Communications medicine · 2025
Artificial intelligence assisted clinical fluorescence imaging achieves in vivo cellular resolution comparable to adaptive optics ophthalmoscopy.
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Li Joanne, Liu Jianfei, Das Vineeta, Le Hong, Aguilera Nancy, Bower Andrew J, Giannini John P, Lu Rongwen, Abouassali Sarah, Chew Emily Y, Brooks Brian P, Zein Wadih M, Huryn Laryssa A, Volkov Andrei, Liu Tao, Tam Johnny
Аннотация
BACKGROUND: Advancements in biomedical optical imaging have enabled researchers to achieve cellular-level imaging in the living human body. However, research-grade technology is not always widely available in routine clinical practice. In this paper, we incorporated artificial intelligence (AI) with standard clinical imaging to successfully obtain images of the retinal pigment epithelial (RPE) cells in living human eyes.
METHODS: Following intravenous injection of indocyanine green (ICG) dye, subjects were imaged by both conventional instruments and adaptive optics (AO) ophthalmoscopy. To improve the visibility of RPE cells in conventional ICG images, we demonstrate both a hardware approach using a custom lens add-on and an AI-based approach using a stratified cycleGAN network.
RESULTS: We observe similar fluorescent mosaic patterns arising from labeled RPE cells on both conventional and AO images, suggesting that cellular-level imaging of RPE may be obtainable using conventional imaging, albeit at lower resolution. Results show that higher resolution ICG RPE images of both healthy and diseased eyes can be obtained from conventional images using AI with a potential 220-fold improvement in time.
CONCLUSIONS: The application of using AI as an add-on module for existing instrumentation is an important step towards routine screening and detection of disease at earlier stages.
METHODS: Following intravenous injection of indocyanine green (ICG) dye, subjects were imaged by both conventional instruments and adaptive optics (AO) ophthalmoscopy. To improve the visibility of RPE cells in conventional ICG images, we demonstrate both a hardware approach using a custom lens add-on and an AI-based approach using a stratified cycleGAN network.
RESULTS: We observe similar fluorescent mosaic patterns arising from labeled RPE cells on both conventional and AO images, suggesting that cellular-level imaging of RPE may be obtainable using conventional imaging, albeit at lower resolution. Results show that higher resolution ICG RPE images of both healthy and diseased eyes can be obtained from conventional images using AI with a potential 220-fold improvement in time.
CONCLUSIONS: The application of using AI as an add-on module for existing instrumentation is an important step towards routine screening and detection of disease at earlier stages.