Using machine-learning to decode postoperative hip mortality Trends: Actionable insights from an extensive clinical dataset.

BACKGROUND: Hip fractures are one of the most common injuries experienced by the general population. Despite advances in surgical techniques, postoperative mortality rates remain high. identifying relevant clinical factors associated with mortality is essential to preoperative risk stratification and tailored post-surgical interventions to improve patient outcomes. The purpose of this study aimed to identify preoperative risk factors and develop predictive models for increased hip fracture-related mortality within 30 days post-surgery, using one of the largest patient cohorts to date. METHODS: Data from the American College of Surgeons National Surgical Quality Improvement Program database, comprising 107,660 hip fracture patients treated with surgical fixation was used. A penalized regression approach, least absolute shrinkage and selection operator was employed to develop two predictive models: one using preoperative factors and the second incorporating both preoperative and postoperative factors. RESULTS: The analysis identified 68 preoperative factor outcomes associated with 30-day mortality. The combined model revealed 84 relevant factors, showing strong predictive power for determining postoperative mortality, with an AUC of 0.83. CONCLUSIONS: The study's comprehensive methodology provides risk assessment tools for clinicians to identify high-risk patients and optimize patient-specific care.

COVID-19: the novel coronavirus disease and its manifestations and management in ophthalmology.

Coronavirus disease 2019 (COVID-19), a newly identified acute respiratory disease caused by a strain of novel coronavirus (SARS-CoV-2), has become a worldwide pandemic. From December 2019 to present, millions of cases have been reported, bringing unprecedented pressure on both health and epidemic prevention services in every country. As frontline healthcare workers, ophthalmologists face an increased threat of viral infection, not only because of close contact with patients during examinations or operations, but also due to evidence showing that ocular fluids such as tears or conjunctival secretions may carry the virus. The risk that healthcare workers face is emphasized by the loss of our colleagues who have sacrificed themselves in combating the virus. As a result, it is necessary to have a comprehensive understanding of the threats that we face. In the first part of this review, we start by explaining the structure of SARS-CoV-2 and examining its transmission and means of infection. Next, we summarize the latest scientific advancements of epidemiology, clinical presentations, and current treatments of COVID-19. In the second half of the review, we emphasize the ocular transmission, symptomatic manifestations, and the essential knowledge in an ophthalmology clinic setting. As the pandemic of COVID-19 continues to pose a threat to global health, we hope that this review makes a contribution to combating COVID-19.

Segmentation of Glomeruli Within Trichrome Images Using Deep Learning.

INTRODUCTION: The number of glomeruli and glomerulosclerosis evaluated on kidney biopsy slides constitute standard components of a renal pathology report. Prevailing methods for glomerular assessment remain manual, labor intensive, and nonstandardized. We developed a deep learning framework to accurately identify and segment glomeruli from digitized images of human kidney biopsies. METHODS: Trichrome-stained images (n = 275) from renal biopsies of 171 patients with chronic kidney disease treated at the Boston Medical Center from 2009 to 2012 were analyzed. A sliding window operation was defined to crop each original image to smaller images. Each cropped image was then evaluated by at least 3 experts into 3 categories: (i) no glomerulus, (ii) normal or partially sclerosed (NPS) glomerulus, and (iii) globally sclerosed (GS) glomerulus. This led to identification of 751 unique images representing nonglomerular regions, 611 images with NPS glomeruli, and 134 images with GS glomeruli. A convolutional neural network (CNN) was trained with cropped images as inputs and corresponding labels as output. Using this model, an image processing routine was developed to scan the test images to segment the GS glomeruli. RESULTS: The CNN model was able to accurately discriminate nonglomerular images from NPS and GS images (performance on test data: Accuracy: 92.67% ± 2.02% and Kappa: 0.8681 ± 0.0392). The segmentation model that was based on the CNN multilabel classifier accurately marked the GS glomeruli on the test data (Matthews correlation coefficient = 0.628). CONCLUSION: This work demonstrates the power of deep learning for assessing complex histologic structures from digitized human kidney biopsies.

Association of Pathological Fibrosis With Renal Survival Using Deep Neural Networks.

INTRODUCTION: Chronic kidney damage is routinely assessed semiquantitatively by scoring the amount of fibrosis and tubular atrophy in a renal biopsy sample. Although image digitization and morphometric techniques can better quantify the extent of histologic damage, we need more widely applicable ways to stratify kidney disease severity. METHODS: We leveraged a deep learning architecture to better associate patient-specific histologic images with clinical phenotypes (training classes) including chronic kidney disease (CKD) stage, serum creatinine, and nephrotic-range proteinuria at the time of biopsy, and 1-, 3-, and 5-year renal survival. Trichrome-stained images processed from renal biopsy samples were collected on 171 patients treated at the Boston Medical Center from 2009 to 2012. Six convolutional neural network (CNN) models were trained using these images as inputs and the training classes as outputs, respectively. For comparison, we also trained separate classifiers using the pathologist-estimated fibrosis score (PEFS) as input and the training classes as outputs, respectively. RESULTS: CNN models outperformed PEFS across the classification tasks. Specifically, the CNN model predicted the CKD stage more accurately than the PEFS model (κ = 0.519 vs. 0.051). For creatinine models, the area under curve (AUC) was 0.912 (CNN) versus 0.840 (PEFS). For proteinuria models, AUC was 0.867 (CNN) versus 0.702 (PEFS). AUC values for the CNN models for 1-, 3-, and 5-year renal survival were 0.878, 0.875, and 0.904, respectively, whereas the AUC values for PEFS model were 0.811, 0.800, and 0.786, respectively. CONCLUSION: The study demonstrates a proof of principle that deep learning can be applied to routine renal biopsy images.