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Liver fibrosis classification from ultrasound using machine learning: a systematic literature review

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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

Liver biopsy was considered the gold standard for diagnosing liver fibrosis; however, with advancements in medical technology and increasing awareness of potential complications, the reliance on liver biopsy has diminished. Ultrasound is gaining popularity due to its wider availability and cost-effectiveness. This study examined the machine learning / deep learning (ML/DL) models for non-invasive liver fibrosis classification from ultrasound.

Methods

Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, we searched five academic databases using the query. We defined population, intervention, comparison, outcomes, and study design (PICOS) framework for the inclusion. Furthermore, Joana Briggs Institute (JBI) checklist for analytical cross-sectional studies is used for quality assessment.

Results

Among the 188 screened studies, 17 studies are selected. The methods are categorized as off-the-shelf (OTS), attention, generative, and ensemble classifiers. Most studies used OTS classifiers that combined pre-trained ML/DL methods with radiomics features to determine fibrosis staging. Although machine learning shows potential for fibrosis classification, there are limited external comparisons of interventions and prospective clinical trials, which limits their applicability.

Conclusion

With the recent success of ML/DL toward biomedical image analysis, automated solutions using ultrasound are developed for predicting liver diseases. However, their applicability is bounded by the limited and imbalanced retrospective studies having high heterogeneity. This challenge could be addressed by generating a standard protocol for study design by selecting appropriate population, interventions, outcomes, and comparison.

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Data availability

All data sources are publicly accessible.

Code availability

Not applicable.

Abbreviations

ACC:

Accuracy

ACGAN:

Auxiliary classifier generative adversarial network

AUROC:

Area under the receiver operating characteristic

ANN:

Artificial neural network

CT:

Computed tomography

CNN:

Convolution neural network

DL:

Deep learning

HKD:

Homodyned k-distribution

LSM:

Liver stiffness measurement

LR:

Logistic regression

ML:

Machine learning

MMGN-AL:

Multi-modal fusion network with active learning

MR:

Magnetic resonance

MSTNet:

Multi-scale texture network

NPV:

Negative predictive value

OTS:

Off-the-shelf

PCFI:

Percentage of color fill-in

pSWE:

Point shear wave elastography

PPV:

Positive predictive value

PRISMA:

Preferred reporting items for systematic reviews and meta-analyses

QUS:

Quantitative ultrasound

RTE:

Real-time elastography

ROI:

Region of Interest

SEN:

Sensitivity

SPE:

Specificity

SWV:

Shear wave velocity

SVM:

Support vector machine

US:

Ultrasound

VGG:

Visual geometry group

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  1. Bhavik Patel and Imon Banerjee have contributed equally to this work.

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  1. Department of Radiology, Mayo Clinic, Phoenix, AZ, USA

    Narinder Singh Punn, Bhavik Patel & Imon Banerjee

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  1. Narinder Singh Punn
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Punn, N.S., Patel, B. & Banerjee, I. Liver fibrosis classification from ultrasound using machine learning: a systematic literature review. Abdom Radiol 49, 69–80 (2024). https://doi.org/10.1007/s00261-023-04081-y

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