Development and Validation of a Deep Learning System for Staging                     Liver Fibrosis by Using Contrast Agent–enhanced CT Images in the                     Liver

Choi, Kyu Jin; Jang, Jong Keon; Lee, Seung Soo; Sung, Yu Sub; Shim, Woo Hyun; Kim, Ho Sung; Yun, Jun-Won; Choi, Jin Young; Lee, Yedaun; Kang, Bo Kyeong; Kim, Jin Hee; Kim, So Yeon; Yu, Eun Sil

doi:10.1148/radiol.2018180763

Cited by 170 publications

(121 citation statements)

References 31 publications

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“…While automated segmentation tasks involving CT of the abdomen have been described [33][34][35] , few deep learning models for automated classification tasks involving abdominopelvic CTs have been reported 36 . In particular, no automated detection tasks involving clinically emergent focal abnormalities such as appendicitis have been reported.…”

Section: Discussionmentioning

confidence: 99%

AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining

Rajpurkar

Park

Irvin

et al. 2020

Sci Rep

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The development of deep learning algorithms for complex tasks in digital medicine has relied on the availability of large labeled training datasets, usually containing hundreds of thousands of examples. The purpose of this study was to develop a 3D deep learning model, AppendiXNet, to detect appendicitis, one of the most common life-threatening abdominal emergencies, using a small training dataset of less than 500 training CT exams. We explored whether pretraining the model on a large collection of natural videos would improve the performance of the model over training the model from scratch. AppendiXNet was pretrained on a large collection of YouTube videos called Kinetics, consisting of approximately 500,000 video clips and annotated for one of 600 human action classes, and then fine-tuned on a small dataset of 438 CT scans annotated for appendicitis. We found that pretraining the 3D model on natural videos significantly improved the performance of the model from an AUC of 0.724 (95% CI 0.625, 0.823) to 0.810 (95% CI 0.725, 0.895). The application of deep learning to detect abnormalities on CT examinations using video pretraining could generalize effectively to other challenging cross-sectional medical imaging tasks when training data is limited.Appendicitis is one of the most common life-threatening abdominal emergencies, with lifetime risk of ranging from 5-10% and an annual incidence of 90-140 per 100,000 individuals 1-3 . Treatment, via appendectomy, remains the most frequently performed surgical intervention in the world 4 . Computed tomography (CT) imaging of the abdomen is the primary imaging modality to make the diagnosis of appendicitis, exclude other etiologies of acute abdominal pain, and allow for surgical planning and intervention. Because a delay in the time between CT imaging diagnosis and surgical appendectomy can worsen patient outcomes 5-9 , there is increasing pressure on hospital systems to provide 24-7 access to advanced imaging and to ensure that the results of urgent findings, such as appendicitis, are rapidly and accurately communicated to the referring physician 10,11 . However, providing rapid and accurate diagnostic imaging is increasingly difficult to sustain for many medical systems and radiology groups as utilization has rapidly expanded; for example, the CT utilization rate in the emergency room increased from 41 per 1000 in 2000 to 74 per 1000 in 2010, with abdominal CTs accounting for over half of this volume 12,13 . Development of automated systems could potentially help improve diagnostic accuracy and reduce time to diagnosis, thereby improving the quality and efficiency of patient care.Recent advancements in deep learning have enabled algorithms to automate a wide variety of medical tasks [14][15][16][17][18][19][20] . A key aspect for the success of deep learning models on these tasks is the availability of large labeled datasets of medical images 21 , usually containing hundreds of thousands of examples. However, it is challenging to curate large labeled medical imaging dat...

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Section: Discussionmentioning

confidence: 99%

AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining

Rajpurkar

Park

Irvin

et al. 2020

Sci Rep

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“…Data augmentation may also be required to overcome the potential problems of data imbalance. If the size of the training data is imbalanced across different classes, a classification algorithm may have poor classification accuracy for the minority classes (45). This may be prevented by data augmentation for those classes.…”

Section: Training Of a Deep Learning Algorithmmentioning

confidence: 99%

“…However, the use of a small test dataset (100 patients) and lack of any external validation limited the generalizability of their study results. Choi et al (45) reported the use of a deep learning algorithm for fully automated liver fibrosis staging using portal venous phase CT images. Using a large training dataset (7491 patients) and internal and external test data (891 patients), these authors reported a high accuracy (AUCs, 0.95-0.97) of the deep learning algorithm in liver fibrosis staging, kjronline.org surpassing that of the serum fibrosis indices and visual image analyses by radiologists.…”

Section: Liver Fibrosis Stagingmentioning

confidence: 99%

Radiomics and Deep Learning: Hepatic Applications

2020

Self Cite

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Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.

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“…Choi, K. J., Jang et.al [1] built up a CT-based DLS for staging of liver fibrosis by utilizing a dataset containing CT images of 7461 liver fibrosis patients of portal venous phase. The working of the DLS was assessed on a test data set containing 891 Patients.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Classifier Accuracy in Liver Disease Diagnosis using a Novel Multi Layer Feed Forward Deep Neural Network

Murty¹,

Kumar²

2019

IJRTE

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 Abstract: Classification techniques are often used for predicting Liver diseases and assist doctors in early detection of liver diseases. As per studies in the past and our experiments, conventional classification algorithms are found to be less accurate in predicting liver diseases. Therefore, there is a need for sophisticated classifiers in this area. For many medical applications, including Liver Diseases, Deep Neural Networks (DNNs) are used but the accuracies are not satisfactory. Deep Neural Network training is a time taking procedure, particularly if the hidden layers and nodes are more. Most of the times it leads to over fitting and the classifier does not perform well on unseen data samples .We, in this paper, tuned a Multi Layer Feed Forward Deep Neural Network (MLFFDNN) by fitting appropriate number of hidden layer and nodes, dropout function after each hidden layer to avoid over fitting, loss functions, bias, learning rate and activation functions for more accurate liver disease predictions. We used a balanced data set containing 882 samples. The data is collected from north coastal districts of Andhra Pradesh hospitals, India. The training process is carried out for 400 epochs and finally It is .observed that our model exhibited 98% accuracy at epoch 363 which is more than the performance of Neural Network models tuned till now by machine learning researchers and also some regularly used classification algorithms like Support Vector Machines (SVM), Naive Bayes (NB), C4.5 Decision Tree, Random Belief Networks and Alternating Decision Trees (ADT) .

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Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent–enhanced CT Images in the Liver

Cited by 170 publications

References 31 publications

AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining

AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining

Radiomics and Deep Learning: Hepatic Applications

Enhanced Classifier Accuracy in Liver Disease Diagnosis using a Novel Multi Layer Feed Forward Deep Neural Network

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