Deep learning–based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC

2021 ◽  
Author(s):  
Dong Wook Kim ◽  
Gaeun Lee ◽  
So Yeon Kim ◽  
Geunhwi Ahn ◽  
June-Goo Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Risk ◽  
Hepatic Malignancy ◽  
Multiphase Ct

Identifying High Risk of Atherosclerosis Using Deep Learning and Ensemble Learning

2021 ◽  
Author(s):  
Hedieh Hashem Olhosseiny ◽  
Mohammadsalar Mirzaloo ◽  
Miodrag Bolic ◽  
Hilmi R. Dajani ◽  
Voicu Groza ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Risk ◽  
Ensemble Learning

Abstract 13102: Prediction of Incident AF With Deep Learning Can Identify Patients at High Risk for AF-related Stroke

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
John Pfeifer ◽  
Sushravya M Raghunath ◽  
Alvaro Ulloa ◽  
Arun Nemani ◽  
Tanner Carbonati ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Risk ◽  
Risk Prediction ◽  
Model Performance ◽  
Prior History ◽  
Test Set ◽  
Stroke Registry ◽  
Number Needed To Screen ◽  
Operating Points ◽  
New Onset

Background: Atrial fibrillation (AF) is associated with stroke, especially when AF goes undetected. Deep neural networks (DNN) can predict incident AF from a 12-lead resting ECG. We hypothesize that use of a DNN to predict new onset AF from an ECG may identify patients at risk of sustaining a potentially preventable AF-related stroke. Methods: We trained a DNN model to predict new-onset AF using 382,604 ECGs prior to 2010. We then evaluated the model performance on a test set of ECGs from 2010 through 2014 linked to patients in an institutional stroke registry. There were 181,969 patients in the test set with at least one ECG and no prior history of AF. Of those patients 3,497 (1.9%) had a stroke following an ECG that did not show AF. Within the set of patients with stroke, 375 had the stroke within 3 years of the ECG and were diagnosed with new AF between -3 and 365 days of the stroke. We considered these potentially preventable AF-related strokes. We report the sensitivity and positive predictive value (PPV) of the model for appropriately risk stratifying these 375 patients who sustained a potentially preventable AF-related stroke. Results: We used F β scores to identify different risk prediction thresholds (operating points) for the model. Operating points chosen by F 0.5 , F 1 , and F 2 scores identified 4, 12, and 21% of the population as high risk for the development of AF within 1 year (Figure 1). Screening 1, 4, 12, and 21% of the overall population resulted in PPV of 28, 21, 15, and 12%, respectively, for identification of new onset AF in one year. Using those same thresholds yielded sensitivities of 4, 17, 45, and 62% for identifying potentially preventable AF-related strokes. The different risk prediction thresholds resulted in a low (120-162) number needed to screen to detect one potentially preventable AF-related stroke at 3 years. Conclusions: Use of a deep learning model to predict new onset AF may identify patients at high risk of sustaining a potentially preventable AF-related stroke.

Toward robust mammography-based models for breast cancer risk

2021 ◽  
Vol 13 (578) ◽  
pp. eaba4373 ◽  
Author(s):  
Adam Yala ◽  
Peter G. Mikhael ◽  
Fredrik Strand ◽  
Gigin Lin ◽  
Kevin Smith ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Massachusetts General Hospital ◽  
The United States ◽  
University Hospital ◽  
Chang Gung Memorial Hospital ◽  
Risk Models

Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines. To bring deep learning risk models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. We developed Mirai, a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and tested on held-out test sets from MGH, Karolinska University Hospital in Sweden, and Chang Gung Memorial Hospital (CGMH) in Taiwan, obtaining C-indices of 0.76 (95% confidence interval, 0.74 to 0.80), 0.81 (0.79 to 0.82), and 0.79 (0.79 to 0.83), respectively. Mirai obtained significantly higher 5-year ROC AUCs than the Tyrer-Cuzick model (P < 0.001) and prior deep learning models Hybrid DL (P < 0.001) and Image-Only DL (P < 0.001), trained on the same dataset. Mirai more accurately identified high-risk patients than prior methods across all datasets. On the MGH test set, 41.5% (34.4 to 48.5) of patients who would develop cancer within 5 years were identified as high risk, compared with 36.1% (29.1 to 42.9) by Hybrid DL (P = 0.02) and 22.9% (15.9 to 29.6) by the Tyrer-Cuzick model (P < 0.001).

Transjugular Intrahepatic Portosystemic Shunts in High-Risk Patients

2020 ◽  
Vol 04 (02) ◽  
pp. 148-156
Author(s):  
David S. Shin ◽  
Hong Vo ◽  
Guy Johnson ◽  
Raimund Pichler ◽  
Scott W. Biggins
Keyword(s):  
High Risk ◽  
Cardiac Disease ◽  
Poor Prognosis ◽  
Hepatic Malignancy ◽  
Safety And Efficacy ◽  
End Stage Liver Disease ◽  
The Past ◽  
Portosystemic Shunts ◽  
Risk Patients ◽  
End Stage

AbstractCirrhosis with complications of portal hypertension portends a poor prognosis. Transjugular intrahepatic portosystemic shunts (TIPS) can successfully treat some of these complications in select patients. While the safety and efficacy of TIPS have improved significantly over the past decade, certain patients are categorized as high-risk based on various demographic, laboratory, and comorbid factors. Herein, we provide an in-depth review of TIPS in these settings, including high model for end-stage liver disease score, hepatic malignancy, advanced age, cardiac disease, renal dysfunction, and pregnancy, and discuss their impact on patient selection and procedural considerations.

Deep learning for improved prediction of late-onset cardiomyopathy among childhood cancer survivors: A report from the St. Jude Lifetime Cohort (SJLIFE).

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 10545-10545
Author(s):  
Fatma Gunturkun ◽  
Robert L Davis ◽  
Gregory T. Armstrong ◽  
John L. Jefferies ◽  
Kirsten K. Ness ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Risk ◽  
Cancer Survivors ◽  
Childhood Cancer ◽  
Childhood Cancer Survivors ◽  
Learning Approaches ◽  
Baseline Evaluation ◽  
Sensitivity Specificity ◽  
Ecg Data

10545 Background: Early identification of survivors at high risk for treatment-induced cardiomyopathy may allow for prevention and/or early intervention. We utilized deep learning methods using COG guideline-recommended baseline electrocardiography (ECG) to improve prediction of future cardiomyopathy. Methods: SJLIFE is a cohort of 5-year clinically assessed childhood cancer survivors including baseline ECG measurements. Development of cardiomyopathy was identified from clinical and echocardiographic measurement using CTCAE criteria (grade 3-4). We applied deep learning approaches to ECG, treatment exposure and demographic data obtained at baseline SJLIFE assessment. We trained a cascaded model combining a 12-layer 1D convolutional neural network to extract features from waveform ECG signals with a 2-layer dense neural network to embed features from other phenotypic data in tabular format to determine if use of deep learning with ECG data could improve prediction of cardiomyopathy. Results: Among 1,218 subjects (median age 31.7 years, range 18.4-66.4) without cardiomyopathy at baseline evaluation, 616 (51%) were male, 1,041 (85%) white, 157 (13%) African American and 792 (65%) were survivors of lymphoma/leukemia. Follow-up averaged 5 (0.5 to 9) years from baseline examination. Mean chest radiation dose was 1350 cGy (range 0 to 6,200 cGy) and mean cumulative anthracycline dose was 191 mg/m2 (range o to 734 mg/m2). A total of 114 (9.4%) survivors developed cardiomyopathy after baseline. A cascaded deep learning model built on a training set (N = 974 participants) classified cardiomyopathy in the test set (N = 244 participants) using both clinical and ECG data with a sensitivity of 70%, specificity of 73%, and AUC of 0.74 (95% CI 0.63-0.85), compared to a model using clinical data alone (sensitivity 61%, specificity 62%, and AUC 0.67, 95% CI 0.56-0.79). In subgroup analyses, models predicting cardiomyopathy within 0-4 years following baseline had a sensitivity, specificity, and AUC of 77%, 78%, and 0.78 (0.65-0.91), respectively. When predicting cardiomyopathy 5-9 years following baseline, model performance dropped to a sensitivity, specificity, and AUC of 70%, 70%, and 0.68 (0.50-0.87), respectively. Conclusions: Deep learning using ECG at baseline evaluation significantly improved prediction of cardiomyopathy in childhood cancer survivors at high risk for cardiomyopathy. Future directions will incorporate deep learning approaches to echocardiography to further improve prediction.

scholarly journals A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis

2020 ◽  
Vol 56 (2) ◽  
pp. 2000775 ◽  
Author(s):  
Shuo Wang ◽  
Yunfei Zha ◽  
Weimin Li ◽  
Qingxia Wu ◽  
Xiaohu Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
High Risk ◽  
Learning System ◽  
Medical Resource ◽  
Early Prevention ◽  
Prognostic Analysis ◽  
Computed Tomography Images ◽  
Risk Patients ◽  
Fully Automatic

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

scholarly journals Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0208924 ◽  
Author(s):  
Valerio Maggio ◽  
Marco Chierici ◽  
Giuseppe Jurman ◽  
Cesare Furlanello
Keyword(s):  
Deep Learning ◽  
High Risk ◽  
Clinical Algorithm
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