Natural Language Processing and Machine Learning for Detection of Respiratory Illness by Chest CT Imaging and Tracking of COVID-19 Pandemic in the US.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread quickly throughout the United States (US) causing significant disruption in healthcare and society. Tools to identify hot spots are important for public health planning. The goal of our study was to determine if natural language processing (NLP) algorithm assessment of thoracic computed tomography (CT) imaging reports correlated with the incidence of official COVID-19 cases in the US. METHODS: Using de-identified HIPAA compliant patient data from our common imaging platform interconnected with over 2,100 facilities covering all 50 states, we developed three NLP algorithms to track positive CT imaging features of respiratory illness typical in SARS-CoV-2 viral infection. We compared our findings against the number of official COVID-19 daily, weekly and state-wide. RESULTS: The NLP algorithms were applied to 450,114 patient chest CT comprehensive reports gathered from January 1st to October 3rd, 2020. The best performing NLP model exhibited strong correlation with daily official COVID-19 cases (r2=0.82, p<0.005). The NLP models demonstrated an early rise in cases followed by the increase of official cases, suggesting the possibility of an early predictive marker, with strong correlation to official cases on a weekly basis (r2=0.91, p<0.005). There was also substantial correlation between the NLP and official COVID-19 incidence by state (r2=0.92, p<0.005). CONCLUSION: Using big data, we developed a novel machine-learning based NLP algorithm that can track imaging findings of respiratory illness detected on chest CT imaging reports with strong correlation with the progression of the COVID-19 pandemic in the US.

Automated Cardiovascular Pathology Assessment Using Semantic Segmentation and Ensemble Learning.

Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac cine sequencing provides a basis to quantify cardiac measures such as ejection fraction. A method is presented that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification into a fully automatic processing pipeline. The initial processing element consists of a robust dilated convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The resulting comprehensive volumetric feature matrix captures diagnostic clinical procedure data and is utilized by the final processing element to model a cardiac pathology classifier. Our approach evaluated anonymized cardiac images from a training data set of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. The top average Dice index scores achieved were 0.940, 0.886, and 0.849 for structure segmentation of the left ventricle (LV), myocardium, and right ventricle (RV), respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using the trained model. Performance results demonstrate the potential for advanced machine learning methods to deliver accurate, efficient, and reproducible cardiac pathological assessment.

Automated Detection of Diabetic Retinopathy using Deep Learning.

Diabetic retinopathy is a leading cause of blindness among working-age adults. Early detection of this condition is critical for good prognosis. In this paper, we demonstrate the use of convolutional neural networks (CNNs) on color fundus images for the recognition task of diabetic retinopathy staging. Our network models achieved test metric performance comparable to baseline literature results, with validation sensitivity of 95%. We additionally explored multinomial classification models, and demonstrate that errors primarily occur in the misclassification of mild disease as normal due to the CNNs inability to detect subtle disease features. We discovered that preprocessing with contrast limited adaptive histogram equalization and ensuring dataset fidelity by expert verification of class labels improves recognition of subtle features. Transfer learning on pretrained GoogLeNet and AlexNet models from ImageNet improved peak test set accuracies to 74.5%, 68.8%, and 57.2% on 2-ary, 3-ary, and 4-ary classification models, respectively.

Relationship between serum 25-hydroxyvitamin D and lung function among Korean adults in Korea National Health and Nutrition Examination Survey (KNHANES), 2008-2010.

CONTEXT: The relationship between vitamin D status and pulmonary function has not been investigated for an East Asian population. OBJECTIVE: The aim of the present study was to examine the relationship of serum 25-hydroxyvitamin D [25(OH)D] with lung function in Korean adults. DESIGN AND SETTING: The analysis used data from the Korea National Health and Nutrition Examination Survey (KNHANES), a cross-sectional survey of Korean civilians, conducted from 2008 to 2010. PARTICIPANTS: A total of 10 096 people aged 19 years and older were selected from 16 administrative districts in South Korea. MAIN OUTCOME MEASURES: Serum 25(OH)D levels with lung function [forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC)]. RESULTS: Serum 25(OH)D concentration was positively associated with lung function after controlling for age, sex, height, and season. For FEV1 and FVC, the differences between top and bottom quartiles in 25(OH)D were 51 mL (SE, 17 mL, P trend <.001) and 58 mL (SE, 20 mL, P trend <.005) greater volume, respectively. Association of serum 25(OH)D with FEV1 and FVC was only slightly attenuated after adjustment for body mass index, lifestyle and socioeconomic factors, and respiratory illness. The subjects with a history of pulmonary tuberculosis showed a much higher increase in FEV1; the difference between top and bottom quartiles in 25(OH)D was 229 mL (SE, 87 mL, P trend <.01). CONCLUSION: Serum 25(OH)D levels have a positive correlation with pulmonary function. This relationship appears prominent in subjects with susceptibility to pulmonary tuberculosis.