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Updated: Feb 10, 2026

In Vivo Multimodal Imaging and Analysis of Mouse Laser-Induced Choroidal Neovascularization Model

In Vivo Multimodal Imaging and Analysis of Mouse Laser-Induced Choroidal Neovascularization Model

Published on: January 21, 2018

強度近視における近視性脈絡膜新生血管の3年および5年リスク予測のためのマルチモーダルAI

Yining Wang¹, Takashi Ishida², Ziye Wang¹

¹Department of Ophthalmology and Visual Science, Institute of Science Tokyo, Tokyo, Japan.

Ophthalmology. Retina

|February 8, 2026

要旨

Purpose:

To develop artificial intelligence (AI)-based models for estimating the risk of myopic macular neovascularization (MNV) in highly myopic eyes.

Design:

Retrospective, single-center cohort study.

Participants:

A total of 4235 eyes from 2501 patients with high myopia who were enrolled between October 2011 and May 2021 at the Institute of Science Tokyo.

Methods:

Baseline fundus photographs and seven clinical variables were used for model development and validation. Three image-based deep learning (DL) models with a DenseNet-121 backbone were trained to predict the risk of MNV within 1, 3, and 5 years after baseline. A multimodal survival model based on the DeepSurv framework was further developed by integrating image-derived features with clinical variables to estimate time-to-event risk. Model performance was evaluated using discrimination, calibration, and clinical utility metrics. Model interpretability was explored using gradient-weighted class activation mapping and Shapley additive explanation (SHAP) analyses.

Main Outcome Measures:

Area under the receiver operating characteristic curve (AUROC) for the DL models and concordance index (C-index) for the multimodal DeepSurv model.

Results:

The DL models achieved AUROCs of 0.785 (95% CI, 0.474-0.988), 0.798 (95% CI, 0.687-0.900), and 0.729 (95% CI, 0.614-0.832) for the 1-, 3-, and 5-year predictions, respectively. The multimodal DeepSurv model achieved a C-index of 0.683 (95% CI, 0.582-0.775) and enabled stratification of eyes into subgroups with distinct long-term probabilities of MNV development. Heatmaps showed activation in clinically relevant regions, and SHAP analysis indicated that axial length and pathologic myopia category showed prominent conditional contributions within the multimodal model.

Conclusions:

AI-based models enabled fixed-horizon risk estimation and longitudinal time-to-event risk assessment for MNV in highly myopic eyes, providing clinically meaningful risk stratification. With further external validation, this approach may support risk-adapted long-term monitoring strategies in high myopia.

キーワード:

強度近視近視性黄斑新生血管リスク予測人工知能深層学習マルチモーダル脈絡膜新生血管眼科

さらに関連する動画

A Mouse Model for Laser-induced Choroidal Neovascularization

A Mouse Model for Laser-induced Choroidal Neovascularization

Published on: December 27, 2015

Scleral Cross-linking Using Riboflavin and Ultraviolet-A Radiation for Prevention of Axial Myopia in a Rabbit Model

Scleral Cross-linking Using Riboflavin and Ultraviolet-A Radiation for Prevention of Axial Myopia in a Rabbit Model

Published on: April 3, 2016

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