Automated diagnosis of age-related macular degeneration using multi-modal vertical plane feature fusion via deep learning - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Apr;49(4):2324-2333.
doi: 10.1002/mp.15541. Epub 2022 Mar 11.

Automated diagnosis of age-related macular degeneration using multi-modal vertical plane feature fusion via deep learning

Menglu Chen  1 , Kai Jin  1 , Yan Yan  1 , Xindi Liu  1 , Xiaoling Huang  1 , Zhiyuan Gao  1 , Yao Wang  1 , Shuai Wang  2 , Juan Ye  1
Affiliations

Automated diagnosis of age-related macular degeneration using multi-modal vertical plane feature fusion via deep learning

Menglu Chen et al. Med Phys. 2022 Apr.

Abstract

Purpose: To develop a computer-aided diagnostic (CADx) system of age-related macular degeneration (AMD) through feature fusion between infrared reflectance (IR) and optical coherence tomography (OCT) modalities in order to explore the superiority of multi-modality CADx system and the optimal feature fusion patterns between multi-modality inputs.

Methods: This is a dual center retrospective study. We retrospectively collected 2006 pairs of IR and OCT images to develop the algorithms. Two single-modality models and three multi-modality models were constructed for the comparison of the diagnostic efficacy. The multi-modality models were designed utilizing a novel feature fusion method, namely, vertical plane feature fusion (VPFF). The results were validated using an independent external validation dataset and compared by three ophthalmologists.

Results: In the test set of the ZJU dataset, our best model named OCT_MAIN demonstrated diagnostic efficiency with an overall accuracy of 0.9608 and area under the curve of 0.9944 for the normal category, 0.9659 for the dry AMD category, and 0.9930 for the wet AMD category. The external validation exhibited an overall accuracy of 0.9159. Its diagnostic efficiency was comparable to that of the senior ophthalmologist.

Conclusions: The VPFF method was successfully employed to develop a multi-modal intelligent diagnostic system for the AMD classification. This is a valuable complement and optimization to the existing CADx system, which reveals a wide application prospect and research potential.

Keywords: age-related macular degeneration; deep learning; feature fusion; multi-modality.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

REFERENCES

    1. Blindness GBD, Vision Impairment C, Vision Loss Expert Group of the Global Burden of Disease S. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2021;9(2):e144-e160.
    1. Mitchell P, Liew G, Gopinath B, Wong TY. Age-related macular degeneration. Lancet. 2018;392(10153):1147-1159.
    1. Stahl A. The diagnosis and treatment of age-related macular degeneration. Dtsch Arztebl Int. 2020;117(29-30):513-520.
    1. Ferris FL 3rd, Wilkinson CP, Bird A, et al. Clinical classification of age-related macular degeneration. Ophthalmology. 2013;120(4):844-851.
    1. Schmidt-Erfurth U, Klimscha S, Waldstein SM, Bogunovic H. A view of the current and future role of optical coherence tomography in the management of age-related macular degeneration. Eye (Lond). 2017;31(1):26-44.

LinkOut - more resources