The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey.

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications

Visual Impairment and Blindness

Blindness and vision impairment affect at least 2.2 billion people worldwide with most individuals having a preventable vision impairment. The majority of people with vision impairment are older than 50 years, however, vision loss can affect people of all ages. Reduced eyesight can have major and long-lasting effects on all aspects of life, including daily personal activities, interacting with the community, school and work opportunities, and the ability to access public services. This book provides an overview of the effects of blindness and visual impairment in the context of the most common causes of blindness in older adults as well as children, including retinal disorders, cataracts, glaucoma, and macular or corneal degeneration

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Hierarchical, informed and robust machine learning for surgical tool management

This thesis focuses on the development of a computer vision and deep learning based system for the intelligent management of surgical tools. The work accomplished included the development of a new dataset, creation of state of the art techniques to cope with volume, variety and vision problems, and designing or adapting algorithms to address specific surgical tool recognition issues. The system was trained to cope with a wide variety of tools, with very subtle differences in shapes, and was designed to work with high volumes, as well as varying illuminations and backgrounds. Methodology that was adopted in this thesis included the creation of a surgical tool image dataset and development of a surgical tool attribute matrix or knowledge-base. This was significant because there are no large scale publicly available surgical tool datasets, nor are there established annotations or datasets of textual descriptions of surgical tools that can be used for machine learning. The work resulted in the development of a new hierarchical architecture for multi-level predictions at surgical speciality, pack, set and tool level. Additional work evaluated the use of synthetic data to improve robustness of the CNN, and the infusion of knowledge to improve predictive performance

Biomedical Engineering

Biomedical engineering is currently relatively wide scientific area which has been constantly bringing innovations with an objective to support and improve all areas of medicine such as therapy, diagnostics and rehabilitation. It holds a strong position also in natural and biological sciences. In the terms of application, biomedical engineering is present at almost all technical universities where some of them are targeted for the research and development in this area. The presented book brings chosen outputs and results of research and development tasks, often supported by important world or European framework programs or grant agencies. The knowledge and findings from the area of biomaterials, bioelectronics, bioinformatics, biomedical devices and tools or computer support in the processes of diagnostics and therapy are defined in a way that they bring both basic information to a reader and also specific outputs with a possible further use in research and development

Multimodal imaging in age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness and affects approximately one in seven Australians aged 50 years and above. Currently, this complex condition is not easily and uniformly assessed. The signs of AMD differ between eyes and also occur in other macular disorders. This hinders accurate diagnosis and classification, which is fundamental to optimal patient care. Ocular imaging and visual function assessment have the potential to minimise the devastating consequences of disease through early detection. However, multiple devices are now commercially available and the impact of these technologies in clinical practice may not be straightforward. For instance, their usefulness may depend on accessibility and the operator’s knowledge and clinical skills. The impact on patient management, as well as alternative models of eye-care delivery, requires clarification. This thesis aims to explore the current and potential utility of imaging technologies (optical coherence tomography, infrared imaging, monochromatic retinal photography and fundus autofluorescence) in the assessment and management of AMD and other diseases of retinal pigment epithelium dysfunction. The findings show that optometrists self-describe high levels of practice competency and make ready use of imaging in everyday practice. However, they also unwittingly demonstrated low awareness of the evidence base in AMD. Furthermore, when their interpretation of images was tested using a series of case vignettes, their diagnostic accuracy as a group improved by only five per cent (from 61 per cent to 66 per cent); their tendency to refer increased by four per cent. These factors might be improved through education. A series of open-access, chair-side reference charts were consequently devised to help optometrists use imaging technologies more effectively in clinical practice. The additive contribution of multimodal structural and functional testing was particularly emphasised. Finally, a novel model of intermediate-tier eye-care in Australia was shown to substantially reduce the number of false positive cases or cases without a specific diagnosis. Interestingly, this model was acclaimed by reviewers as “scoring highly for originality and of international relevance”. Most excitingly, the thesis concludes with future directions regarding collaborative care and multimodal imaging, where detection of disease might be facilitated via a computational approach