scholarly journals Deep learning for automatically predicting early haematoma expansion in Chinese patients

2021 ◽  
pp. svn-2020-000647
Author(s):  
Jia-wei Zhong ◽  
Yu-jia Jin ◽  
Zai-jun Song ◽  
Bo Lin ◽  
Xiao-hui Lu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Model ◽  
Chinese Patients ◽  
Deep Learning Algorithm ◽  
Haematoma Volume ◽  
C Statistic ◽  
Haematoma Expansion ◽  
Contrast Ct ◽  
Deep Learning Model

Background and purposeEarly haematoma expansion is determinative in predicting outcome of intracerebral haemorrhage (ICH) patients. The aims of this study are to develop a novel prediction model for haematoma expansion by applying deep learning model and validate its prediction accuracy.MethodsData of this study were obtained from a prospectively enrolled cohort of patients with primary supratentorial ICH from our centre. We developed a deep learning model to predict haematoma expansion and compared its performance with conventional non-contrast CT (NCCT) markers. To evaluate the predictability of this model, it was also compared with a logistic regression model based on haematoma volume or the BAT score.ResultsA total of 266 patients were finally included for analysis, and 74 (27.8%) of them experienced early haematoma expansion. The deep learning model exhibited highest C statistic as 0.80, compared with 0.64, 0.65, 0.51, 0.58 and 0.55 for hypodensities, black hole sign, blend sign, fluid level and irregular shape, respectively. While the C statistics for swirl sign (0.70; p=0.211) and heterogenous density (0.70; p=0.141) were not significantly higher than that of the deep learning model. Moreover, the predictive value for the deep learning model was significantly superior to that of the logistic model of haematoma volume (0.62; p=0.042) and the BAT score (0.65; p=0.042).ConclusionsCompared with the conventional NCCT markers and BAT predictive model, the deep learning algorithm showed superiority for predicting early haematoma expansion in ICH patients.

scholarly journals A Deep Learning Model for Screening Type 2 Diabetes from Retinal Photographs

2021 ◽  
Author(s):  
Jae-Seung Yun ◽  
Jaesik Kim ◽  
Sang-Hyuk Jung ◽  
Seon-Ah Cha ◽  
Seung-Hyun Ko ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Model ◽  
Retinal Images ◽  
Uk Biobank ◽  
Non Invasive ◽  
Deep Learning Algorithm ◽  
Deep Learning Model

Objective: We aimed to develop and evaluate a non-invasive deep learning algorithm for screening type 2 diabetes in UK Biobank participants using retinal images. Research Design and Methods: The deep learning model for prediction of type 2 diabetes was trained on retinal images from 50,077 UK Biobank participants and tested on 12,185 participants. We evaluated its performance in terms of predicting traditional risk factors (TRFs) and genetic risk for diabetes. Next, we compared the performance of three models in predicting type 2 diabetes using 1) an image-only deep learning algorithm, 2) TRFs, 3) the combination of the algorithm and TRFs. Assessing net reclassification improvement (NRI) allowed quantification of the improvement afforded by adding the algorithm to the TRF model. Results: When predicting TRFs with the deep learning algorithm, the areas under the curve (AUCs) obtained with the validation set for age, sex, and HbA1c status were 0.931 (0.928-0.934), 0.933 (0.929-0.936), and 0.734 (0.715-0.752), respectively. When predicting type 2 diabetes, the AUC of the composite logistic model using non-invasive TRFs was 0.810 (0.790-0.830), and that for the deep learning model using only fundus images was 0.731 (0.707-0.756). Upon addition of TRFs to the deep learning algorithm, discriminative performance was improved to 0.844 (0.826-0.861). The addition of the algorithm to the TRFs model improved risk stratification with an overall NRI of 50.8%. Conclusions: Our results demonstrate that this deep learning algorithm can be a useful tool for stratifying individuals at high risk of type 2 diabetes in the general population.

Extracting non-small cell lung cancer (NSCLC) diagnosis and diagnosis dates from electronic health record (EHR) text using a deep learning algorithm.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1556-1556
Author(s):  
Alexander S. Rich ◽  
Barry Leybovich ◽  
Melissa Estevez ◽  
Jamie Irvine ◽  
Nisha Singh ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Real World ◽  
Learning Algorithm ◽  
Learning Model ◽  
Early Stage Disease ◽  
Deep Learning Algorithm ◽  
The Impact ◽  
Deep Learning Model ◽  
Diagnosis Date

1556 Background: Identifying patients with a particular cancer and determining the date of that diagnosis from EHR data is important for selecting real world research cohorts and conducting downstream analyses. However, cancer diagnoses and their dates are often not accurately recorded in the EHR in a structured form. We developed a unified deep learning model for identifying patients with NSCLC and their initial and advanced diagnosis date(s). Methods: The study used a cohort of 52,834 patients with lung cancer ICD codes from the nationwide deidentified Flatiron Health EHR-derived database. For all patients in the cohort, abstractors used an in-house technology-enabled platform to identify an NSCLC diagnosis, advanced disease, and relevant diagnosis date(s) via chart review. Advanced NSCLC was defined as stage IIIB or IV disease at diagnosis or early stage disease that recurred or progressed. The deep learning model was trained on 38,517 patients, with a separate 14,317 patient test cohort. The model input was a set of sentences containing keywords related to (a)NSCLC, extracted from a patient’s EHR documents. Each sentence was associated with a date, using the document timestamp or, if present, a date mentioned explicitly in the sentence. The sentences were processed by a GRU network, followed by an attentional network that integrated across sentences, outputting a prediction of whether the patient had been diagnosed with (a)NSCLC and the diagnosis date(s) if so. We measured sensitivity and positive predictive value (PPV) of extracting the presence of initial and advanced diagnoses in the test cohort. Among patients with both model-extracted and abstracted diagnosis dates, we also measured 30-day accuracy, defined as the proportion of patients where the dates match to within 30 days. Real world overall survival (rwOS) for patients abstracted vs. model-extracted as advanced was calculated using Kaplan-Meier methods (index date: abstracted vs. model-extracted advanced diagnosis date). Results: Results in the Table show the sensitivity, PPV, and accuracy of the model extracted diagnoses and dates. RwOS was similar using model extracted aNSCLC diagnosis dates (median = 13.7) versus abstracted diagnosis dates (median = 13.3), with a difference of 0.4 months (95% CI = [0.0, 0.8]). Conclusions: Initial and advanced diagnosis of NSCLC and dates of diagnosis can be accurately extracted from unstructured clinical text using a deep learning algorithm. This can further enable the use of EHR data for research on real-world treatment patterns and outcomes analysis, and other applications such as clinical trials matching. Future work should aim to understand the impact of model errors on downstream analyses.[Table: see text]

scholarly journals A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development

10.2196/15931 ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. e15931 ◽  
Author(s):  
Chin-Sheng Lin ◽  
Chin Lin ◽  
Wen-Hui Fang ◽  
Chia-Jung Hsu ◽  
Sy-Jou Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Tissue ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Absolute Error ◽  
Learning Model ◽  
Cardiac Events ◽  
Deep Learning Algorithm ◽  
And Performance ◽  
Deep Learning Model

Background The detection of dyskalemias—hypokalemia and hyperkalemia—currently depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia, electrocardiography (ECG) may be able to uncover clinically important dyskalemias before laboratory results. Objective Our study aimed to develop a deep-learning model, ECG12Net, to detect dyskalemias based on ECG presentations and to evaluate the logic and performance of this model. Methods Spanning from May 2011 to December 2016, 66,321 ECG records with corresponding serum potassium (K+) concentrations were obtained from 40,180 patients admitted to the emergency department. ECG12Net is an 82-layer convolutional neural network that estimates serum K+ concentration. Six clinicians—three emergency physicians and three cardiologists—participated in human-machine competition. Sensitivity, specificity, and balance accuracy were used to evaluate the performance of ECG12Net with that of these physicians. Results In a human-machine competition including 300 ECGs of different serum K+ concentrations, the area under the curve for detecting hypokalemia and hyperkalemia with ECG12Net was 0.926 and 0.958, respectively, which was significantly better than that of our best clinicians. Moreover, in detecting hypokalemia and hyperkalemia, the sensitivities were 96.7% and 83.3%, respectively, and the specificities were 93.3% and 97.8%, respectively. In a test set including 13,222 ECGs, ECG12Net had a similar performance in terms of sensitivity for severe hypokalemia (95.6%) and severe hyperkalemia (84.5%), with a mean absolute error of 0.531. The specificities for detecting hypokalemia and hyperkalemia were 81.6% and 96.0%, respectively. Conclusions A deep-learning model based on a 12-lead ECG may help physicians promptly recognize severe dyskalemias and thereby potentially reduce cardiac events.

scholarly journals Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices

2021 ◽  
Vol 251 ◽  
pp. 04012
Author(s):  
Simon Akar ◽  
Gowtham Atluri ◽  
Thomas Boettcher ◽  
Michael Peters ◽  
Henry Schreiner ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Efficiency ◽  
Learning Algorithm ◽  
Learning Model ◽  
Operating Conditions ◽  
Proton Collision ◽  
Proton Proton ◽  
Deep Learning Algorithm ◽  
Proton Proton Collision ◽  
Deep Learning Model

The locations of proton-proton collision points in LHC experiments are called primary vertices (PVs). Preliminary results of a hybrid deep learning algorithm for identifying and locating these, targeting the Run 3 incarnation of LHCb, have been described at conferences in 2019 and 2020. In the past year we have made significant progress in a variety of related areas. Using two newer Kernel Density Estimators (KDEs) as input feature sets improves the fidelity of the models, as does using full LHCb simulation rather than the “toy Monte Carlo” originally (and still) used to develop models. We have also built a deep learning model to calculate the KDEs from track information. Connecting a tracks-to-KDE model to a KDE-to-hists model used to find PVs provides a proof-of-concept that a single deep learning model can use track information to find PVs with high efficiency and high fidelity. We have studied a variety of models systematically to understand how variations in their architectures affect performance. While the studies reported here are specific to the LHCb geometry and operating conditions, the results suggest that the same approach could be used by the ATLAS and CMS experiments.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

scholarly journals Prediction of Merchandise Sales on E-Commerce Platforms Based on Data Mining and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaoting Yin ◽  
Xiaosha Tao
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Research Process ◽  
Deep Learning Algorithm ◽  
Sales Prediction ◽  
Different Types ◽  
Online Business ◽  
Product Sales ◽  
Deep Learning Model

Online business has grown exponentially during the last decade, and the industries are focusing on online business more than before. However, just setting up an online store and starting selling might not work. Different machine learning and data mining techniques are needed to know the users’ preferences and know what would be best for business. According to the decision-making needs of online product sales, combined with the influencing factors of online product sales in various industries and the advantages of deep learning algorithm, this paper constructs a sales prediction model suitable for online products and focuses on evaluating the adaptability of the model in different types of online products. In the research process, the full connection model is compared with the training results of CNN, which proves the accuracy and generalization ability of CNN model. By selecting the non-deep learning model as the comparison baseline, the performance advantages of CNN model under different categories of products are proved. In addition, the experiment concludes that the unsupervised pretrained CNN model is more effective and adaptable in sales forecasting.

scholarly journals https://jusst.org/developing-the-rural-economy-structure-in-agriculture-towards-the-betterment-of-indias-economy/

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Amit Doegar ◽  
◽  
Maitreyee Dutta ◽  
Gaurav Kumar ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Rural Economy ◽  
Learning Algorithm ◽  
Learning Model ◽  
Image Features ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Testing Dataset ◽  
Deep Learning Model

In the present scenario, one of the threats of trust on images for digital and online applications as well as on social media. Individual’s reputation can be turnish using misinformation or manipulation in the digital images. Image forgery detection is an approach for detection and localization of forged components in the image which is manipulated. For effective image forgery detection, an adequate number of features are required which can be accomplished by a deep learning model, which does not require manual feature engineering or handcraft feature approaches. In this paper we have implemented GoogleNet deep learning model to extract the image features and employ Random Forest machine learning algorithm to detect whether the image is forged or not. The proposed approach is implemented on the publicly available benchmark dataset MICC-F220 with k-fold cross validation approach to split the dataset into training and testing dataset and also compared with the state-of-the-art approaches.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery

2021 ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods: We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results: The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion: IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

scholarly journals TickPhone App: A Smartphone Application for Rapid Tick Identification Using Deep Learning

2021 ◽  
Vol 11 (16) ◽  
pp. 7355
Author(s):  
Zhiheng Xu ◽  
Xiong Ding ◽  
Kun Yin ◽  
Ziyue Li ◽  
Joan A. Smyth ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lyme Disease ◽  
Health Care Providers ◽  
Tick Species ◽  
Disease Risk ◽  
Learning Algorithm ◽  
Learning Model ◽  
Care Providers ◽  
Accurate Identification ◽  
Deep Learning Model

Tick species are considered the second leading vector of human diseases. Different ticks can transmit a variety of pathogens that cause various tick-borne diseases (TBD), such as Lyme disease. Currently, it remains a challenge to diagnose Lyme disease because of its non-specific symptoms. Rapid and accurate identification of tick species plays an important role in predicting potential disease risk for tick-bitten patients, and ensuring timely and effective treatment. Here, we developed, optimized, and tested a smartphone-based deep learning algorithm (termed “TickPhone app”) for tick identification. The deep learning model was trained by more than 2000 tick images and optimized by different parameters, including normal sizes of images, deep learning architectures, image styles, and training–testing dataset distributions. The optimized deep learning model achieved a training accuracy of ~90% and a validation accuracy of ~85%. The TickPhone app was used to identify 31 independent tick species and achieved an accuracy of 95.69%. Such a simple and easy-to-use TickPhone app showed great potential to estimate epidemiology and risk of tick-borne disease, help health care providers better predict potential disease risk for tick-bitten patients, and ultimately enable timely and effective medical treatment for patients.

Automation of quantifying and mapping of stromal and malignant cells in pancreatic adenocarcinoma using deep learning.

2018 ◽  
Vol 36 (4_suppl) ◽  
pp. 266-266
Author(s):  
Sunyoung S. Lee ◽  
Jin Cheon Kim ◽  
Jillian Dolan ◽  
Andrew Baird
Keyword(s):  
Deep Learning ◽  
Pancreatic Adenocarcinoma ◽  
Learning Algorithm ◽  
Learning Model ◽  
The Cancer Genome Atlas ◽  
Malignant Cells ◽  
Mutational Burden ◽  
Response To Chemotherapy ◽  
Significant Difference ◽  
Deep Learning Model

266 Background: The characteristic histological feature of pancreatic adenocarcinoma (PAD) is extensive desmoplasia alongside leukocytes and cancer-associated fibroblasts. Desmoplasia is a known barrier to the absorption and penetration of therapeutic drugs. Stromal cells are key elements for a clinical response to chemotherapy and immunotherapy, but few models exist to analyze the spatial and architectural elements that compose the complex tumor microenvironment in PAD. Methods: We created a deep learning algorithm to analyze images and quantify cells and fibrotic tissue. Histopathology slides of PAD patients (pts) were then used to automate the recognition and mapping of adenocarcinoma cells, leukocytes, fibroblasts, and degree of desmoplasia, defined as the ratio of the area of fibrosis to that of the tumor gland. This information was correlated with mutational burden, defined as mutations (mts) per megabase (mb) of each pt. Results: The histopathology slides (H&E stain) of 126 pts were obtained from The Cancer Genome Atlas (TCGA) and analyzed with the deep learning model. Pt with the largest mutational burden (733 mts/mb, n = 1 pt) showed the largest number of leukocytes (585/mm2). Those with the smallest mutational burden (0 mts/mb, n = 16 pts) showed the fewest leukocytes (median, 14/mm2). Mutational burden was linearly proportional to the number of leukocytes (R2 of 0.7772). The pt with a mutational burden of 733 was excluded as an outlier. No statistically significant difference in the number of fibroblasts, degree of desmoplasia, or thickness of the first fibrotic layer (the smooth muscle actin-rich layer outside of the tumor gland), was found among pts of varying mutational burden. The median distance from a tumor gland to a leukocyte was inversely proportional to the number of leukocytes in a box of 1 mm2 with a tumor gland at the center. Conclusions: A deep learning model enabled automated quantification and mapping of desmoplasia, stromal and malignant cells, revealing the spatial and architectural relationship of these cells in PAD pts with varying mutational burdens. Further biomarker driven studies in the context of immunotherapy and anti-fibrosis are warranted.

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