LegoNet: Alternating Model Blocks for Medical Image Segmentation

Since the emergence of convolutional neural networks (CNNs), and later vision transformers (ViTs), the common paradigm for model development has always been using a set of identical block types with varying parameters/hyper-parameters. To leverage the benefits of different architectural designs (e.g. CNNs and ViTs), we propose to alternate structurally different types of blocks to generate a new architecture, mimicking how Lego blocks can be assembled together. Using two CNN-based and one SwinViT-based blocks, we investigate three variations to the so-called LegoNet that applies the new concept of block alternation for the segmentation task in medical imaging. We also study a new clinical problem which has not been investigated before, namely the right internal mammary artery (RIMA) and perivascular space segmentation from computed tomography angiography (CTA) which has demonstrated a prognostic value to major cardiovascular outcomes. We compare the model performance against popular CNN and ViT architectures using two large datasets (e.g. achieving 0.749 dice similarity coefficient (DSC) on the larger dataset). We evaluate the performance of the model on three external testing cohorts as well, where an expert clinician made corrections to the model segmented results (DSC>0.90 for the three cohorts). To assess our proposed model for suitability in clinical use, we perform intra- and inter-observer variability analysis. Finally, we investigate a joint self-supervised learning approach to assess its impact on model performance. The code and the pretrained model weights will be available upon acceptance.Comment: 12 pages, 5 figures, 4 table

A nationwide study of adults admitted to hospital with diabetic ketoacidosis or hyperosmolar hyperglycaemic state and COVID‐19

AimsTo investigate characteristics of people hospitalized with coronavirus-disease-2019 (COVID-19) and diabetic ketoacidosis (DKA) or hyperosmolar hyperglycaemic state (HHS), and to identify risk factors for mortality and intensive care admission.Materials and methodsRetrospective cohort study with anonymized data from the Association of British Clinical Diabetologists nationwide audit of hospital admissions with COVID-19 and diabetes, from start of pandemic to November 2021. The primary outcome was inpatient mortality. DKA and HHS were adjudicated against national criteria. Age-adjusted odds ratios were calculated using logistic regression.ResultsIn total, 85 confirmed DKA cases, and 20 HHS, occurred among 4073 people (211 type 1 diabetes, 3748 type 2 diabetes, 114 unknown type) hospitalized with COVID-19. Mean (SD) age was 60 (18.2) years in DKA and 74 (11.8) years in HHS (p < .001). A higher proportion of patients with HHS than with DKA were of non-White ethnicity (71.4% vs 39.0% p = .038). Mortality in DKA was 36.8% (n = 57) and 3.8% (n = 26) in type 2 and type 1 diabetes respectively. Among people with type 2 diabetes and DKA, mortality was lower in insulin users compared with non-users [21.4% vs. 52.2%; age-adjusted odds ratio 0.13 (95% CI 0.03-0.60)]. Crude mortality was lower in DKA than HHS (25.9% vs. 65.0%, p = .001) and in statin users versus non-users (36.4% vs. 100%; p = .035) but these were not statistically significant after age adjustment.ConclusionsHospitalization with COVID-19 and adjudicated DKA is four times more common than HHS but both associate with substantial mortality. There is a strong association of previous insulin therapy with survival in type 2 diabetes-associated DKA

Prevalence of non-alcoholic fatty liver disease: population based study

Background and aim: Non-alcoholic fatty liver disease (NAFLD) is a common cause of chronic liver disease and liver transplantation in western countries. Increasing incidence of NAFLD has been well documented from Asian countries like Japan and China. Diabetes mellitus (DM), obesity, hyperinsulinemia are predisposing factors for NAFLD. There is increase in incidence of DM, obesity and insulin resistance in India in last two decades. Hence it is logical to expect increase in incidence of NAFLD in India. There is limited data on the prevalence of NAFLD from India. Majority of data comes from hospital based studies including small number of patients. Therefore this study was planned to estimate the prevalence of NAFLD in general population. Material and methods: Residents of two Railway colonies were evaluated on history, clinical examination, anthropometric measurements, biochemical tests and abdominal ultrasound. Results: 1,168 participants were evaluated. Persons with any amount of alcohol consumption, HBs Ag positive, Anti HCV positive, persons with other known liver diseases and taking medications causing liver disease were excluded. Prevalence of NAFLD on ultrasound was 16.6%. Out of 730 subjects above the age of 20 years (341 male 384 female 389) mean age 39.08 ± 12.3 years, 4% had diabetes, 57% had central obesity. Prevalence of NAFLD based on the ultrasound above 20 years of age was 18.9%. NAFLD was more prevalent in male than female (24.6% vs 13.6%, p 25, elevated fasting blood sugar, raised AST and ALT. Conclusion: Prevalence of NAFLD in Indian population is comparable to the west