Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data

Classification of ultrasound (US) kidney images for diagnosis of congenital abnormalities of the kidney and urinary tract (CAKUT) in children is a challenging task. It is desirable to improve existing pattern classification models that are built upon conventional image features. In this study, we propose a transfer learning-based method to extract imaging features from US kidney images in order to improve the CAKUT diagnosis in children. Particularly, a pre-trained deep learning model (imagenet-caffe-alex) is adopted for transfer learning-based feature extraction from 3-channel feature maps computed from US images, including original images, gradient features, and distanced transform features. Support vector machine classifiers are then built upon different sets of features, including the transfer learning features, conventional imaging features, and their combination. Experimental results have demonstrated that the combination of transfer learning features and conventional imaging features yielded the best classification performance for distinguishing CAKUT patients from normal controls based on their US kidney images.Comment: Accepted paper in IEEE International Symposium on Biomedical Imaging (ISBI), 201

Lupus cystitis presenting with urinary symptoms.

We present a case of a young woman presenting with irritative lower urinary tract symptoms and microscopic hematuria who was diagnosed with systemic lupus erythematosus (SLE). Abdominal ultrasound revealed bilateral hydronephrosis and a thickened bladder wall. Cystoscopic evaluation revealed severe diffuse inflammation, erythema and hemorrhage at the trigone with punctate extensions to the bladder base. She was treated with prednisone and mycophenolate mofetil with improvements in her symptoms and ultrasound findings. Lupus cystitis is a rare manifestation of SLE

Sex differences in the temperature dependence of kidney stone presentations: a population-based aggregated case-crossover study.

Previous studies assumed a uniform relationship between heat and kidney stone presentations. Determining whether sex and other characteristics modify the temperature dependence of kidney stone presentations has implications for explaining differences in nephrolithiasis prevalence and improving projections of the effect of climate change on nephrolithiasis. We performed an aggregated case-crossover study among 132,597 children and adults who presented with nephrolithiasis to 68 emergency departments throughout South Carolina from 1997 to 2015. We used quasi-Poisson regression with distributed lag non-linear models to estimate sex differences in the cumulative exposure and lagged response between maximum daily wet-bulb temperatures and emergent kidney stone presentations, aggregated at the ZIP-code level. We also explored interactions by age, race, payer, and climate. Compared to 10 °C, daily wet-bulb temperatures at the 99th percentile were associated with a greater increased relative risk (RR) of kidney stone presentations over 10 days for males (RR 1.73; 95% CI 1.56, 1.91) than for females (RR 1.15; 95% CI 1.01, 1.32; interaction P < 0.001). The shape of the lagged response was similar for males and females, with the greatest risk estimated for the 2 days following high temperatures. There were weak differences by age, race, and climatic zone, and no differences by payer status. The estimated risk of presenting emergently with kidney stones within 10 days of high daily wet-bulb temperatures was substantially greater among men than women, and similar between patients with public and private insurance. These findings suggest that the higher risk among males may be due to sexually dimorphic physiologic responses rather than greater exposure to ambient temperatures

Standardization of microbiome studies for urolithiasis: an international consensus agreement

Numerous metagenome-wide association studies (MWAS) for urolithiasis have been published, leading to the discovery of potential interactions between the microbiome and urolithiasis. However, questions remain about the reproducibility, applicability and physiological relevance of these data owing to discrepancies in experimental technique and a lack of standardization in the field. One barrier to interpreting MWAS is that experimental biases can be introduced at every step of the experimental pipeline, including sample collection, preservation, storage, processing, sequencing, data analysis and validation. Thus, the introduction of standardized protocols that maintain the flexibility to achieve study-specific objectives is urgently required. To address this need, the first international consortium for microbiome in urinary stone disease - MICROCOSM - was created and consensus panel members were asked to participate in a consensus meeting to develop standardized protocols for microbiome studies if they had published an MWAS on urolithiasis. Study-specific protocols were revised until a consensus was reached. This consensus group generated standardized protocols, which are publicly available via a secure online server, for each step in the typical clinical microbiome-urolithiasis study pipeline. This standardization creates the benchmark for future studies to facilitate consistent interpretation of results and, collectively, to lead to effective interventions to prevent the onset of urolithiasis, and will also be useful for investigators interested in microbiome research in other urological diseases

From Single-Visit to Multi-Visit Image-Based Models: Single-Visit Models are Enough to Predict Obstructive Hydronephrosis

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.Comment: Paper accepted to SIPAIM 2022 (in Valparaiso, Chile

The Association of Social Distancing, Population Density, and Temperature with the SARS-CoV-2 Instantaneous Reproduction Number in Counties Across the United States

AbstractImportanceThe Covid-19 pandemic has been marked by considerable heterogeneity in outbreaks across the United States. Local factors that may be associated with variation in SARS-CoV-2 transmission have not been well studied.ObjectiveTo examine the association of county-level factors with variation in the SARS-CoV-2 reproduction number over time.DesignObservational studySetting211 counties in 46 states and the District of Columbia between February 25, 2020 and April 23, 2020.ParticipantsResidents within the counties (55% of the US population)ExposuresSocial distancing as measured by percent change in visits to non-essential businesses, population density, lagged daily wet bulb temperatures.Main Outcomes and MeasuresThe instantaneous reproduction number (Rt) which is the estimated number of cases generated by one case at a given time during the pandemic.ResultsMedian case incidence was 1185 cases and fatality rate was 43.7 deaths per 100,000 people for the top decile of 21 counties, nearly ten times the incidence and fatality rate in the lowest density quartile. Average Rt in the first two weeks was 5.7 (SD 2.5) in the top decile, compared to 3.1 (SD 1.2) in the lowest quartile. In multivariable analysis, a 50% decrease in visits to non-essential businesses was associated with a 57% decrease in Rt (95% confidence interval, 56% to 58%). Cumulative temperature effects over 4 to 10 days prior to case incidence were nonlinear; relative Rt decreased as temperatures warmed above 32°F to 53°F, which was the point of minimum Rt, then increased between 53°F and 66°F, at which point Rt began to decrease. At 55°F, and with a 70% reduction in visits to non-essential business, 96% of counties were estimated to fall below a threshold Rt of 1.0, including 86% of counties among the top density decile and 98% of counties in the lowest density quartile.Conclusions and RelevanceSocial distancing, lower population density, and temperate weather change were associated with a decreased SARS-Co-V-2 Rt in counties across the United States. These relationships can inform selective public policy planning in communities during the SARS-CoV-2 pandemic.Key PointsQuestionHow is the instantaneous reproduction number (Rt) of SARS-CoV-2 influenced by local area effects of social distancing, wet bulb temperature, and population density in counties across the United States?FindingsSocial distancing, temperate weather, and lower population density were associated with a decrease in Rt. Of these county-specific factors, social distancing appeared to be the most significant in reducing SARS-CoV-2 transmission.MeaningRt varies significantly across counties. The relationship between Rt and county-specific factors can inform policies to reduce SARS-CoV-2 transmission in selective and heterogeneous communities.</jats:sec