Alzheimer's Together with Mild Cognitive Impairment Screening Using Polar Transformation of Middle Zone of Fundus Images Based Deep Learning.

Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) are considered an increasing major health problem in elderlies. However, current clinical methods of Alzheimer's detection are expensive and difficult to access, making the detection inconvenient and unsuitable for developing countries such as Thailand. Thus, we developed a method of AD together with MCI screening by fine-tuning a pre-trained Densely Connected Convolutional Network (DenseNet-121) model using the middle zone of polar transformed fundus image. The polar transformation in the middle zone of the fundus is a key factor helping the model to extract features more effectively and that enhances the model accuracy. The dataset was divided into 2 groups: normal and abnormal (AD and MCI). This method can classify between normal and abnormal patients with 96% accuracy, 99% sensitivity, 90% specificity, 95% precision, and 97% F1 score. Parts of both MCI and AD input images that most impact the classification score visualized by Grad-CAM++ focus in superior and inferior retinal quadrants.Clinical relevance- The parts of both MCI and AD input images that have the most impact the classification score (visualized by Grad-CAM++) are superior and inferior retinal quadrants. Polar transformation of the middle zone of retinal fundus images is a key factor that enhances the classification accuracy.

3-LbNets: Tri-Labeling Deep Convolutional Neural Network for the Automated Screening of Glaucoma, Glaucoma Suspect, and No Glaucoma in Fundus Images.

Early detection of glaucoma, a widespread visual disease, can prevent vision loss. Unfortunately, ophthalmologists are scarce and clinical diagnosis requires much time and cost. Therefore, we developed a screening Tri-Labeling deep convolutional neural network (3-LbNets) to identify no glaucoma, glaucoma suspect, and glaucoma cases in global fundus images. 3-LbNets extracts important features from 3 different labeling modals and puts them into an artificial neural network (ANN) to find the final result. The method was effective, with an AUC of 98.66% for no glaucoma, 97.54% for glaucoma suspect, and 97.19% for glaucoma when analysing 206 fundus images evaluated with unanimous agreement from 3 well-trained ophthalmologists (3/3). When analysing 178 difficult to interpret fundus images (with majority agreement (2/3)), this method had an AUC of 80.80% for no glaucoma, 69.52% for glaucoma suspect, and 82.74% for glaucoma cases.Clinical relevance-This establishes a robust global fundus image screening network based on the ensemble method that can optimize glaucoma screening to alleviate the toll on those with glaucoma and prevent glaucoma suspects from developing the disease.

Donut: Augmentation Technique for Enhancing The Efficacy of Glaucoma Suspect Screening.

Glaucoma is the second most common cause of blindness. A glaucoma suspect has risk factors that increase the possibility of developing glaucoma. Evaluating a patient with suspected glaucoma is challenging. The "donut method" was developed in this study as an augmentation technique for obtaining high-quality fundus images for training ConvNeXt-Small model. Fundus images from GlauCUTU-DATA, labelled by randomizing at least 3 well-trained ophthalmologists (4 well-trained ophthalmologists in case of no majority agreement) with a unanimous agreement (3/3) and majority agreement (2/3), were used in the experiment. The experimental results from the proposed method showed the training model with the "donut method" increased the sensitivity of glaucoma suspects from 52.94% to 70.59% for the 3/3 data and increased the sensitivity of glaucoma suspects from 37.78% to 42.22% for the 2/3 data. This method enhanced the efficacy of classifying glaucoma suspects in both equalizing sensitivity and specificity sufficiently. Furthermore, three well-trained ophthalmologists agreed that the GradCAM++ heatmaps obtained from the training model using the proposed method highlighted the clinical criteria.Clinical relevance- The donut method for augmentation fundus images focuses on the optic nerve head region for enhancing efficacy of glaucoma suspect screening, and uses Grad-CAM++ to highlight the clinical criteria.