Automated Identification of Referable Retinal Pathology in Teleophthalmology Setting
| Autor: | Qitong Gao, Scott W. Cousins, Majda Hadziahmetovic, Miroslav Pajic, Joshua Amason |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
medicine.medical_specialty automated diagnosis genetic structures teleophthalmology Biomedical Engineering Teleophthalmology Retina Article 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Text mining Optical coherence tomography image analysis Ophthalmology medicine Medical imaging Humans Retinal pathology Retrospective Studies retinal pathology Modality (human–computer interaction) medicine.diagnostic_test Receiver operating characteristic business.industry deep learning Retinal Telemedicine 030104 developmental biology chemistry 030221 ophthalmology & optometry business Tomography Optical Coherence |
| Zdroj: | Translational Vision Science & Technology |
| ISSN: | 2164-2591 |
| DOI: | 10.1167/tvst.10.6.30 |
| Popis: | Purpose This study aims to meet a growing need for a fully automated, learning-based interpretation tool for retinal images obtained remotely (e.g. teleophthalmology) through different imaging modalities that may include imperfect (uninterpretable) images. Methods A retrospective study of 1148 optical coherence tomography (OCT) and color fundus photography (CFP) retinal images obtained using Topcon's Maestro care unit on 647 patients with diabetes. To identify retinal pathology, a Convolutional Neural Network (CNN) with dual-modal inputs (i.e. CFP and OCT images) was developed. We developed a novel alternate gradient descent algorithm to train the CNN, which allows for the use of uninterpretable CFP/OCT images (i.e. ungradable images that do not contain sufficient image biomarkers for the reviewer to conclude absence or presence of retinal pathology). Specifically, a 9:1 ratio to split the training and testing dataset was used for training and validating the CNN. Paired CFP/OCT inputs (obtained from a single eye of a patient) were grouped as retinal pathology negative (RPN; 924 images) in the absence of retinal pathology in both imaging modalities, or if one of the imaging modalities was uninterpretable and the other without retinal pathology. If any imaging modality exhibited referable retinal pathology, the corresponding CFP/OCT inputs were deemed retinal pathology positive (RPP; 224 images) if any imaging modality exhibited referable retinal pathology. Results Our approach achieved 88.60% (95% confidence interval [CI] = 82.76% to 94.43%) accuracy in identifying pathology, along with the false negative rate (FNR) of 12.28% (95% CI = 6.26% to 18.31%), recall (sensitivity) of 87.72% (95% CI = 81.69% to 93.74%), specificity of 89.47% (95% CI = 83.84% to 95.11%), and area under the curve of receiver operating characteristic (AUC-ROC) was 92.74% (95% CI = 87.71% to 97.76%). Conclusions Our model can be successfully deployed in clinical practice to facilitate automated remote retinal pathology identification. Translational Relevance A fully automated tool for early diagnosis of retinal pathology might allow for earlier treatment and improved visual outcomes. |
| Databáze: | OpenAIRE |
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