Angelica Ly

Infrared reflectance imaging in age‐related macular degeneration

The purpose of this article is to describe the appearance of age‐related macular degeneration (AMD) phenotypes using infrared (IR) reflectance imaging. IR reflectance imaging of the retina has the potential to highlight specific sub‐retinal features and pathology. However, its role in macular disease, specifically AMD, is often underestimated and requires clarification.

Fundus Autofluorescence in Age-related Macular Degeneration.

ABSTRACT Fundus autofluorescence (FAF) provides detailed insight into the health of the retinal pigment epithelium (RPE). This is highly valuable in age-related macular degeneration (AMD) as RPE damage is a hallmark of the disease. The purpose of this paper is to critically appraise current clinical descriptions regarding the appearance of AMD using FAF and to integrate these findings into a chair-side reference. A wide variety of FAF patterns have been described in AMD, which is consistent with the clinical heterogeneity of the disease. In particular, FAF imaging in early to intermediate AMD has the capacity to reveal RPE alterations in areas that appear normal on funduscopy, which aids in the stratification of cases and may have visually significant prognostic implications. It can assist in differential diagnoses and also represents a reliable, sensitive method for distinguishing reticular pseudodrusen. FAF is especially valuable in the detection, evaluation, and monitoring of geographic atrophy and has been used as an endpoint in clinical trials. In neovascular AMD, FAF reveals distinct patterns of classic choroidal neovascularization noninvasively and may be especially useful for determining which eyes are likely to benefit from therapeutic intervention. FAF represents a rapid, effective, noninvasive imaging method that has been underutilized, and incorporation into the routine assessment of AMD cases should be considered. However, the practicing clinician should also be aware of the limitations of the modality, such as in the detection of foveal involvement and in the distinction of phenotypes (hypo-autofluorescent drusen from small areas of geographic atrophy).

Pigmented Lesions of the Retinal Pigment Epithelium.

&NA;The primary eye care practitioner assumes an important role in clinical decisions involving the differentiation between malignant and nonmalignant pigmented lesions. A misdiagnosis may have profound consequences on patient management and visual or life prognosis. However, information on these lesions, particularly their appearance using advanced imaging, is fragmented throughout the literature. The purpose of this review is to describe these features in detail, so that the implications of this information on clinical practice are more readily apparent. Clinically relevant descriptions of pigmented lesions of the retinal pigment epithelium using traditional and advanced imaging modalities in the literature were collated and integrated with findings from patients seen at the Centre for Eye Health. The information was then organized and tabulated. Finally, a flow diagram was created to be used as a clinical reference in the differential diagnosis of pigmented lesions of the retinal pigment epithelium.

Collaborative care of non-urgent macular disease: a study of inter-optometric referrals

Diseases involving the macula and posterior pole are leading causes of visual impairment and blindness worldwide and may require prompt ophthalmological care. However, access to eye‐care and timely patient management may be limited due to inefficient and inappropriate referrals between primary eye‐care providers and ophthalmology. Optometrists with a special interest in macular disease may be useful as a community aid to better stratify and recommend best‐practice management plans for suitable patients. This study assesses such a notion by appraising the optometric referral patterns of patients with suspected macular disease to an intermediate‐tier optometric imaging clinic.

Advanced imaging for the diagnosis of age-related macular degeneration: a case vignettes study

The aim of this study is to evaluate the diagnosis, staging, imaging and management preferences, and the effect of advanced imaging among practising optometrists in age‐related macular degeneration (AMD).

Self-reported optometric practise patterns in age-related macular degeneration

The use of advanced imaging in clinical practice is emerging and the use of this technology by optometrists in assessing patients with age‐related macular degeneration is of interest. Therefore, this study explored contemporary, self‐reported patterns of practice regarding age‐related macular degeneration diagnosis and management using a cross‐sectional survey of optometrists in Australia and New Zealand.

Lesion detection in ultra-wide field retinal images for diabetic retinopathy diagnosis.

Diabetic retinopathy (DR) leads to irreversible vision loss. Diagnosis and staging of DR is usually based on the presence, number, location and type of retinal lesions. Ultra-wide field (UWF) digital scanning laser technology provides an opportunity for computer-aided DR lesion detection. High-resolution UWF images (3078×2702 pixels) may allow detection of more clinically relevant retinopathy in comparison with conventional retinal images as UWF imaging covers a 200° retinal area, versus 45° by conventional cameras. Current approaches to DR diagnosis that analyze 7-field Early Treatment Diabetic Retinopathy Study (ETDRS) retinal images provide similar results to UWF imaging. However, in 40% of cases, more retinopathy was found outside the 7- field ETDRS fields by UWF and in 10% of cases, retinopathy was reclassified as more severe. The reason is that UWF images examine both the central retina and more peripheral regions. We propose an algorithm for automatic detection and classification of DR lesions such as cotton wool spots, exudates, microaneurysms and haemorrhages in UWF images. The algorithm uses convolutional neural network (CNN) as a feature extractor and classifies the feature vectors extracted from colour-composite UWF images using a support vector machine (SVM). The main contribution includes detection of four types of DR lesions in the peripheral retina for diagnostic purposes. The evaluation dataset contains 146 UWF images. The proposed method for detection of DR lesion subtypes in UWF images using two scenarios for transfer learning achieved AUC ≈ 80%. Data was split at the patient level to validate the proposed algorithm.

Multispectral Pattern Recognition Reveals a Diversity of Clinical Signs in Intermediate Age-Related Macular Degeneration

Purpose To develop a proof-of-concept, computational method for the quantification and classification of fundus images in intermediate age-related macular degeneration (AMD). Methods Multispectral, unsupervised pattern recognition was applied to 184 fundus images from 10 normal and 36 intermediate AMD eyes. The imaging results of preprocessed, grayscale images from three modalities (infrared, green, and fundus autofluorescence scanning laser ophthalmoscopy) were automatically classified into various clusters sharing a common spectral signature, using a k-means clustering algorithm. Class separability was calculated by using transformed divergence (DT). The classification results for large drusen, pigmentary abnormalities, and areas unaffected by AMD were compared against three expert observers for concordance, and to calculate sensitivity and specificity. Results Multispectral, unsupervised pattern recognition successfully identified a finite number of AMD-specific, statistically separable signatures in eyes with intermediate AMD. By using a correct classification criterion of >83% for identical clusters and a total of 1693 expert annotations, the sensitivity and specificity of multispectral pattern recognition for the detection of AMD lesions was 74% and 98%, respectively. Large drusen and pigmentary abnormalities were correctly classified in 75% and 68% of instances, respectively. Conclusions We describe herein a novel approach for the classification of multispectral images in intermediate AMD. Automated classification of intermediate AMD, using multispectral pattern recognition, has moderate sensitivity and high specificity, when compared against clinical experts. The methods described may have a future role in AMD screening or monitoring.

Developing prognostic biomarkers in intermediate age-related macular degeneration: their clinical use in predicting progression: Prognostic biomarkers in AMD Ly, Yapp, Nivison-Smith et al.

Age‐related macular degeneration is a common, complex and blinding eye disease. When early and intermediate levels of severity are detected in one or both eyes, there is a wide‐ranging 0.4 to 53 per cent risk of progression to advanced disease in five years. In order to maximise visual outcomes for their patients, practising eye‐care professionals must be able to stratify patients according to their risk of progression, intervene (for example by recommending smoking cessation or nutritional supplements and Amsler grid self‐monitoring in intermediate disease) and monitor accordingly. With the aid of ocular imaging, a range of under‐recognised yet meaningful risk factors have been identified. The purpose of this review is to assist the eye‐care practitioner in stratifying the risk of progression in intermediate age‐related macular degeneration using the range of established and emerging precursory signs that herald loss of vision.

Automatic detection of diabetic retinopathy features in ultra-wide field retinal images.

Diabetic retinopathy (DR) is a major cause of irreversible vision loss. DR screening relies on retinal clinical signs (features). Opportunities for computer-aided DR feature detection have emerged with the development of Ultra-WideField (UWF) digital scanning laser technology. UWF imaging covers 82% greater retinal area (200°), against 45° in conventional cameras3 , allowing more clinically relevant retinopathy to be detected4 . UWF images also provide a high resolution of 3078 x 2702 pixels. Currently DR screening uses 7 overlapping conventional fundus images, and the UWF images provide similar results1,4. However, in 40% of cases, more retinopathy was found outside the 7-field ETDRS) fields by UWF and in 10% of cases, retinopathy was reclassified as more severe4 . This is because UWF imaging allows examination of both the central retina and more peripheral regions, with the latter implicated in DR6 . We have developed an algorithm for automatic recognition of DR features, including bright (cotton wool spots and exudates) and dark lesions (microaneurysms and blot, dot and flame haemorrhages) in UWF images. The algorithm extracts features from grayscale (green “red-free” laser light) and colour-composite UWF images, including intensity, Histogram-of-Gradient and Local binary patterns. Pixel-based classification is performed with three different classifiers. The main contribution is the automatic detection of DR features in the peripheral retina. The method is evaluated by leave-one-out cross-validation on 25 UWF retinal images with 167 bright lesions, and 61 other images with 1089 dark lesions. The SVM classifier performs best with AUC of 94.4% / 95.31% for bright / dark lesions.