Recent Developments in Detection of Central Serous Retinopathy through Imaging and Artificial Intelligence Techniques – A Review

Central Serous Retinopathy (CSR) or Central Serous Chorioretinopathy (CSC) is a significant disease that causes blindness and vision loss among millions of people worldwide. It transpires as a result of accumulation of watery fluids behind the retina. Therefore, detection of CSR at early stages allows preventive measures to avert any impairment to the human eye. Traditionally, several manual methods for detecting CSR have been developed in the past; however, they have shown to be imprecise and unreliable. Consequently, Artificial Intelligence (AI) services in the medical field, including automated CSR detection, are now possible to detect and cure this disease. This review assessed a variety of innovative technologies and researches that contribute to the automatic detection of CSR. In this review, various CSR disease detection techniques, broadly classified into two categories: a) CSR detection based on classical imaging technologies, and b) CSR detection based on Machine/Deep Learning methods, have been reviewed after an elaborated evaluation of 29 different relevant articles. Additionally, it also goes over the advantages, drawbacks and limitations of a variety of traditional imaging techniques, such as Optical Coherence Tomography Angiography (OCTA), Fundus Imaging and more recent approaches that utilize Artificial Intelligence techniques. Finally, it is concluded that the most recent Deep Learning (DL) classifiers deliver accurate, fast, and reliable CSR detection. However, more research needs to be conducted on publicly available datasets to improve computation complexity for the reliable detection and diagnosis of CSR disease

Automatic segmentation and classification methods using optical coherence tomography angiography (Octa): A review and handbook

Optical coherence tomography angiography (OCTA) is a promising technology for the non-invasive imaging of vasculature. Many studies in literature present automated algorithms to quantify OCTA images, but there is a lack of a review on the most common methods and their comparison considering multiple clinical applications (e.g., ophthalmology and dermatology). Here, we aim to provide readers with a useful review and handbook for automatic segmentation and classification methods using OCTA images, presenting a comparison of techniques found in the literature based on the adopted segmentation or classification method and on the clinical application. Another goal of this study is to provide insight into the direction of research in automated OCTA image analysis, especially in the current era of deep learning

An in vivo investigation of choroidal vasculature in age-related macular degeneration

Age-related macular degeneration (AMD) is the leading cause of visual impairment in the developed world. Whilst the pathogenesis is complex and not fully understood, changes to the choroidal vasculature in AMD have been demonstrated using histology. Advances in imaging technology, particularly long-wavelength optical coherence tomography (OCT), allow in vivo visualisation and investigation of this structure. The aim of this work is to determine whether changes to the choroidal vasculature are detectable in AMD using in vivo imaging. This was achieved through the evaluation of parameters for quantifying the structure, and the application of a machine learning approach to automated disease severity classification, based on choroidal appearance. Participants with early AMD (n=25), neovascular AMD (nAMD; n=25), and healthy controls (n=25) underwent imaging with a non-commercial long-wavelength (λc=1040 nm) OCT device. Subfoveal choroidal thickness, choroidal area, and luminal area were significantly lower in the nAMD group than the healthy and early AMD groups, whilst vessel ratio was significantly greater (P<0.05 in all cases). There was no significant difference in visible vessel diameter, choroidal vascularity index, luminal area ratio, or luminal perimeter ratio between the groups. No significant differences were found between the healthy and early AMD groups for any of the eight vascular parameters assessed. Classification of the disease groups based on choroidal OCT images was demonstrated using machine learning techniques. Textural features within the images were extracted using Gabor filters, and K-nearest neighbour, support vector machine, and random forest classifiers were assessed for this classification task. Textural changes were most pronounced in late-stage disease, although attribution to pathology or pharmacological intervention (anti-VEGF treatment) was not possible. Changes were also discernible in the early AMD group, suggesting sensitivity of this approach to detecting vascular involvement in early disease. In conclusion, structural changes to the choroidal vasculature in AMD are detectable in vivo using OCT imaging, demonstrated with both manual and automated analysis techniques. Whilst changes were most prominent in late-stage disease, subtle structural changes in early AMD were identified with texture analysis, warranting further investigation to improve our understanding of choroidal involvement in the pathogenesis of early AMD

Real-time OCT feedback-controlled RPE photodisruption in ex vivo porcine eyes using 8 microsecond laser pulses

Selective retinal pigment epithelium (RPE) photodisruption requires reliable real-time feedback dosimetry (RFD) to prevent unwanted overexposure. In this study, optical coherence tomography (OCT) based RFD was investigated in ex vivo porcine eyes exposed to laser pulses of 8 µs duration (wavelength: 532 nm, exposure area: 90 × 90 µm2, radiant exposure: 247 to 1975 mJ/µm2). For RFD, fringe washouts in time-resolved OCT M-scans (central wavelength: 870 nm, scan rate: 85 kHz) were compared to an RPE cell viability assay. Statistical analysis revealed a moderate correlation between RPE lesion size and applied treatment energy, suggesting RFD adaptation to inter- and intraindividual RPE pigmentation and ocular transmission

Characterising pattern asymmetry in pigmented skin lesions

Abstract. In clinical diagnosis of pigmented skin lesions asymmetric pigmentation is often indicative of melanoma. This paper describes a method and measures for characterizing lesion symmetry. The estimate of mirror symmetry is computed first for a number of axes at different degrees of rotation with respect to the lesion centre. The statistics of these estimates are the used to assess the overall symmetry. The method is applied to three different lesion representations showing the overall pigmentation, the pigmentation pattern, and the pattern of dermal melanin. The best measure is a 100% sensitive and 96% specific indicator of melanoma on a test set of 33 lesions, with a separate training set consisting of 66 lesions

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research