AimTo develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk.MethodsA total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality.ResultsThe DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality.ConclusionsOur findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions.
Lead halide perovskites have attracted striking attention recently, due to their appealing properties. However, toxicity and stability are two main factors restricting their application. In this work, a less toxic and highly stable Pd-based hybrid perovskite was experimentally synthesized, after exploring different experimental conditions. This new hybrid organic-inorganic perovskite (CH NH ) PdBr was found to be an orthorhombic crystal (Cmce, Z=4) with lattice parameters a=8.00, b=7.99, c=18.89 Å. The Cmce symmetry and lattice parameters were confirmed using Pawley refinement and the atoms positions were confirmed based on DFT calculation. This perovskite compound was determined to be a p-type semiconductor, with a resistivity of 102.9 kΩ cm, a carrier concentration of 3.4 ×10 cm , and a mobility of 23.4 cm (V s) . Interestingly, XRD and UV/Vis measurements indicated that the phase of this new perovskite was maintained with an optical gap of 1.91 eV after leaving in air with a high humidity of 60 % for 4 days, and unchanged for months in N atmosphere; much more stable than most existing organic-inorganic perovskites. The synthesis and various characterizations of this work further the understanding of this (CH NH ) PdBr organic-inorganic hybrid perovskite material.
Artificial intelligence has rapidly evolved from the experimental phase to the implementation phase in many image-driven clinical disciplines, including ophthalmology. A combination of the increasing availability of large datasets and computing power with revolutionary progress in deep learning has created unprecedented opportunities for major breakthrough improvements in the performance and accuracy of automated diagnoses that primarily focus on image recognition and feature detection. Such an automated disease classification would significantly improve the accessibility, efficiency, and cost-effectiveness of eye care systems where it is less dependent on human input, potentially enabling diagnosis to be cheaper, quicker, and more consistent. Although this technology will have a profound impact on clinical flow and practice patterns sooner or later, translating such a technology into clinical practice is challenging and requires similar levels of accountability and effectiveness as any new medication or medical device due to the potential problems of bias, and ethical, medical, and legal issues that might arise. The objective of this review is to summarize the opportunities and challenges of this transition and to facilitate the integration of artificial intelligence (AI) into routine clinical practice based on our best understanding and experience in this area.
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