CNN-Based Device-Agnostic Feature Extraction From ONH OCT Scans.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY Translational Vision Science & Technology Pub Date : 2024-12-02 DOI:10.1167/tvst.13.12.5

Sjoerd J Driessen, Karin A van Garderen, Danilo Andrade De Jesus, Luisa Sanchez Brea, João Barbosa-Breda, Bart Liefers, Hans G Lemij, Doreen Nelson-Ayifah, Angelina Ampong, Pieter W M Bonnemaijer, Alberta A H J Thiadens, Caroline C W Klaver

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引用次数: 0

Abstract

Purpose: Optical coherence tomography (OCT)-derived measurements of the optic nerve head (ONH) from different devices are not interchangeable. This poses challenges to patient follow-up and collaborative studies. Here, we present a device-agnostic method for the extraction of OCT biomarkers using artificial intelligence.

Methods: ONH-centered OCT volumes from the Heidelberg SPECTRALIS, ZEISS CIRRUS HD-OCT 5000, and Topcon 3D OCT-1000 Mark I/II and 3D OCT-2000 devices were annotated by trained graders. A convolutional neural network (CNN) was trained on these segmented B-scans and utilized to obtain several ONH biomarkers, such as the retinal nerve fiber layer (RNFL) and the minimal rim width (MRW). The CNN results were compared between different devices and to the manufacturer-reported values using an independent test set.

Results: The intraclass correlation coefficient (ICC) for the circumpapillary retinal nerve fiber layer (cpRNFL) at 3.4 mm reported by the CIRRUS and 3D OCT-2000 was 0.590 (95% confidence interval [CI], -0.079 to 0.901), and our CNN resulted in a cpRNFL ICC of 0.667 (95% CI, -0.035 to 0.939). The cpRNFL at 3.5 mm on the CIRRUS, 3D OCT-2000, and SPECTRALIS generated by the CNN resulted in an ICC of 0.656 (95% CI, 0.055-0.922). Comparing the global mean MRWs from the SPECTRALIS between CNN and manufacturer yielded an ICC of 0.983 (95% CI, 0.917-0.997). The CNN ICC for the MRW among the CIRRUS, 3D OCT-2000, and SPECTRALIS was 0.917 (95% CI, 0.947-0.981).

Conclusions: Our device-agnostic feature extraction from ONH OCT scans showed a higher reliability than the measures generated by the manufacturers for cpRNFL. MRW measurements compared very well among the manufacturers.

Translational relevance: This open-source software can robustly extract a wide range of biomarkers from any OCT device, removing the dependency on manufacturer-specific algorithms, which has significant implications for patient follow-up and collaborative research.

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来源期刊

Translational Vision Science & Technology Engineering-Biomedical Engineering

CiteScore

5.70

自引率

3.30%

发文量

346

审稿时长

25 weeks

期刊介绍： Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO. The journal covers a broad spectrum of work, including but not limited to: Applications of stem cell technology for regenerative medicine, Development of new animal models of human diseases, Tissue bioengineering, Chemical engineering to improve virus-based gene delivery, Nanotechnology for drug delivery, Design and synthesis of artificial extracellular matrices, Development of a true microsurgical operating environment, Refining data analysis algorithms to improve in vivo imaging technology, Results of Phase 1 clinical trials, Reverse translational ("bedside to bench") research. TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.