A New Deep Learning Algorithm with Activation Mapping for Diabetic Retinopathy: Backtesting after 10 Years of Tele-Ophthalmology

We report the development of a deep learning algorithm (AI) to detect signs of diabetic retinopathy (DR) from fundus images. For this, we use a ResNet-50 neural network with a double resolution, the addition of Squeeze–Excitation blocks, pre-trained in ImageNet, and trained for 50 epochs using the A...

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Bibliographic Details
Published inJournal of clinical medicine Vol. 11; no. 17; p. 4945
Main Authors Pareja-Ríos, Alicia, Ceruso, Sabato, Romero-Aroca, Pedro, Bonaque-González, Sergio
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 23.08.2022
MDPI
Subjects
Algorithms
Artificial intelligence
Cost control
Datasets
Deep learning
Diabetes
Diabetic retinopathy
FDA approval
Mathematical functions
Medical personnel
Neural networks
Ophthalmology
Patients
Software
Canary Islands
Europe
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Summary:We report the development of a deep learning algorithm (AI) to detect signs of diabetic retinopathy (DR) from fundus images. For this, we use a ResNet-50 neural network with a double resolution, the addition of Squeeze–Excitation blocks, pre-trained in ImageNet, and trained for 50 epochs using the Adam optimizer. The AI-based algorithm not only classifies an image as pathological or not but also detects and highlights those signs that allow DR to be identified. For development, we have used a database of about half a million images classified in a real clinical environment by family doctors (FDs), ophthalmologists, or both. The AI was able to detect more than 95% of cases worse than mild DR and had 70% fewer misclassifications of healthy cases than FDs. In addition, the AI was able to detect DR signs in 1258 patients before they were detected by FDs, representing 7.9% of the total number of DR patients detected by the FDs. These results suggest that AI is at least comparable to the evaluation of FDs. We suggest that it may be useful to use signaling tools such as an aid to diagnosis rather than an AI as a stand-alone tool.
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ISSN:2077-0383
2077-0383
DOI:10.3390/jcm11174945