ArticleQuantitative imaging in medicine and surgery2025
Development and validation of the Artificial Intelligence-Proliferative Vitreoretinopathy (AI-PVR) Insight system for deep learning-based diagnosis and postoperative risk prediction in proliferative vitreoretinopathy using multimodal fundus imaging.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Deep learning-based classification of multiple fundus diseases using ultra-widefield images.Frontiers in cell and developmental biology · 2025Article
- Knowledge-enhanced AI drives diagnosis of multiple retinal diseases in fundus fluorescein angiography.Frontiers in cell and developmental biology · 2025Article
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9 authors.
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Abstract
Background: Early diagnosis of proliferative vitreoretinopathy (PVR) is crucial for preventing vision loss and ensuring effective treatment of retinal detachment. This study developed and validated a system named Artificial Intelligence-Proliferative Vitreoretinopathy (AI-PVR) Insight, which aims to automate the identification, grading, and postoperative risk assessment of PVR. Methods: We retrospectively collected data from 1,700 eyes of 1,700 patients who underwent vitrectomy at the Eye Hospital affiliated with Nanchang University and at Jiangxi Provincial People's Hospital from June 2015 to December 2023 for the development and validation of the AI-PVR Insight system performance. This system is based on two deep learning models, TwinsSVT and DenseNet-121, which extract features from three modalities: B-scan ultrasound (B-scan), optical coherence tomography (OCT), and ultra-widefield (UWF) retinal imaging. After principal component analysis (PCA) dimension reduction and feature fusion, multi-layer perceptron (MLP) or support vector machine (SVM) classifiers are used to identify PVR, assess severity, and predict postoperative risks. The performance of the system was evaluated by calculating area under the curve (AUC) values, accuracy, precision, recall, and F1 scores. Results: The AI-PVR Insight system demonstrated exceptional performance in PVR identification and severity grading, with AUC values exceeding 0.957 (0.902, 1.000) on internal and external test sets, respectively. For predicting postoperative PVR risk, the system achieved AUC values above 0.827 (0.737, 0.916) on both test sets, respectively. Conclusions: The AI-PVR Insight system has successfully achieved automatic identification, grading, and postoperative risk assessment of PVR, providing clinical physicians with support in formulating more targeted treatment plans and delivering critical insights for the effective prevention and management of PVR progression.
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