN:1078-8956
1546-170X
1546-170X
DOI:10.1038/s41591-018-0107-6