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Artificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude that clinicians have to this new technology. In June–August 2019 we conducted an online survey of fellows and trainees of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand on artificial intelligence. There were 632 complete responses (n = 305, 230, and 97, respectively), equating to a response rate of 20.4%, 5.1%, and 13.2% for the above colleges, respectively. The majority (n = 449, 71.0%) believed artificial intelligence would improve their field of medicine, and that medical workforce needs would be impacted by the technology within the next decade (n = 542, 85.8%). Improved disease screening and streamlining of monotonous tasks were identified as key benefits of artificial intelligence. The divestment of healthcare to technology companies and medical liability implications were the greatest concerns. Education was identified as a priority to prepare clinicians for the implementation of artificial intelligence in healthcare. This survey highlights parallels between the perceptions of different clinician groups in Australia and New Zealand about artificial intelligence in medicine. Artificial intelligence was recognized as valuable technology that will have wide-ranging impacts on healthcare.

Studies of rodent models of Alzheimer’s disease (AD) and of human tissues suggest that the retinal changes that occur in AD, including the accumulation of amyloid beta (Aβ), may serve as surrogate markers of brain Aβ levels. As Aβ has a wavelength-dependent effect on light scatter, we investigate the potential for in vivo retinal hyperspectral imaging to serve as a biomarker of brain Aβ. Significant differences in the retinal reflectance spectra are found between individuals with high Aβ burden on brain PET imaging and mild cognitive impairment ( n  = 15), and age-matched PET-negative controls ( n  = 20). Retinal imaging scores are correlated with brain Aβ loads. The findings are validated in an independent cohort, using a second hyperspectral camera. A similar spectral difference is found between control and 5xFAD transgenic mice that accumulate Aβ in the brain and retina. These findings indicate that retinal hyperspectral imaging may predict brain Aβ load. The use of PET for detection of Aβ in the brain in AD has limitations; studies also indicate that retinal changes, including Aβ deposition, occur in AD. Here the authors demonstrate the potential to use in vivo retinal hyperspectral imaging as a surrogate for brain accumulation of Aβ.

Background/aimsTo examine the association between artificial intelligence (AI)-driven segmentation of geographic atrophy (GA) on optical coherence tomography (OCT) and visual sensitivity loss quantified by defect-mapping microperimetry, a testing strategy optimised to quantify the spatial extent of deep visual sensitivity losses.Methods50 individuals with GA secondary to age-related macular degeneration underwent defect-mapping microperimetry testing within the central 8° radius region in one eye. GA on OCT was automatically segmented with an AI-based multiclass classification and segmentation model, and GA on fundus autofluorescence (FAF) images was manually annotated. Their extent in the topographically corresponding region sampled on microperimetry was derived, and structure-function relationships were examined based on Spearman correlation coefficients (ρ). The distance of each test location from the OCT-defined and FAF-defined GA margin was also derived and used in prediction models of non-response on defect-mapping microperimetry.ResultsThere was a strong correlation between the proportion of locations missed on defect-mapping microperimetry and the corresponding percentage of the central 8° radius region with GA on OCT (ρ=0.85) and FAF (ρ=0.89). Prediction models for non-response at individual test locations using GA derived from OCT and FAF imaging had a sensitivity of 59% and 62% (p=0.310), respectively, at 95% specificity.ConclusionsAI-driven, automated quantification of GA on OCT showed a strong correlation with the global extent of visual sensitivity loss, comparable with those based on manual annotations on FAF imaging. These findings affirm the expected functional relevance of OCT-derived GA measurements and their clinical utility for monitoring disease progression in those with GA.

Introduction We performed a systematic review and meta‐analysis of the association between retinal imaging parameters and Alzheimer's disease (AD). Methods PubMed, EMBASE, and Scopus were systematically searched for prospective and observational studies. Included studies had AD case definition based on brain amyloid beta (Aβ) status. Study quality assessment was performed. Random‐effects meta‐analyses of standardized mean difference, correlation, and diagnostic accuracy were conducted. Results Thirty‐eight studies were included. There was weak evidence of peripapillary retinal nerve fiber layer thinning on optical coherence tomography (OCT) (p = 0.14, 11 studies, n = 828), increased foveal avascular zone area on OCT‐angiography (p = 0.18, four studies, n = 207), and reduced arteriole and venule vessel fractal dimension on fundus photography (p < 0.001 and p = 0.08, respectively, three studies, n = 297) among AD cases. Discussion Retinal imaging parameters appear to be associated with AD. Small study sizes and heterogeneity in imaging methods and reporting make it difficult to determine utility of these changes as AD biomarkers. Highlights We performed a systematic review on retinal imaging and Alzheimer's disease (AD). We only included studies in which cases were based on brain amyloid beta status. Several retinal biomarkers were associated with AD but clinical utility is uncertain. Studies should focus on biomarker‐defined AD and use standardized imaging methods.

Hyperspectral imaging is a frontier in the field of medical imaging technology. It enables the simultaneous collection of spectroscopic and spatial data. Structural and physiological information encoded in these data can be used to identify and localise typically elusive biomarkers. Studies of retinal hyperspectral imaging have provided novel insights into disease pathophysiology and new ways of non-invasive diagnosis and monitoring of retinal and systemic diseases. This review provides a concise overview of recent advances in retinal hyperspectral imaging.

In this study, we report the results of a comprehensive phenotyping of the retina of the App mouse. We demonstrate that soluble Aβ accumulation is present in the retina of these mice early in life and progresses to Aβ plaque formation by midlife. This rising Aβ burden coincides with local microglia reactivity, astrogliosis, and abnormalities in retinal vein morphology. Electrophysiological recordings revealed signs of neuronal dysfunction yet no overt neurodegeneration was observed and visual performance outcomes were unaffected in the App mouse. Furthermore, we show that hyperspectral imaging can be used to quantify retinal Aβ, underscoring its potential as a biomarker for AD diagnosis and monitoring. These findings suggest that the App retina mimics the early, preclinical stages of AD, and, together with retinal imaging techniques, offers unique opportunities for drug discovery and fundamental research into preclinical AD.

Background/aimsTo investigate the additional prognostic value of quantifying the extent of colour fundus photography (CFP)-defined hyperpigmentary abnormalities (HPAs) compared with their presence alone for predicting progression to late-stage age-related macular degeneration (AMD) and to understand their association with visual sensitivity in individuals with intermediate AMD.Methods140 participants with bilateral large drusen underwent multimodal imaging and microperimetry at baseline and then every 6 months for up to 3 years. Baseline CFPs were graded for the presence of HPAs and their extent was quantified. Optical coherence tomography (OCT) scans were used to quantify drusen volume. Predictive models for progression to late AMD (including OCT signs of atrophy) were developed using either HPA presence or extent. The association between HPA extent with mean visual sensitivity (both overall and sector based) was also evaluated. All models were adjusted for the confounders of baseline age and drusen volume.ResultsThe predictive performance for late AMD development was not significantly different for HPA presence or extent (p=0.92). Increasing HPA extent in each sector, but not its overall extent in an eye, was associated with reduced sector-based visual sensitivity (p<0.001 and p=0.671, respectively).ConclusionIn a cohort with bilateral large drusen, quantifying HPA extent did not improve the prediction of late AMD development compared with presence alone. HPA extent was associated with more local, rather than generalised, reductions in visual sensitivity. These findings suggest that quantification of HPA extent adds little to the prediction of AMD progression, but that it provides an imaging biomarker of visual dysfunction.

In this study, we report the results of a comprehensive phenotyping of the retina of the App NL - G - F mouse. We demonstrate that soluble Aβ accumulation is present in the retina of these mice early in life and progresses to Aβ plaque formation by midlife. This rising Aβ burden coincides with local microglia reactivity, astrogliosis, and abnormalities in retinal vein morphology. Electrophysiological recordings revealed signs of neuronal dysfunction yet no overt neurodegeneration was observed and visual performance outcomes were unaffected in the App NL - G - F mouse. Furthermore, we show that hyperspectral imaging can be used to quantify retinal Aβ, underscoring its potential as a biomarker for AD diagnosis and monitoring. These findings suggest that the App NL - G - F retina mimics the early, preclinical stages of AD, and, together with retinal imaging techniques, offers unique opportunities for drug discovery and fundamental research into preclinical AD.

Abstract In this study, we report the results of a comprehensive phenotyping of the retina of the AppNL-G-F mouse. We demonstrate that soluble Aβ accumulation is present in the retina of these mice early in life and progresses to Aβ plaque formation by midlife. This rising Aβ burden coincides with local microglia reactivity, astrogliosis, and abnormalities in retinal vein morphology. Electrophysiological recordings reveal signs of neuronal dysfunction yet no overt neurodegeneration was observed and visual performance outcomes were unaffected in the AppNL-G-F mouse. Furthermore, we show that hyperspectral imaging can be used to quantify retinal Aβ, underscoring its potential as a biomarker for AD diagnosis and monitoring. These findings suggest that the AppNL-G-F retina mimics the early, preclinical stages of AD, and, together with retinal imaging techniques, offers unique opportunities for drug discovery and fundamental research into preclinical AD. Competing Interest Statement The authors declare the following competing interests: XH and PvW have filed an International (PCT) Patent Application No PCT/AU2019/000003 relating to retinal hyperspectral imaging. They are co-founders of Enlighten Imaging PTY LTD, a start-up company focused on developing novel retinal imaging solutions for neurological and retinal diseases. * List of abbreviations AD Alzheimer’s disease Aβ amyloid-beta CNS central nervous system ChAT choline acetyltransferase GFAP glial acidic fibrillary protein GFP green fluorescent protein HSI hyperspectral imaging Iba1 ionized calcium-binding adapter molecule 1 PFA paraformaldehyde RBPMS RNA-binding protein with multiple splicing S100B S100 calcium binding protein B WT wild type