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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. [ABSTRACT FROM AUTHOR]

The problem of reconstructing a depth map from a sequence of differently focused images (focal stack) is called Depth from focus. The core idea of this method is to analyze the sharpness of each pixel and compare it along the z axis of the focal stack to estimate the true depth value. This approach has two main drawbacks: it depends on the optics of the camera and on the focus measure operator. Recent advances in deep learning techniques show promising results in this way, however, still have problems generalizing to different scenes, cameras, optics and focal stack focus positions. In this paper we propose a novel deep learning based method to approach this problem. Firstly, we propose to estimate distances relative to the focal stack focus position instead of estimating true or invariant depths, allowing us to generalize different scenes and optical setups without losing the possibility to extract real distances. Secondly, we present our novel architecture: a 2D siamese encoder–3D decoder with a differentiable argmax regression that is able to compute depth from stacks of variable sizes. Finally, we compare our method with 2 other depth from focus algorithms and with a monocular depth estimation method. [Display omitted] • A novel deep learning based method able to extract depth maps from focal stacks with values relative to focus positions. • A model architecture independent from the number of planes of the focal stack. • A quantitative comparison along with a qualitative one with different cameras, lenses and focus configurations. [ABSTRACT FROM AUTHOR]