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Automated Assessment of OCT Angiography Image Quality Using the Artificial Intelligence Ready and Exploratory Atlas
Jimmy S Chen1,2,3,4, Lauren E Wedekind4,5,6, Akshara Legala4,5
1Harvard Retinal Imaging Lab, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Objective:
OCT angiography (OCTA) images present challenges for clinical and research use due to variability and noise. The aim was to develop artificial intelligence models for OCTA image quality evaluation using the Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) data set.
Design:
Cross-sectional study.
Subjects:
Six thousand two hundred sixty-nine OCTA 6 × 6-mm and 325 12 × 12-mm macula-centered photographs of the superficial vascular plexus from 1067 AI-READI study participants, and 1100 6 × 6-mm OCTA photographs from 539 patients from the Massachusetts Eye and Ear Infirmary (MEEI).
Methods:
All photographs were labeled as acceptable or poor quality by two ophthalmologists and five medical students. Predefined training and validation sets were used to fine-tune four deep learning (DL) models (ResNet, EfficientNet, Vision Transformer [ViT], and ConvNeXt v2) via hyperparameter grid search.
Main Outcome Measures:
Model performance on all photographs from the AI-READI and MEEI test sets were assessed by accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) scores.
Results:
The accuracy/AUROC of the fine-tuned ResNet, EfficientNet, ViT, and ConvNeXt v2 models on the 6 × 6-mm AI-READI test set were 83.5%/0.904, 82.5%/0.904, 83.5%/0.908%, and 82.9%/0.913, with sensitivities/specificities of 77.9%/86.1%, 73.4%/89.9%, 69.2%/92.7%, and 79.8%/84.2%, respectively. The accuracy/AUROC of these models on the 6 × 6-mm MEEI test set were 91.5%/0.925, 94.3%/0.974, 93.9%/0.974%, and 88.0%/0.957, with sensitivities/specificities of 73.2%/98.4%, 84.5%/97.3%, 86.8%/97.2%, and 84.4%/92.7%, respectively. The accuracies/AUROC for the 12 × 12-mm photographs were 72.3%/0.775, 76.4%/0.775, 66.2%/0.979%, and 83.7%/0.834, with sensitivities/specificities of 95.7%/54.5%, 95.7%/57.8%, 97.9%/42.2%, and 96.4%/74.1%, respectively.
Conclusions:
The AI-READI data set contains heterogeneous data usable for high-performing DL models for OCTA image quality assessment. These models were generalizable across institutions, cameras, and image sizes and may be used to rapidly screen OCTA image quality for use in future clinical studies.
Financial Disclosures:
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.