A Clinically Guided Approach for Training Deep Neural Networks for Myopic Maculopathy Classification

Pathologic myopia (PM) is a sight-threatening disease characterised by abnormal ocular changes due to excessive axial elongation in myopes. One important clinical manifestation of PM is myopic maculopathy (MM), which is categorised into 5 ordinal classes based on the established META-PM classification framework. This paper details a robust deep learning approach to automatically classifying MM from colour fundus photographs as part of the recently held Myopic Maculopathy Analysis Challenge (MMAC). A ResNet-18 model pretrained on ImageNet-1K was trained for the task. Pertinent MM lesions (patchy or macular atrophy) were manually segmented in images from the MMAC dataset and another publicly available dataset (PALM) to create a collection of lesion masks based on which an additional 250 images with severe MM were synthesised to mitigate class imbalance in the original training set. The image synthesis pipeline was guided by clinical domain knowledge: (1) synthesised macular atrophy tended to be circular with a regressed fibrovascular membrane near its centre, while patchy atrophy was more irregular and varied more greatly in size; (2) synthesised images were created using images with diffuse or patchy atrophy as background; and (3) synthesised images included examples that were not easily classifiable (e.g. creating patchy lesions that were in close proximity to the fovea). This, coupled with mix-up augmentation and ensemble prediction via test-time augmentation, enabled the model to rank first in the validation phase and fifth in the test phase. The source code is freely available at https://github.com/fyii200/MyopicMaculopathyClassification

ARA-net: an attention-aware retinal atrophy segmentation network coping with fundus images

BackgroundAccurately detecting and segmenting areas of retinal atrophy are paramount for early medical intervention in pathological myopia (PM). However, segmenting retinal atrophic areas based on a two-dimensional (2D) fundus image poses several challenges, such as blurred boundaries, irregular shapes, and size variation. To overcome these challenges, we have proposed an attention-aware retinal atrophy segmentation network (ARA-Net) to segment retinal atrophy areas from the 2D fundus image.MethodsIn particular, the ARA-Net adopts a similar strategy as UNet to perform the area segmentation. Skip self-attention connection (SSA) block, comprising a shortcut and a parallel polarized self-attention (PPSA) block, has been proposed to deal with the challenges of blurred boundaries and irregular shapes of the retinal atrophic region. Further, we have proposed a multi-scale feature flow (MSFF) to challenge the size variation. We have added the flow between the SSA connection blocks, allowing for capturing considerable semantic information to detect retinal atrophy in various area sizes.ResultsThe proposed method has been validated on the Pathological Myopia (PALM) dataset. Experimental results demonstrate that our method yields a high dice coefficient (DICE) of 84.26%, Jaccard index (JAC) of 72.80%, and F1-score of 84.57%, which outperforms other methods significantly.ConclusionOur results have demonstrated that ARA-Net is an effective and efficient approach for retinal atrophic area segmentation in PM

Artificial intelligence-aided diagnosis and treatment in the field of optometry

With the rapid development of computer technology, the application of artificial intelligence (AI) to ophthalmology has gained prominence in modern medicine. As modern optometry is closely related to ophthalmology, AI research on optometry has also increased. This review summarizes current AI research and technologies used for diagnosis in optometry, related to myopia, strabismus, amblyopia, optical glasses, contact lenses, and other aspects. The aim is to identify mature AI models that are suitable for research on optometry and potential algorithms that may be used in future clinical practice