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 2018-09, Vol.24 (9), p.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:	Artikel
Sprache:	eng
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
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