Automated fatty liver disease detection in point-of-care ultrasound B-mode images

Miriam Naim Ibrahim; Raul Blázquez-García; Adi Lightstone; Fankun Meng; Mamatha Bhat; Ahmed El Kaffas; Eranga Ukwatta

doi:10.1117/1.JMI.10.3.034505

5 June 2023 Automated fatty liver disease detection in point-of-care ultrasound B-mode images

Miriam Naim Ibrahim, Raul Blázquez-García, Adi Lightstone, Fankun Meng, Mamatha Bhat, Ahmed El Kaffas, Eranga Ukwatta

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 3, 034505 (June 2023). https://doi.org/10.1117/1.JMI.10.3.034505

Abstract

Purpose

Non-alcoholic fatty liver disease (NAFLD) is an increasing global health concern, with a prevalence of 25% worldwide. The rising incidence of NAFLD, an asymptomatic condition, reinforces the need for systematic screening strategies in primary care. We present the use of non-expert acquired point-of-care ultrasound (POCUS) B-mode images for the development of an automated steatosis classification algorithm.

Approach

We obtained a Health Insurance Portability and Accountability Act compliant dataset consisting of 478 patients [body mass index 23.60 ± 3.55, age 40.97 ± 10.61], imaged with POCUS by non-expert health care personnel. A U-Net deep learning (DL) model was used for liver segmentation in the POCUS B-mode images, followed by 224 × 224 patch extraction of liver parenchyma. Several DL models including VGG-16, ResNet-50, Inception V3, and DenseNet-121 were trained for binary classification of steatosis. All layers of each tested model were unfrozen, and the final layer was replaced with a custom classifier. Majority voting was applied for patient-level results.

Results

On a hold-out test set of 81 patients, the final DenseNet-121 model yielded an area under the receiver operator characteristic curve of 90.1%, sensitivity of 95.0%, and specificity of 85.2% for the detection of liver steatosis. Average cross-validation performance in models using patches of liver parenchyma as input outperformed methods using complete B-mode frames.

Conclusions

Despite minimal POCUS acquisition training, and low-quality B-mode images, it is possible to detect steatosis using DL algorithms. Implementation of this algorithm in POCUS software may offer an accessible, low-cost steatosis screening technology, for use by non-expert health care personnel.

Citation Download Citation

Miriam Naim Ibrahim, Raul Blázquez-García, Adi Lightstone, Fankun Meng, Mamatha Bhat, Ahmed El Kaffas, and Eranga Ukwatta "Automated fatty liver disease detection in point-of-care ultrasound B-mode images," Journal of Medical Imaging 10(3), 034505 (5 June 2023). https://doi.org/10.1117/1.JMI.10.3.034505

Received: 24 November 2022; Accepted: 19 May 2023; Published: 5 June 2023

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KEYWORDS

Liver

Education and training

Diseases and disorders

Image segmentation

Ultrasonography

Point-of-care devices

Cross validation

Purpose

Approach

Results

Conclusions

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