網膜観察技術を強化する新手法を開発(NIH researchers supercharge ordinary clinical device to get a better look at the back of the eye)

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2025-04-23 アメリカ国立衛生研究所 (NIH)

米国国立衛生研究所(NIH)の研究チームは、人工知能(AI)を活用して、従来の眼科診療で使用される走査型レーザー検眼鏡(SLO)の画像を高精細化する技術を開発しました。このAIシステムは、先進的な適応光学技術で取得された1,400枚以上の網膜画像を学習し、標準的なSLO画像の解像度を8倍に向上させました。特に、視細胞を支える網膜色素上皮(RPE)細胞の可視化が可能となり、加齢黄斑変性症やスターガルト病などの早期診断や治療効果のモニタリングに貢献します。この技術は、インドシアニングリーン(ICG)という造影剤を用いることで、通常の眼科診療機器で短時間に高品質な画像を取得でき、専門的な設備や高度な技術を必要としないため、一般の眼科クリニックでも導入可能です。この研究成果は、視覚障害の早期発見と個別化医療の推進に寄与することが期待されます。

​<関連情報>

人工知能が支援する臨床蛍光イメージングにより、補償光学眼底鏡検査に匹敵する生体内細胞解像度が達成された Artificial intelligence assisted clinical fluorescence imaging achieves in vivo cellular resolution comparable to adaptive optics ophthalmoscopy

Joanne Li,Jianfei Liu,Vineeta Das,Hong Le,Nancy Aguilera,Andrew J. Bower,John P. Giannini,Rongwen Lu,Sarah Abouassali,Emily Y. Chew,Brian P. Brooks,Wadih M. Zein,Laryssa A. Huryn,Andrei Volkov,Tao Liu & Johnny Tam
Communications Medicine  Published:23 April 2025
DOI:https://doi.org/10.1038/s43856-025-00803-z

網膜観察技術を強化する新手法を開発(NIH researchers supercharge ordinary clinical device to get a better look at the back of the eye)

Abstract

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.

Plain language summary

Advanced imaging methods that allow single cells to be seen in the living human eyes are not always available to ophthalmologists working in the clinic. We combined artificial intelligence (AI) with standard clinical imaging to visualize cells within the retina, a part of the eye, that are critical for maintaining vision and having healthy eyes. We found the cells could be seen using a commonly used fluorescent dye and standard clinical imaging equipment augmented with AI. The resulting AI images are comparable to those acquired using advanced imaging technology. By making cellular-level information more readily available in routine clinical practice, it may be possible to detect diseases earlier.

医療・健康
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