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A fluorescein angiography-based computer-aided algorithm for assessing the retinal vasculature in diabetic retinopathy

Eye volume 37, pages 1293–1301 (2023)Cite this article

Subjects

Abstract

Objective

To present a fluorescein angiography (FA)‒based computer algorithm for quantifying retinal blood flow, perfusion, and permeability, in patients with diabetic retinopathy (DR). Secondary objectives were to quantitatively assess treatment efficacy following panretinal photocoagulation (PRP) and define thresholds for pathology based on a new retinovascular function (RVF) score for quantifying disease severity.

Methods

FA images of 65 subjects (58 patients and 7 healthy volunteers) were included. Dye intensity kinetics were derived using pixel-wise linear regression as a measure of retinal blood flow, perfusion, and permeability. Maps corresponding to each measure were then generated for each subject and segmented further using an ETDRS grid. Non-parametric statistical analyses were performed between all ETDRS subfields. For 16 patients, the effect of PRP was measured using the same parameters, and an amalgam of RVF was used to create an RVF index. For ten post-treatment patients, the change in FA-derived data was compared to the macular thickness measured using optical coherence tomography.

Results

Compared to healthy controls, patients had significantly lower retinal and regional perfusion and flow, as well as higher retinal permeability (p < 0.05). Moreover, retinal flow was inversely correlated with permeability (R = –0.41; p < 0.0001). PRP significantly reduced retinal permeability (p < 0.05). The earliest marker of DR was reduced retinal blood flow, followed by increased permeability. FA-based RVF index was a more sensitive indicator of treatment efficacy than macular thickness.

Conclusions

Our algorithm can be used to quantify retinovascular function, providing an earlier diagnosis and an objective characterisation of disease state, disease progression, and response to treatment.

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Fig. 1: The approach.
Fig. 2: Using the algorithm to estimate disease severity.
Fig. 3: Effect of PRP on retinal thickness versus the estimated disease severity.
Fig. 4: Summary of retinal vascular parameters measured in healthy controls and patients with diabetic retinopathy.
Fig. 5: Assessment of the effect of panretinal photocoagulation (PRP) in patients with DR.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada

    Amir Abbasnejad

  2. Emagix, Inc., Halifax, NS, Canada

    Amir Abbasnejad & Alon Friedman

  3. Department of Ophthalmology, Faculty of Medicine, Carmel Medical Center, Technion, Haifa, Israel

    Oren Tomkins-Netzer

  4. Department of Ophthalmology, QEII Hospital, Dalhousie University, Halifax, NS, Canada

    Aaron Winter & Alan Cruess

  5. Departments of Medical Neuroscience and Pediatrics, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada

    Alon Friedman

  6. Neurology Residency Training Program and Department of Neurology and Neurosurgery, Jewish General Hospital (J.M.), McGill University, Montreal, QC, Canada

    Yonatan Serlin

  7. Department of Ophthalmology, Hadassah-Hebrew University Medical Center, Jerusalem, Israel

    Jaime Levy

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Contributions

The authors’ responsibilities were as follows: AF and JL formulated the research question and designed the study; AW, AC, YS and JL obtained data; AA analysed the data; AA, OT-N, AL, YS and JL: interpreted the findings and wrote the article. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jaime Levy.

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Competing interests

AA and AF: Emagix, Inc., Halifax, Nova Scotia, Canada. The other authors declare no competing interests.

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Abbasnejad, A., Tomkins-Netzer, O., Winter, A. et al. A fluorescein angiography-based computer-aided algorithm for assessing the retinal vasculature in diabetic retinopathy. Eye 37, 1293–1301 (2023). https://doi.org/10.1038/s41433-022-02120-4

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