Use of a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver

Nguyen, Trong; Podkowa, Anthony; Park, Trevor H; Miller, Rita J.; Minh, N.; Oelze, Michael L.

doi:10.1016/j.ultrasmedbio.2020.10.025

Cited by 18 publications

(27 citation statements)

References 21 publications

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“…The investigators obtained a 97% sensitivity and 94% specificity, and the positive predictive value was up to 97%, which demonstrated that the utilization of original ultrasound radiofrequency could be applied in diagnosing NAFLD and to quantifying liver fat fraction. Similarly, in an animal experiment, a CNN model based on radiofrequency signals was shown to have a better performance than the traditional quantitative ultrasound when classifying steatosis[ 32 ].…”

Section: Application Of Ai In Diffuse Liver Diseasementioning

confidence: 99%

Artificial intelligence in liver ultrasound

Cao¹,

Mei²,

Xie³

et al. 2022

WJG

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Artificial intelligence (AI) is playing an increasingly important role in medicine, especially in the field of medical imaging. It can be used to diagnose diseases and predict certain statuses and possible events that may happen. Recently, more and more studies have confirmed the value of AI based on ultrasound in the evaluation of diffuse liver diseases and focal liver lesions. It can assess the severity of liver fibrosis and nonalcoholic fatty liver, differentially diagnose benign and malignant liver lesions, distinguish primary from secondary liver cancers, predict the curative effect of liver cancer treatment and recurrence after treatment, and predict microvascular invasion in hepatocellular carcinoma. The findings from these studies have great clinical application potential in the near future. The purpose of this review is to comprehensively introduce the current status and future perspectives of AI in liver ultrasound.

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Section: Application Of Ai In Diffuse Liver Diseasementioning

confidence: 99%

Artificial intelligence in liver ultrasound

Cao¹,

Mei²,

Xie³

et al. 2022

WJG

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show abstract

“…Liu et al [3] and Shi et al [4] developed a supervised DL algorithm for tumor classification. As an another example of classification, Nguyen et al [5] demonstrated that CNNs are able to successfully classify liver tissues into fatty liver tissue and healthy liver tissue based on using raw RF backscattered data. Similarly, a common detection application is detecting tumors.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we examine DL techniques for classifying samples based on ultrasonic backscattered RF data similar to the work of Nguyen et al [5]. To improve classification we consider the diffraction patterns associated with ultrasonic transducers and how they result in different regions or 'zones' that must also be learned to separate the system signal from the sample signal.…”

Section: Introductionmentioning

confidence: 99%

“…Nguyen et al [20] demonstrated that QUS techniques are able to detect the presence of steatosis in a rabbit model of fatty liver with a classification accuracy of 84.11 %. In a later study, Nguyen et al [5] compared a DLbased classifier to a QUS-based classifier for the problem of fatty liver classifier and found that the DL-based classifier outperformed the QUS-based approach with the accuracy of 74 % versus 59 %. While the traditional spectral-based QUS approach does not utilize the phase information in the RF signal, DL-based approaches can extract additional classification power from the lost phase information from the RF data.…”

Section: Introductionmentioning

confidence: 99%

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A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training

Soylu¹,

Oelze²

2022

Preprint

Self Cite

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Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field where researchers adapt the image analysis capabilities of DL algorithms to biomedical ultrasound imaging settings. A major roadblock to wider adoption of DL powered biomedical ultrasound imaging is that acquiring large and diverse datasets is expensive in clinical settings, which is a requirement for successful DL implementation. Hence, there is a constant need for developing data-efficient DL techniques to turn DL powered biomedical ultrasound imaging into reality. In this work, we develop a data-efficient deep learning training strategy, which we named Zone Training. In Zone Training, we propose to divide the complete field of view of an ultrasound image into multiple zones associated with different regions of a diffraction pattern and then, train separate DL networks for each zone. The main advantage of Zone Training is that it requires less training data to achieve high accuracy. In this work, three different tissuemimicking phantoms were classified by a DL network. The results demonstrated that Zone Training required a factor of 2-5 less training data to achieve similar classification accuracies compared to a conventional training strategy.

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“…Hepatic fibrosis is reversible in the early stage, so early detection of it is of critical importance. The evaluation of hepatic fibrosis stages is essential for prognosis, surveillance, and treatment decisions in patients with chronic liver disease [ 1 ]. Currently, liver biopsy [ 2 ] is the gold standard for liver fibrosis assessment.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Hepatic Fibrosis Using Ultrasound Backscattered Radiofrequency Signals and One-Dimensional Convolutional Neural Networks

et al. 2022

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The early detection of hepatic fibrosis is of critical importance. Ultrasound backscattered radiofrequency signals from the liver contain abundant information about its microstructure. We proposed a method for characterizing human hepatic fibrosis using one-dimensional convolutional neural networks (CNNs) based on ultrasound backscattered signals. The proposed CNN model was composed of four one-dimensional convolutional layers, four one-dimensional max-pooling layers, and four fully connected layers. Ultrasound radiofrequency signals collected from 230 participants (F0: 23; F1: 46; F2: 51; F3: 49; F4: 61) with a 3-MHz transducer were analyzed. Liver regions of interest (ROIs) that contained most of the liver ultrasound backscattered signals were manually delineated using B-mode images reconstructed from the backscattered signals. ROI signals were normalized and augmented by using a sliding window technique. After data augmentation, the radiofrequency signal segments were divided into training sets, validation sets and test sets at a ratio of 80%:10%:10%. In the test sets, the proposed algorithm produced an area under the receive operating characteristic curve of 0.933 (accuracy: 91.30%; sensitivity: 92.00%; specificity: 90.48%), 0.997 (accuracy: 94.29%; sensitivity: 94.74%; specificity: 93.75%), 0.818 (accuracy: 75.00%; sensitivity: 69.23%; specificity: 81.82%), and 0.934 (accuracy: 91.67%; sensitivity: 88.89%; specificity: 94.44%) for diagnosis liver fibrosis stage ≥F1, ≥F2, ≥F3, and ≥F4, respectively. Experimental results indicated that the proposed deep learning algorithm based on ultrasound backscattered signals yields a satisfying performance when diagnosing hepatic fibrosis stages. The proposed method may be used as a new quantitative ultrasound approach to characterizing hepatic fibrosis.

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Use of a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver

Cited by 18 publications

References 21 publications

Artificial intelligence in liver ultrasound

Artificial intelligence in liver ultrasound

A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training

Evaluation of Hepatic Fibrosis Using Ultrasound Backscattered Radiofrequency Signals and One-Dimensional Convolutional Neural Networks

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