Is there a relationship between hypoparathyroidism and retinal microcirculation?

A lightweight deep learning model for automatic segmentation and analysis of ophthalmic images

Detection, diagnosis, and treatment of ophthalmic diseases depend on extraction of information (features and/or their dimensions) from the images. Deep learning (DL) model are crucial for the automation of it. Here, we report on the development of a lightweight DL model, which can precisely segment/detect the required features automatically. The model utilizes dimensionality reduction of image to extract important features, and channel contraction to allow only the required high-level features necessary for reconstruction of segmented feature image. Performance of present model in detection of glaucoma from optical coherence tomography angiography (OCTA) images of retina is high (area under the receiver-operator characteristic curve AUC ~ 0.81). Bland–Altman analysis gave exceptionally low bias (~ 0.00185), and high Pearson’s correlation coefficient (p = 0.9969) between the parameters determined from manual and DL based segmentation. On the same dataset, bias is an order of magnitude higher (~ 0.0694, p = 0.8534) for commercial software. Present model is 10 times lighter than Unet (popular for biomedical image segmentation) and have a better segmentation accuracy and model training reproducibility (based on the analysis of 3670 OCTA images). High dice similarity coefficient (D) for variety of ophthalmic images suggested it’s wider scope in precise segmentation of images even from other fields. Our concept of channel narrowing is not only important for the segmentation problems, but it can also reduce number of parameters significantly in object classification models. Enhanced disease diagnostic accuracy can be achieved for the resource limited devices (such as mobile phone, Nvidia’s Jetson, Raspberry pi) used in self-monitoring, and tele-screening (memory size of trained model ~ 35 MB).

Normal parathyroid hormone and non-proliferative diabetic retinopathy in patients with type 2 diabetes

AIMS/INTRODUCTION

To investigate the associations between parathyroid hormone (PTH) and non-proliferative diabetic retinopathy (NPDR) in patients with type 2 diabetes mellitus.

MATERIALS AND METHODS

Data were collected from 2,322 patients with type 2 diabetes mellitus in hospital between 2017 and 2019. The odds ratio (OR) and the corresponding 95% confidence interval related to the quartiles of PTH were obtained by logistic regression analysis after adjusting the potential confounding variation.

RESULTS

The patients were stratified into quartiles (Q1-Q4) based on the PTH levels, with the cut-off limits of ≤23.74, 23.74-29.47, 29.47-37.30 and >37.30 pg/mL in men, and ≤24.47, 24.47-31.22, 31.22-39.49 and >39.49 pg/mL in women. The first quartile (Q1) represents the lowest quartile and the fourth quartile (Q4) is the highest. According to the quartiles (Q1-Q4), the prevalence rate of NPDR in patients showed a significantly decreasing trend (37.9%, 36.3%, 34.0% vs 24.0% in men; 43.2%, 40.5%, 31.1% vs 26.2% in women, both P < 0.05). Independent of age, diabetes duration and other metabolic factors, multivariate logistic regression showed that participants in Q4 had a lower OR of NPDR than those in Q1 (OR 0.443, 95% confidence interval 0.300-0.654, P < 0.001 for men; OR 0.428, 95% confidence interval 0.283-0.646, P < 0.001 for women).

CONCLUSIONS

Low serum PTH levels were significantly associated with complications of NPDR in inpatients. Its causality remains to be further studied.