Expert-level Automated Biomarker Identification in Optical Coherence Tomography Scans

Kurmann, Thomas; Yu, Siqing; Márquez-Neila, Pablo; Ebneter, Andreas; Zinkernagel, Martin; Munk, Marion R.; Wolf, Sebastian; Sznitman, Raphael (2019). Expert-level Automated Biomarker Identification in Optical Coherence Tomography Scans. Scientific reports, 9(1), p. 13605. Springer Nature 10.1038/s41598-019-49740-7

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In ophthalmology, retinal biological markers, or biomarkers, play a critical role in the management of chronic eye conditions and in the development of new therapeutics. While many imaging technologies used today can visualize these, Optical Coherence Tomography (OCT) is often the tool of choice due to its ability to image retinal structures in three dimensions at micrometer resolution. But with widespread use in clinical routine, and growing prevalence in chronic retinal conditions, the quantity of scans acquired worldwide is surpassing the capacity of retinal specialists to inspect these in meaningful ways. Instead, automated analysis of scans using machine learning algorithms provide a cost effective and reliable alternative to assist ophthalmologists in clinical routine and research. We present a machine learning method capable of consistently identifying a wide range of common retinal biomarkers from OCT scans. Our approach avoids the need for costly segmentation annotations and allows scans to be characterized by biomarker distributions. These can then be used to classify scans based on their underlying pathology in a device-independent way.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Ophthalmology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Kurmann, Thomas Kevin; Yu, Siqing; Márquez Neila, Pablo; Ebneter, Andreas; Zinkernagel, Martin; Munk, Marion; Wolf, Sebastian and Sznitman, Raphael

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
000 Computer science, knowledge & systems
600 Technology > 620 Engineering

ISSN:

2045-2322

Publisher:

Springer Nature

Language:

English

Submitter:

Thomas Kevin Kurmann

Date Deposited:

25 Oct 2019 15:02

Last Modified:

27 Oct 2019 02:46

Publisher DOI:

10.1038/s41598-019-49740-7

PubMed ID:

31537854

BORIS DOI:

10.7892/boris.134202

URI:

https://boris.unibe.ch/id/eprint/134202

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