Clinically applicable deep learning for diagnosis and referral in retinal disease
| العنوان: | Clinically applicable deep learning for diagnosis and referral in retinal disease |
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| المؤلفون: | Kareem Ayoub, Trevor Back, Rosalind Raine, Alan Karthikesalingam, Pearse A. Keane, Xavier Glorot, Hugh Montgomery, Julien Cornebise, Demis Hassabis, Daniel Visentin, Brendan O'Donoghue, Adnan Tufail, Catherine A Egan, Jeffrey De Fauw, Olaf Ronneberger, Peng T. Khaw, Harry Askham, Reena Chopra, Faith Mackinder, Joseph R. Ledsam, Dominic King, Nenad Tomasev, Julian Hughes, Simon Bouton, Clemens Meyer, George van den Driessche, Cian Hughes, Stanislav Nikolov, Mustafa Suleyman, Balaji Lakshminarayanan, Dawn A Sim, Bernardino Romera-Paredes, Sam Blackwell, Geraint Rees |
| المصدر: | Nature Medicine. 24:1342-1350 |
| بيانات النشر: | Springer Science and Business Media LLC, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Male, 0301 basic medicine, medicine.medical_specialty, Referral, Computer science, Clinical Decision-Making, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Disease, Retina, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Deep Learning, 0302 clinical medicine, Clinical pathway, Retinal Diseases, Optical coherence tomography, medicine, Medical imaging, Humans, Medical physics, Set (psychology), Referral and Consultation, Aged, medicine.diagnostic_test, business.industry, Deep learning, General Medicine, Middle Aged, Multiple pathologies, 030104 developmental biology, 030221 ophthalmology & optometry, Female, Artificial intelligence, business, Tomography, Optical Coherence |
| الوصف: | 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. |
| تدمد: | 1546-170X 1078-8956 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::45a19e5177294bdc580995357abe336eTest https://doi.org/10.1038/s41591-018-0107-6Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....45a19e5177294bdc580995357abe336e |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 1546170X 10788956 |
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