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RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population.

Publication ,  Journal Article
Song, A; Lusk, JB; Roh, K-M; Hsu, ST; Valikodath, NG; Lad, EM; Muir, KW; Engelhard, MM; Limkakeng, AT; Izatt, JA; McNabb, RP; Kuo, AN
Published in: Transl Vis Sci Technol
March 1, 2024

PURPOSE: To evaluate the diagnostic performance of a robotically aligned optical coherence tomography (RAOCT) system coupled with a deep learning model in detecting referable posterior segment pathology in OCT images of emergency department patients. METHODS: A deep learning model, RobOCTNet, was trained and internally tested to classify OCT images as referable versus non-referable for ophthalmology consultation. For external testing, emergency department patients with signs or symptoms warranting evaluation of the posterior segment were imaged with RAOCT. RobOCTNet was used to classify the images. Model performance was evaluated against a reference standard based on clinical diagnosis and retina specialist OCT review. RESULTS: We included 90,250 OCT images for training and 1489 images for internal testing. RobOCTNet achieved an area under the curve (AUC) of 1.00 (95% confidence interval [CI], 0.99-1.00) for detection of referable posterior segment pathology in the internal test set. For external testing, RAOCT was used to image 72 eyes of 38 emergency department patients. In this set, RobOCTNet had an AUC of 0.91 (95% CI, 0.82-0.97), a sensitivity of 95% (95% CI, 87%-100%), and a specificity of 76% (95% CI, 62%-91%). The model's performance was comparable to two human experts' performance. CONCLUSIONS: A robotically aligned OCT coupled with a deep learning model demonstrated high diagnostic performance in detecting referable posterior segment pathology in a cohort of emergency department patients. TRANSLATIONAL RELEVANCE: Robotically aligned OCT coupled with a deep learning model may have the potential to improve emergency department patient triage for ophthalmology referral.

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Published In

Transl Vis Sci Technol

DOI

EISSN

2164-2591

Publication Date

March 1, 2024

Volume

13

Issue

3

Start / End Page

12

Location

United States

Related Subject Headings

  • Retina
  • Humans
  • Deep Learning
  • 3212 Ophthalmology and optometry
  • 1113 Opthalmology and Optometry
  • 0903 Biomedical Engineering
 

Citation

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Song, A., Lusk, J. B., Roh, K.-M., Hsu, S. T., Valikodath, N. G., Lad, E. M., … Kuo, A. N. (2024). RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population. Transl Vis Sci Technol, 13(3), 12. https://doi.org/10.1167/tvst.13.3.12
Song, Ailin, Jay B. Lusk, Kyung-Min Roh, S Tammy Hsu, Nita G. Valikodath, Eleonora M. Lad, Kelly W. Muir, et al. “RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population.Transl Vis Sci Technol 13, no. 3 (March 1, 2024): 12. https://doi.org/10.1167/tvst.13.3.12.
Song A, Lusk JB, Roh K-M, Hsu ST, Valikodath NG, Lad EM, et al. RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population. Transl Vis Sci Technol. 2024 Mar 1;13(3):12.
Song, Ailin, et al. “RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population.Transl Vis Sci Technol, vol. 13, no. 3, Mar. 2024, p. 12. Pubmed, doi:10.1167/tvst.13.3.12.
Song A, Lusk JB, Roh K-M, Hsu ST, Valikodath NG, Lad EM, Muir KW, Engelhard MM, Limkakeng AT, Izatt JA, McNabb RP, Kuo AN. RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population. Transl Vis Sci Technol. 2024 Mar 1;13(3):12.

Published In

Transl Vis Sci Technol

DOI

EISSN

2164-2591

Publication Date

March 1, 2024

Volume

13

Issue

3

Start / End Page

12

Location

United States

Related Subject Headings

  • Retina
  • Humans
  • Deep Learning
  • 3212 Ophthalmology and optometry
  • 1113 Opthalmology and Optometry
  • 0903 Biomedical Engineering