scholarly journals Deep-learning model for screening sepsis using electrocardiography

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Joon-myoung Kwon ◽  
Ye Rang Lee ◽  
Min-Seung Jung ◽  
Yoon-Ji Lee ◽  
Yong-Yeon Jo ◽  
...  
Keyword(s):  
Septic Shock ◽  
Deep Learning ◽  
Confidence Interval ◽  
Characteristic Curve ◽  
External Validation ◽  
Medical Emergency ◽  
Validation Dataset ◽  
Internal Validation ◽  
Significant Difference ◽  
Sepsis And Septic Shock

Abstract Background Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG). Methods This retrospective cohort study included 46,017 patients who were admitted to two hospitals. A total of 1,548 and 639 patients had sepsis and septic shock, respectively. The DLM was developed using 73,727 ECGs from 18,142 patients, and internal validation was conducted using 7774 ECGs from 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs from 20,101 patients from another hospital to verify the applicability of the DLM across centers. Results During the internal and external validations, the area under the receiver operating characteristic curve (AUC) of the DLM using 12-lead ECG was 0.901 (95% confidence interval, 0.882–0.920) and 0.863 (0.846–0.879), respectively, for screening sepsis and 0.906 (95% confidence interval (CI), 0.877–0.936) and 0.899 (95% CI, 0.872–0.925), respectively, for detecting septic shock. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs was 0.845–0.882. A sensitivity map revealed that the QRS complex and T waves were associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who were admitted with an infectious disease, and the AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793–0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs. 0.574, p < 0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs. 0.725, p = 0.018). Conclusions The DLM delivered reasonable performance for sepsis screening using 12-, 6-, and single-lead ECGs. The results suggest that sepsis can be screened using not only conventional ECG devices but also diverse life-type ECG machines employing the DLM, thereby preventing irreversible disease progression and mortality.

scholarly journals Deep Learning Model for Screening Sepsis Using Electrocardiography

2021 ◽  
Author(s):  
Joon-myoung Kwon ◽  
Ye Rang Lee ◽  
Min-Seung Jung ◽  
Yoon-Ji Lee ◽  
Yong-Yeon Jo ◽  
...  
Keyword(s):  
Septic Shock ◽  
Deep Learning ◽  
Characteristic Curve ◽  
External Validation ◽  
Medical Emergency ◽  
Validation Dataset ◽  
Internal Validation ◽  
Significant Difference ◽  
Life Threatening ◽  
Sepsis And Septic Shock

Abstract Background: Sepsis is a life-threatening organ dysfunction and is a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, it is difficult to screen the occurrence of sepsis. In this study, we propose an artificial intelligence based on deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG).Methods: This retrospective cohort study included 46,017 patients who admitted to two hospitals. 1,548 and 639 patients underwent sepsis and septic shock. The DLM was developed using 73,727 ECGs of 18,142 patients and internal validation was conducted using 7,774 ECGs of 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs of 20,101 patients from another hospital to verify the applicability of the DLM across centers.Results: During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of an DLM using 12-lead ECG for screening sepsis were 0.901 (95% confidence interval 0.882–0.920) and 0.863 (0.846–0.879), respectively. During internal and external validation, AUC of an DLM for detecting septic shock were 0.906 (95% CI = 0.877–0.936) and 0.899 (95% CI = 0.872–0.925), respectively. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs were 0.845–0.882. A sensitivity map showed that the QRS complex and T wave was associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who admitted with infectious disease, The AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793–0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs 0.574, p<0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs 0.725, p=0.018).Conclusions: The DLM demonstrated reasonable performance for screening sepsis using 12-, 6-, and single-lead ECG. The results suggest that sepsis can be screened using not only conventional ECG devices, but also diverse life-type ECG machine employing the DLM, thereby preventing irreversible disease progression and mortality.

scholarly journals Predicting Sex from Retinal Fundus Photographs Using Automated Deep Learning

2021 ◽  
Author(s):  
Edward Korot ◽  
Nikolas Pontikos ◽  
Xiaoxuan Liu ◽  
Siegfried K Wagner ◽  
Livia Faes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
Model Performance ◽  
Validation Dataset ◽  
Internal Validation ◽  
Fundus Photographs ◽  
The Uk ◽  
Retinal Fundus

Abstract Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was performed on 252 fundus photos from a tertiary ophthalmic referral center. For internal validation, the area under the receiver operating characteristic curve (AUROC) of the code free deep learning (CFDL) model was 0.93. Sensitivity, specificity, positive predictive value (PPV) and accuracy (ACC) were 88.8%, 83.6%, 87.3% and 86.5%, and for external validation were 83.9%, 72.2%, 78.2% and 78.6% respectively. Clinicians are currently unaware of distinct retinal feature variations between males and females, highlighting the importance of model explainability for this task. The model performed significantly worse when foveal pathology was present in the external validation dataset, ACC: 69.4%, compared to 85.4% in healthy eyes, suggesting the fovea is a salient region for model performance OR (95% CI): 0.36 (0.19, 0.70) p = 0.0022. Automated machine learning (AutoML) may enable clinician-driven automated discovery of novel insights and disease biomarkers.

scholarly journals Predicting sex from retinal fundus photographs using automated deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Edward Korot ◽  
Nikolas Pontikos ◽  
Xiaoxuan Liu ◽  
Siegfried K. Wagner ◽  
Livia Faes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
Model Performance ◽  
Validation Dataset ◽  
Internal Validation ◽  
Fundus Photographs ◽  
The Uk ◽  
Retinal Fundus

AbstractDeep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was performed on 252 fundus photos from a tertiary ophthalmic referral center. For internal validation, the area under the receiver operating characteristic curve (AUROC) of the code free deep learning (CFDL) model was 0.93. Sensitivity, specificity, positive predictive value (PPV) and accuracy (ACC) were 88.8%, 83.6%, 87.3% and 86.5%, and for external validation were 83.9%, 72.2%, 78.2% and 78.6% respectively. Clinicians are currently unaware of distinct retinal feature variations between males and females, highlighting the importance of model explainability for this task. The model performed significantly worse when foveal pathology was present in the external validation dataset, ACC: 69.4%, compared to 85.4% in healthy eyes, suggesting the fovea is a salient region for model performance OR (95% CI): 0.36 (0.19, 0.70) p = 0.0022. Automated machine learning (AutoML) may enable clinician-driven automated discovery of novel insights and disease biomarkers.

scholarly journals Deep Learning-Based Breast Cancer Diagnosis at Ultrasound: Initial Application of Weakly-Supervised Algorithm Without Image Annotation Original Research

2021 ◽  
Author(s):  
Jaeil Kim ◽  
Hye Jung Kim ◽  
Chanho Kim ◽  
Jin Hwa Lee ◽  
Keum Won Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Image Annotation ◽  
Characteristic Curve ◽  
External Validation ◽  
Region Of Interest ◽  
Breast Cancer Diagnosis ◽  
Original Research ◽  
Internal Validation ◽  
Weakly Supervised

Abstract Conventional deep learning (DL) algorithm requires full supervision of annotating the region of interest (ROI) that is laborious and often biased. We aimed to develop a weakly-supervised DL algorithm that diagnosis breast cancer at ultrasound without image annotation. Weakly-supervised DL algorithms were implemented with three networks (VGG16, ResNet34, and GoogLeNet) and trained using 1000 unannotated US images (500 benign and 500 malignant masses). Two sets of 200 images (100 benign and 100 malignant masses) were used for internal and external validation sets. For comparison with fully-supervised algorithms, ROI annotation was performed manually and automatically. Diagnostic performances were calculated as the area under the receiver operating characteristic curve (AUC). Using the class activation map, we determined how accurately the weakly-supervised DL algorithms localized the breast masses. For internal validation sets, the weakly-supervised DL algorithms achieved excellent diagnostic performances, with AUC values of 0.92–0.96, which were not statistically different (all Ps > 0.05) from those of fully-supervised DL algorithms with either manual or automated ROI annotation (AUC, 0.92–0.96). For external validation sets, the weakly-supervised DL algorithms achieved AUC values of 0.86–0.90, which were not statistically different (Ps > 0.05) or higher (P = 0.04, VGG16 with automated ROI annotation) from those of fully-supervised DL algorithms (AUC, 0.84–0.92). In internal and external validation sets, weakly-supervised algorithms could localize 100% of malignant masses, except for ResNet34 (98%). The weakly-supervised DL algorithms developed in the present study were feasible for US diagnosis of breast cancer with well-performing localization and differential diagnosis.

scholarly journals A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong-Soo Baek ◽  
Sang-Chul Lee ◽  
Wonik Choi ◽  
Dae-Hyeok Kim
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Sinus Rhythm ◽  
Learning Algorithm ◽  
Normal Sinus Rhythm ◽  
Characteristic Curve ◽  
External Validation ◽  
Digital Data ◽  
Internal Validation ◽  
Optimal Interval

AbstractAtrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.

scholarly journals Deep Learning Model for Detection of Hypoalbuminemia Using Electrocardiography

2021 ◽  
Author(s):  
Joon-myoung Kwon ◽  
Soo Youn Lee ◽  
Yoon-Ji Lee ◽  
Yong-Yeon Jo ◽  
Min-Seung Jung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
External Validation ◽  
Patient Data ◽  
Albumin Concentration ◽  
Historical Cohort ◽  
Internal Validation ◽  
Patient Deterioration ◽  
Risk Patients ◽  
Deep Learning Model

Background: Albumin has a pivotal role in the homeostasis of osmotic pressure and is associated with cardiovascular, nephrotic, hepatic, and nutritional diseases. The detection of hypoalbuminemia is a cornerstone for diagnosis of hidden diseases and patient deterioration. We developed and validated a deep-learning-based model (DLM) for detection of hypoalbuminemia using electrocardiography (ECG). Methods: This historical cohort study included data from consecutive patients from two hospitals. The patient data in one hospital were divided into development (82,499 ECGs from 54,248 patients) and internal validation (20,664 ECGs from 20,664 patients) datasets, whereas the patient data in the other hospital were included in only an external validation (37,421 ECGs from 37,421 patients) dataset. An DLM was developed using a 12-lead ECG signal, age, and sex from the development dataset. The endpoint was hypoalbuminemia, defined by serum albumin concentration below 3.5 g/dL. Results: During the internal and external validations, the areas under the receiver operating characteristic curve of the DLM for the detection of hypoalbuminemia were 0.887 (0.877&ndash;0.897) and 0.888 (0.880&ndash;0.896), respectively. Among the 27,400 individuals without hypoalbuminemia at the initial laboratory exam, those identified by the DLM as higher-risk patients had a significantly larger change in developing hypoalbuminemia than those in the low-risk group (7.09% vs. 1.01%, p &lt; 0.001) during 24 months. The sensitivity map showed that the DLM focused on the T wave and QRS complex for the detection of hypoalbuminemia. Conclusions: The DLM exhibited a high accuracy for hypoalbuminemia detection and prediction using 12-, 6-, and single-lead ECGs.

scholarly journals Weakly-supervised deep learning for ultrasound diagnosis of breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jaeil Kim ◽  
Hye Jung Kim ◽  
Chanho Kim ◽  
Jin Hwa Lee ◽  
Keum Won Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Operating Characteristic ◽  
Image Annotation ◽  
Characteristic Curve ◽  
External Validation ◽  
Region Of Interest ◽  
Internal Validation ◽  
Breast Masses ◽  
Weakly Supervised

AbstractConventional deep learning (DL) algorithm requires full supervision of annotating the region of interest (ROI) that is laborious and often biased. We aimed to develop a weakly-supervised DL algorithm that diagnosis breast cancer at ultrasound without image annotation. Weakly-supervised DL algorithms were implemented with three networks (VGG16, ResNet34, and GoogLeNet) and trained using 1000 unannotated US images (500 benign and 500 malignant masses). Two sets of 200 images (100 benign and 100 malignant masses) were used for internal and external validation sets. For comparison with fully-supervised algorithms, ROI annotation was performed manually and automatically. Diagnostic performances were calculated as the area under the receiver operating characteristic curve (AUC). Using the class activation map, we determined how accurately the weakly-supervised DL algorithms localized the breast masses. For internal validation sets, the weakly-supervised DL algorithms achieved excellent diagnostic performances, with AUC values of 0.92–0.96, which were not statistically different (all Ps > 0.05) from those of fully-supervised DL algorithms with either manual or automated ROI annotation (AUC, 0.92–0.96). For external validation sets, the weakly-supervised DL algorithms achieved AUC values of 0.86–0.90, which were not statistically different (Ps > 0.05) or higher (P = 0.04, VGG16 with automated ROI annotation) from those of fully-supervised DL algorithms (AUC, 0.84–0.92). In internal and external validation sets, weakly-supervised algorithms could localize 100% of malignant masses, except for ResNet34 (98%). The weakly-supervised DL algorithms developed in the present study were feasible for US diagnosis of breast cancer with well-performing localization and differential diagnosis.

scholarly journals Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score

2021 ◽  
Author(s):  
Rong Hua ◽  
Jianhao Xiong ◽  
Gail Li ◽  
Yidan Zhu ◽  
Zongyuan Ge ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Score ◽  
Learning Algorithm ◽  
External Validation ◽  
Population Based ◽  
Validation Dataset ◽  
Internal Validation ◽  
Deep Learning Algorithm ◽  
Dementia Risk ◽  
Fundus Photographs

AbstractImportanceThe Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognized tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional information and fasting blood draw. Consequently, effective, convenient and noninvasive tool for screening individuals with high dementia risk in large population-based settings is urgently needed.ObjectiveTo develop and validate a deep learning algorithm using retinal fundus photographs for estimating the CAIDE dementia risk score and identifying individuals with high dementia risk.DesignA deep learning algorithm trained via fundus photographs was developed, validated internally and externally with cross-sectional design.SettingPopulation-based.ParticipantsA health check-up population with 271,864 adults were randomized into a development dataset (95%) and an internal validation dataset (5%). The external validation used data from the Beijing Research on Ageing and Vessel (BRAVE) with 1,512 individuals.ExposuresThe estimated CAIDE dementia risk score generated from the algorithm.Main Outcome and MeasureThe algorithm’s performance for identifying individuals with high dementia risk was evaluated by area under the receiver operating curve (AUC) with 95% confidence interval (CI).ResultsThe study involved 258,305 participants (mean aged 42.1 ± 13.4 years, men: 52.7%) in development, 13,559 (mean aged 41.2 ± 13.3 years, men: 52.5%) in internal validation, and 1,512 (mean aged 59.8 ± 7.3 years, men: 37.1%) in external validation. The adjusted coefficient of determination (R2) between the estimated and actual CAIDE dementia risk score was 0.822 in the internal and 0.300 in the external validations, respectively. The algorithm achieved an AUC of 0.931 (95%CI, 0.922–0.939) in the internal validation group and 0.782 (95%CI, 0.749–0.815) in the external group. Besides, the estimated CAIDE dementia risk score was significantly associated with both comprehensive cognitive function and specific cognitive domains.Conclusions and RelevanceThe present study demonstrated that the deep learning algorithm trained via fundus photographs could well identify individuals with high dementia risk in a population-based setting. Our findings suggest that fundus photography may be utilized as a noninvasive and more expedient method for dementia risk stratification.Key PointsQuestionCan a deep learning algorithm based on fundus images estimate the CAIDE dementia risk score and identify individuals with high dementia risk?FindingsThe algorithm developed by fundus photographs from 258,305 check-up participants could well identify individuals with high dementia risk, with area under the receiver operating characteristic curve of 0.931 in internal validation and 0.782 in external validation dataset, respectively. Besides, the estimated CAIDE dementia risk score generated from the algorithm exhibited significant association with cognitive function.MeaningThe deep learning algorithm based on fundus photographs has potential to screen individuals with high dementia risk in population-based settings.

scholarly journals Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Wenying Zhou ◽  
Yang Yang ◽  
Cheng Yu ◽  
Juxian Liu ◽  
Xingxing Duan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Biliary Atresia ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Learning Model ◽  
Video Sequences ◽  
Validation Dataset ◽  
Patient Level ◽  
Deep Learning Model

AbstractIt is still challenging to make accurate diagnosis of biliary atresia (BA) with sonographic gallbladder images particularly in rural area without relevant expertise. To help diagnose BA based on sonographic gallbladder images, an ensembled deep learning model is developed. The model yields a patient-level sensitivity 93.1% and specificity 93.9% [with areas under the receiver operating characteristic curve of 0.956 (95% confidence interval: 0.928-0.977)] on the multi-center external validation dataset, superior to that of human experts. With the help of the model, the performances of human experts with various levels are improved. Moreover, the diagnosis based on smartphone photos of sonographic gallbladder images through a smartphone app and based on video sequences by the model still yields expert-level performances. The ensembled deep learning model in this study provides a solution to help radiologists improve the diagnosis of BA in various clinical application scenarios, particularly in rural and undeveloped regions with limited expertise.

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