A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography

Gatos, Ilias; Tsantis, Stavros; Spiliοpoulos, Stavros; Karnabatidis, Dimitris; Theotokas, Ioannis; Zoumpoulis, P.S.; Loupas, T.; Hazle, John D.; Kagadis, George C.

doi:10.1016/j.ultrasmedbio.2017.05.002

Cited by 79 publications

(76 citation statements)

References 39 publications

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“…The proposed CNN classification scheme (using both masked and unmasked stiffness boxes) is superior to clinical studies and traditional machine learning studies. It is also superior to our previous approaches, which scored 87% and 87.3%, respectively with 91% accuracy. The results derived from the masked dataset surpassed those of Meng et al .…”

Section: Discussionmentioning

confidence: 49%

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Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment

et al. 2019

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Purpose To automatically detect and isolate areas of low and high stiffness temporal stability in shear wave elastography (SWE) image sequences and define their impact in chronic liver disease (CLD) diagnosis improvement by means of clinical examination study and deep learning algorithm employing convolutional neural networks (CNNs). Materials and Methods Two hundred SWE image sequences from 88 healthy individuals (F0 fibrosis stage) and 112 CLD patients (46 with mild fibrosis (F1), 16 with significant fibrosis (F2), 22 with severe fibrosis (F3), and 28 with cirrhosis (F4)) were analyzed to detect temporal stiffness stability between frames. An inverse Red, Green, Blue (RGB) colormap‐to‐stiffness process was performed for each image sequence, followed by a wavelet transform and fuzzy c‐means clustering algorithm. This resulted in a binary mask depicting areas of high and low stiffness temporal stability. The mask was then applied to the first image of the SWE sequence, and the derived, masked SWE image was used to estimate its impact in standard clinical examination and CNN classification. Regarding the impact of the masked SWE image in clinical examination, one measurement by two radiologists was performed in each SWE image and two in the corresponding masked image measuring areas with high and low stiffness temporal stability. Then, stiffness stability parameters, interobserver variability evaluation and diagnostic performance by means of ROC analysis were assessed. The masked and unmasked sets of SWE images were fed into a CNN scheme for comparison. Results The clinical impact evaluation study showed that the masked SWE images decreased the interobserver variability of the radiologists’ measurements in the high stiffness temporal stability areas (interclass correlation coefficient (ICC) = 0.92) compared to the corresponding unmasked ones (ICC = 0.76). In terms of diagnostic accuracy, measurements in the high‐stability areas of the masked SWE images (area‐under‐the‐curve (AUC) ranging from 0.800 to 0.851) performed similarly to those in the unmasked SWE images (AUC ranging from 0.805 to 0.893). Regarding the measurements in the low stiffness temporal stability areas of the masked SWE images, results for interobserver variability (ICC = 0.63) and diagnostic accuracy (AUC ranging from 0.622 to 0.791) were poor. Regarding the CNN classification, the masked SWE images showed improved accuracy (ranging from 82.5% to 95.5%) compared to the unmasked ones (ranging from 79.5% to 93.2%) for various CLD stage combinations. Conclusion Our detection algorithm excludes unreliable areas in SWE images, reduces interobserver variability, and augments CNN's accuracy scores for many combinations of fibrosis stages.

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Section: Discussionmentioning

confidence: 49%

“…In previous studies, Gatos et al . employed computerized pattern recognition algorithms to evaluate the accuracy of different techniques for differentiating healthy individuals from patients with CLD.…”

Section: Introductionmentioning

confidence: 99%

Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment

et al. 2019

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“…In supervised learning, tools learn to output the correct labeled target, which can vary from detection of underlying liver disease in patients, early detection of nonalcoholic fatty liver disease (NAFLD) with images, or better identification of patients with primary sclerosing cholangitis (PSC) at risk for hepatic decompensation (HD) or otherwise (Fig. ) . ML in the supervised setting encompasses tools that can uncover nonlinear patterns in the data to predict these various output targets.…”

Section: Tools and Applications To Liver Diseasementioning

confidence: 99%

“…In a study of 126 patients (56 healthy patients with F0, 70 patients with F1 and above), an SVM classification algorithm was used to analyze the still images obtained from shear wave elastography (SWE) and categorize patients by the presence or absence of underlying liver disease. Through an inverse mapping sequence correlating quantifiable color information with obtained stiffness values, this algorithm was able to accurately discriminate healthy subjects from those with CLD at 87.3%, with a sensitivity of 93.5% and specificity of 81.2% when compared to gold‐standard liver biopsy . Unfortunately, this study excluded patients with obesity, limiting generalizability of the algorithm, and etiology of underlying liver disease was not taken into account.…”

Section: In Liver Diseasesmentioning

confidence: 99%

“…1). (11)(12)(13)(14)52) ML in the supervised setting encompasses tools that can uncover nonlinear patterns in the data to predict these various output targets. A simple extension of Bayes' theorem, naïve Bayes classifiers predict class labels by computing the likelihood of the observed features under each class and returning the class with the maximum likelihood.…”

Section: Tools and Applications To Liver Diseasementioning

confidence: 99%

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Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

et al. 2020

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Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas within hepatology where these methods can be applied. In this review, we examine the literature pertaining to machine learning in hepatology and liver transplant medicine. We provide an overview of the strengths and limitations of ML tools and their potential applications to both clinical and molecular data in hepatology. ML has been applied to various types of data in liver disease research, including clinical, demographic, molecular, radiological, and pathological data. We anticipate that use of ML tools to generate predictive algorithms will change the face of clinical practice in hepatology and transplantation. This review will provide readers with the opportunity to learn about the ML tools available and potential applications to questions of interest in hepatology.

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Performance of shear wave elastography: A single centre pilot study of mixed etiology liver disease patients with normal BMI

Laroia

Srinivasan

Yadav

et al. 2021

Australas J Ultrason Med

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Purpose: To assess the performance of shear wave ultrasound elastography (SWE) for non-invasive grading of fibrosis in normal BMI patients with varied aetiology chronic liver disease. Method: Prospective SWE liver and spleen stiffness (LS, SS respectively) of 124 patients (94 men, mean age 45.4 AE 12.4 years, mean BMI 19.66 AE 1.49) with CLD of mixed aetiology, who underwent liver biopsy, between January 2019-20 was analysed using receiver operating curve (ROC) and classification analysis regression tree (CART) to determine fibrosis cut-off values and nominal logistical regression to quantify fibrosis. Results: Of 124 patients, 50 (40%) had non-alcoholic steatohepatitis (NASH), 31 (25%) chronic hepatitis B (CHB) and 43 (35%) alcoholic liver disease (ALD) on biopsy. Overall mean LS and SS of the study population was 11.81 AE 5.9 and 16.88 AE 10.8 kPa, respectively. LS cut-off value <8 kPa was consistent with F0, 9-14 kPa for F1-F2 and >14.9 kPa for F3-F4 fibrosis on biopsy. On application of CART, LS value < 5.3 kPa was discriminative for NASH, 5.32 to <12.64 kPa for CHB, >12.64 kPa for ALD, SS <15.3 kPa was discriminative for NASH, 15.3-30 kPa for CHB and >30 kPa for ALD in our study population. Conclusion: SWE is a viable non-invasive tool for assessment of liver fibrosis grading in a population of mixed aetiology CLD. LS values in conjunction with SS are promising predictors of F2-F3 fibrosis with potential to discriminate select categories like CHB and NASH in such a population.

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A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography

Cited by 79 publications

References 39 publications

Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment

Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

Performance of shear wave elastography: A single centre pilot study of mixed etiology liver disease patients with normal BMI

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