Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

The detection of keratoconus has been a difficult and arduous process over the years for ophthalmologists who have devised traditional approaches of diagnosis including the slit-lamp examination and observation of thinning of the corneal. The main contribution of this paper is using deep learning models namely Resnet50 and EfficientNet to not just detect whether an eye has been infected with keratoconus or not but also accurately detect the stages of infection namely mild, moderate, and advanced. The dataset used consists of corneal topographic maps and pentacam images. Individually the models achieved 97% and 94% accuracy on the dataset. We have also employed class activated maps (CAM) to observe and help visualize which areas of the images are utilized when making classifications for the different stages of keratoconus. Using deep learning models to predict the detection and severity of the infection can drastically speed up and provide accurate results at the same time

A cross-sectional study on asymptomatic bacteriuria among antenatal women attending an urban tertiary health care center in Southern India

Background: Asymptomatic bacteriuria (ASB) in pregnancy is a treatable risk factor for preterm delivery. India accounts for the highest preterm birth incidence in the world according to the WHO census released in November 2016. This study was aimed at finding the prevalence of asymptomatic bacteriuria, the spectrum of bacteria involved and the susceptibility pattern for the antimicrobials in the antenatal women attending a tertiary care hospital in urban Southern India.Methods: One hundred and eighty ante-natal patients without symptoms of ongoing urinary tract infection were enrolled to this study. Clean midstream urine sample was collected in a wide mouthed container and sample was analyzed by standardized microbiological testing techniques.Results: Out of the 180 ante-natal women included in the study, 11(6.1%) patients were found to have insignificant bacteriuria and 38(21.1%) had a significant bacteriuria. E. coli was the most frequently isolated organism and about 95% of the organisms were sensitive to Nitrofurantoin.Conclusions: More than a fifth of all pregnant women have ASB and E. coli is the most frequent pathogen encountered

Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

The detection of keratoconus has been a difficult and arduous process over the years for ophthalmologists who have devised traditional approaches of diagnosis including the slit-lamp examination and observation of thinning of the corneal. The main contribution of this paper is using deep learning models namely Resnet50 and EfficientNet to not just detect whether an eye has been infected with keratoconus or not but also accurately detect the stages of infection namely mild, moderate, and advanced. The dataset used consists of corneal topographic maps and pentacam images. Individually the models achieved 97% and 94% accuracy on the dataset. We have also employed class activated maps (CAM) to observe and help visualize which areas of the images are utilized when making classifications for the different stages of keratoconus. Using deep learning models to predict the detection and severity of the infection can drastically speed up and provide accurate results at the same time