Philipp Berens1,2, Sebastian M Waldstein3,4, Murat Seckin Ayhan5, Louis Kümmerle5, Hansjürgen Agostini6, Andreas Stahl7, Focke Ziemssen8. 1. Forschungsinstitut für Augenheilkunde, Universität Tübingen, Otfried-Müller-Str. 25, 72076, Tübingen, Deutschland. philipp.berens@uni-tuebingen.de. 2. Interfakultäres Institut für Bioinformatik und Medizininformatik, Universität Tübingen, Tübingen, Deutschland. philipp.berens@uni-tuebingen.de. 3. Christian Doppler Laboratory for Ophthalmic Image Analysis, Univ. Klinik für Augenheilkunde und Optometrie, Medizinische Universität Wien, Wien, Österreich. 4. Department of Ophthalmology, Westmead Hospital, University of Sydney, Sydney, Australien. 5. Forschungsinstitut für Augenheilkunde, Universität Tübingen, Otfried-Müller-Str. 25, 72076, Tübingen, Deutschland. 6. Klinik für Augenheilkunde, Universitätsklinikum Freiburg, Freiburg, Deutschland. 7. Universitätsaugenklinik Greifswald, Universitätsmedizin Greifswald, Greifswald, Deutschland. 8. Universitäts-Augenklinik Tübingen, Universität Tübingen, Tübingen, Deutschland.
Abstract
BACKGROUND: Procedures with artificial intelligence (AI), such as deep neural networks, show promising results in automatic analysis of ophthalmological imaging data. OBJECTIVE: This article discusses to what extent the application of AI algorithms can contribute to quality assurance in the field of ophthalmology. METHODS: Relevant aspects from the literature are discussed. FINDINGS: Systems based on artificial deep neural networks achieve remarkable results in the diagnostics of eye diseases, such as diabetic retinopathy and are very helpful, for example by segmenting optical coherence tomographic (OCT) images and detecting lesion components with high fidelity. To train these algorithms large data sets are required. The quality and availability of such data sets determine the continuous improvement of the algorithms. The comparison between the AI algorithms and physicians for image interpretation has also enabled insights into the diagnostic concordance between physicians. Current challenges include the development of methods for modelling decision uncertainty and improved interpretability of automated diagnostic decisions. CONCLUSION: Systems based on AI can support decision making for physicians and thereby contribute to a more efficient quality assurance.
BACKGROUND: Procedures with artificial intelligence (AI), such as deep neural networks, show promising results in automatic analysis of ophthalmological imaging data. OBJECTIVE: This article discusses to what extent the application of AI algorithms can contribute to quality assurance in the field of ophthalmology. METHODS: Relevant aspects from the literature are discussed. FINDINGS: Systems based on artificial deep neural networks achieve remarkable results in the diagnostics of eye diseases, such as diabetic retinopathy and are very helpful, for example by segmenting optical coherence tomographic (OCT) images and detecting lesion components with high fidelity. To train these algorithms large data sets are required. The quality and availability of such data sets determine the continuous improvement of the algorithms. The comparison between the AI algorithms and physicians for image interpretation has also enabled insights into the diagnostic concordance between physicians. Current challenges include the development of methods for modelling decision uncertainty and improved interpretability of automated diagnostic decisions. CONCLUSION: Systems based on AI can support decision making for physicians and thereby contribute to a more efficient quality assurance.
Authors: Andre Esteva; Alexandre Robicquet; Bharath Ramsundar; Volodymyr Kuleshov; Mark DePristo; Katherine Chou; Claire Cui; Greg Corrado; Sebastian Thrun; Jeff Dean Journal: Nat Med Date: 2019-01-07 Impact factor: 53.440
Authors: Thomas Kurmann; Siqing Yu; Pablo Márquez-Neila; Andreas Ebneter; Martin Zinkernagel; Marion R Munk; Sebastian Wolf; Raphael Sznitman Journal: Sci Rep Date: 2019-09-19 Impact factor: 4.379