A Deep Learning Trial on Transient Elastography for Assessment of Liver Fibrosis

Li, Yongshuai; He, Qiang; Luo, Jianwen

doi:10.1109/ultsym.2018.8579992

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…Six‐teen studies 21‐36 met our inclusion criteria (Figure 2). Studies were published between 2017 and 2020.…”

Section: Resultsmentioning

confidence: 99%

“…Li et al 27 developed a model to predict fibrosis using TE. Their model performed slightly better than the currently used liver stiffness measurements (LSM) method for advanced fibrosis (≥F3) and cirrhosis.…”

Section: Resultsmentioning

confidence: 99%

“…Studies were published between 2017 and 2020. Imaging modalities included US [21][22][23][24][25][26][27][28][29][30] (n = 10, 63%), MRI [31][32][33] (n = 3, 19%), and computed tomography (CT) [34][35][36] (n = 3, 19%). A total of 40 405 radiological images were analyzed from 15 853 patients.…”

Section: Study Selection and Characteristicsmentioning

confidence: 99%

“…Six-teen studies [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] met our inclusion criteria (Figure 2). Studies were published between 2017 and 2020.…”

Section: Study Selection and Characteristicsmentioning

confidence: 99%

“…Ten studies [21][22][23][24][25][26][27][28][29][30] classified fibrosis using US images. Six focused on shear wave elastography (SWE), 21,22,[24][25][26]29 one on transient elastography (TE), 7 and three on non-elastography US images.…”

Section: Us Imaging Classificationmentioning

confidence: 99%

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Deep learning for noninvasive liver fibrosis classification: A systematic review

Anteby

Klang

Horesh

et al. 2021

Liver International

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Background and Aims While biopsy is the gold standard for liver fibrosis staging, it poses significant risks. Noninvasive assessment of liver fibrosis is a growing field. Recently, deep learning (DL) technology has revolutionized medical image analysis. This technology has the potential to enhance noninvasive fibrosis assessment. We systematically examined the application of DL in noninvasive liver fibrosis imaging. Methods Embase, MEDLINE, Web of Science, and IEEE Xplore databases were used to identify studies that reported on the accuracy of DL for classification of liver fibrosis on noninvasive imaging. The search keywords were “liver or hepatic,” “fibrosis or cirrhosis,” and “neural or DL networks.” Risk of bias and applicability were evaluated using the QUADAS‐2 tool. Results Sixteen studies were retrieved. Imaging modalities included ultrasound (n = 10), computed tomography (n = 3), and magnetic resonance imaging (n = 3). The studies analyzed a total of 40 405 radiological images from 15 853 patients. All but two of the studies were retrospective. In most studies the “ground truth” reference was the METAVIR score for pathological staging (n = 9.56%). The majority of the studies reported an accuracy >85% when compared to histopathology. Fourteen studies (87.5%) had a high risk of bias and concerns regarding applicability. Conclusions Deep learning has the potential to play an emerging role in liver fibrosis classification. Yet, it is still limited by a relatively small number of retrospective studies. Clinicians should facilitate the use of this technology by sharing databases and standardized reports. This may optimize the noninvasive evaluation of liver fibrosis on a large scale.

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“…Six‐teen studies 21‐36 met our inclusion criteria (Figure 2). Studies were published between 2017 and 2020.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Study Selection and Characteristicsmentioning

confidence: 99%

“…Six-teen studies [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] met our inclusion criteria (Figure 2). Studies were published between 2017 and 2020.…”

Section: Study Selection and Characteristicsmentioning

confidence: 99%