Deep Learning for Predicting Refractive Error From Retinal Fundus Images

PURPOSE. We evaluate how deep learning can be applied to extract novel information such as refractive error from retinal fundus imaging. METHODS. Retinal fundus images used in this study were 45- and 30-degree field of view images from the UK Biobank and Age-Related Eye Disease Study (AREDS) clinical trials, respectively. Refractive error was measured by autorefraction in UK Biobank and subjective refraction in AREDS. We trained a deep learning algorithm to predict refractive error from a total of 226,870 images and validated it on 24,007 UK Biobank and 15,750 AREDS images. Our model used the ‘‘attention’’ method to identify features that are correlated with refractive error. RESULTS. The resulting algorithm had a mean absolute error (MAE) of 0.56 diopters (95% confidence interval [CI]: 0.55–0.56) for estimating spherical equivalent on the UK Biobank data set and 0.91 diopters (95% CI: 0.89–0.93) for the AREDS data set. The baseline expected MAE (obtained by simply predicting the mean of this population) was 1.81 diopters (95% CI: 1.79–1.84) for UK Biobank and 1.63 (95% CI: 1.60–1.67) for AREDS. Attention maps suggested that the foveal region was one of the most important areas used by the algorithm to make this prediction, though other regions also contribute to the prediction. CONCLUSIONS. To our knowledge, the ability to estimate refractive error with high accuracy from retinal fundus photos has not been previously known and demonstrates that deep learning can be applied to make novel predictions from medical images

Comparison of body mass index with waist circumference and skinfold-based percent body fat in firefighters: adiposity classification and associations with cardiovascular disease risk factors

PurposeThis study aims to examine whether body mass index (BMI) overestimates the prevalence of overweight or obese firefighters when compared to waist circumference (WC) and skinfold-based percent body fat (PBF) and to investigate differential relationships of the three adiposity measures with other biological cardiovascular disease (CVD) risk factors.MethodsThe adiposity of 355 (347 males and eight females) California firefighters was assessed using three different measures. Other CVD risk factors (high blood pressure, high lipid profiles, high glucose, and low VO2 max) of the firefighters were also clinically assessed.ResultsThe prevalence of total overweight and obesity was significantly (p < 0.01) higher by BMI (80.4 %) than by WC (48.7 %) and by PBF (55.6 %) in male firefighters. In particular, the prevalence of overweight firefighters was much higher (p < 0.01) by BMI (57.3 %) than by WC (24.5 %) and PBF (38.3 %). 60-64 % of male firefighters who were assessed as normal weight by WC and PBF were misclassified as overweight by BMI. When overweight by BMI was defined as 27.5-29.9 kg/m(2) (vs. the standard definition of 25.0-29.9 kg/m(2)), the agreement of the adiposity classification increased between BMI and other two adiposity measures. Obese firefighters had the highest CVD risk profiles across all three adiposity measures. Only when overweight by BMI was defined narrowly, overweight firefighters had substantially higher CVD risk profiles. Obesity and overweight were less prevalent in female and Asian male firefighters.ConclusionsBMI overestimated the prevalence of total overweight and obesity among male firefighters, compared to WC and skinfold-based PBF. Overweight by BMI needs to be more narrowly defined, or the prevalence of BMI-based overweight (27.5-29.9 kg/m(2)) should be reported additionally for prevention of CVD among male firefighters