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Artificial Intelligence for Precision Medicine
Published in Kamal Kumar Sharma, Akhil Gupta, Bandana Sharma, Suman Lata Tripathi, Intelligent Communication and Automation Systems, 2021
Fundus photography, typically examined and assessed by ophthalmologists, is a non-invasive technique to capture images of the optic disc, the retina or the macula and discover or track eye diseases, for instance, age-related macular degeneration (AMD), glaucoma and diabetic retinopathy (DR). For instance, a DL algorithm was developed to find DR using 128 175 retinal images and reached analogous functioning to ophthalmologists in datasets of two independent examinations. Deep convolutional neural networks have been used to find glaucoma and instantly rank AMD using fundus photos, and the process reached a precision close to that of professional ophthalmologists [12].
Computer Aided Diagnosis System for Early Detection of Diabetic Retinopathy Using OCT Images
Published in Ayman El-Baz, Jasjit S. Suri, Big Data in Multimodal Medical Imaging, 2019
Ahmed ElTanboly, Ahmed Shalaby, Ali Mahmoud, Mohammed Ghazal, Andrew Switala, Fatma Taher, Jasjit S. Suri, Robert Keynton, Ayman El-Baz
Most CAD systems for early DR detection being introduced in the literature have been proposed from fundus images. Fundus photography uses the same concept of the indirect ophthalmoscope for a wide view of the retina. One of the reasons fundus pictures are more common in CAD systems is that they can give a good presentation of systemic diseases. However, one of its crucial drawbacks is it gives pictures in 2D with no appreciation for depth. To the best of our knowledge, there are no CAD systems in the literature that aim at early detection of DR using OCT scans, and we are the first group proposing such a CAD system.
A fully automated pipeline of extracting biomarkers to quantify vascular changes in retina-related diseases
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2019
Jiong Zhang, Behdad Dashtbozorg, Fan Huang, Tao Tan, B. M. ter Haar Romeny
Many ocular and systemic diseases including T2DM and DR can cause geometrical or pathological changes in the retina. A special property of the retina is that it is one of the only places in the human body where the vascular system can be directly observed. Clinical examination of the retina can be achieved via different techniques, in which the retinal imaging through fundus photography provides a non-invasive way to ophthalmologists for investigating different eye-related and systemic diseases (Abra`moff et al. 2010) including DR, age-related macular degeneration and glaucoma (Wong et al. 2008; Lim et al. 2012; Amerasinghe et al. 2008). The advantage of retinal imaging is that it provides direct access to the vascular abnormalities and enables further quantitative analysis of the retinal vasculature.
Computation of retinal fundus parameters for stroke prediction
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2020
Jeena Raveendran Susha, Sukeshkumar A, Mahadevan K
Over the last decade, advances in retinal imaging techniques suggest that changes or abnormalities found in human retinal vasculature have a strong correlation with various systemic conditions like hypertension, diabetes and cardiovascular diseases. Hence, regular ocular examination is significant for the detection of retinal vascular abnormalities to identify people at high risk of these diseases for early and in-time treatments. Fundus photography still remains an important imaging tool as it allows non-invasive monitoring of disease progression (Yanuzzi et al. 2004) over time.
Deep CNN based microaneurysm-haemorrhage classification in retinal images considering local neighbourhoods
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
Mahua Nandy Pal, Ankit Sarkar, Anindya Gupta, Minakshi Banerjee
Nayak et al. (2008) and Acharya et al. (2008) deal with different stages of diabetic retinopathy. Nayak et al. classified images of DR (normal, proliferative and non-proliferative) with the artificial neural network using 140 images graded by ophthalmologists. They reported image classification accuracy of 93%, sensitivity of 90% and specificity of 100%. Acharya et al. classified DR images (no DR, mild DR, moderate DR, severe DR and proliferative DR) with an accuracy of 82% and specificity of 88%. Pratt et al. also used CNN architecture with a high-end GPU on a big data set of 80,000 images and demonstrated 75% validation accuracy. Lam et al. (2018a)used transfer learning with pre-trained GoogLeNet and AlexNet models from ImageNet. They reported image level classification accuracies of 74.5%, 68.8%, and 57.2% in 2-class, 3-class and 4-class classification for the detection of diabetic retinopathy affected images. Pratt et al. (Pratt, et al., 2016b)also reported image-level sensitivity of 95% and accuracy of 75% on 5,000 validation images. They resized images to 512 × 512 resolution. Model training time complexity is quite high. Tavakoli et al. (2013) evaluated their work on a different modality, fluorescein angiography images. In fluorescein angiography, a dye is injected into the vein and images of the back surface of the eye are captured as the dye propagates through the vessels. This procedure facilitates the diagnosis of abnormalities in the vessels. However, in fundus photography, a special camera is used to capture the image of the back surface of the eye. They provided Image level DR detection sensitivities with two rural databases and ROC online database. Orlando et al. (2018) provided the ROC curve and AUC values for image-level evaluation with random forest classifier using both deep learned and handcrafted features. Sun (2019) is another DR image diagnostic system. All these works address image level retinal classification.