This paper proposes a simple convolutional neural model as a novel method to predict the level of hepatic steatosis from ultrasound data. Hepatic steatosis is the major histologic feature of non-alcoholic fatty liver disease (NAFLD), which has become a major global health challenge. Recently a new definition for FLD, that take into account the risk factors and clinical characteristics of subjects, has been suggested; the proposed criteria for Metabolic Disfunction-Associated Fatty Liver Disease (MAFLD) are based on histological (biopsy), imaging or blood biomarker evidence of fat accumulation in the liver (hepatic steatosis), in subjects with overweight/obesity or presence of type 2 diabetes mellitus. In lean or normal weight, non-diabetic individuals with steatosis, MAFLD is diagnosed when at least two metabolic abnormalities are present. Ultrasound examinations are the most used technique to non-invasively identify liver steatosis in a screening settings. However, the diagnosis is operator dependent, as accurate image processing techniques have not entered yet in the diagnostic routine. In this paper, we discuss the adoption of simple convolutional neural models to estimate the degree of steatosis from echographic images in accordance with the state-of-the-art magnetic resonance spectroscopy measurements (expressed as percentage of the estimated liver fat). More than 22,000 ultrasound images were used to train three networks, and results show promising performances in our study (150 subjects).

A Deep Learning Approach for Hepatic Steatosis Estimation from Ultrasound Imaging / Colantonio, S.; Salvati, A.; Caudai, C.; Bonino, F.; De Rosa, L.; Pascali, M. A.; Germanese, D.; Brunetto, M. R.; Faita, F.. - 1463:(2021), pp. 703-714. (Intervento presentato al convegno 13th International Conference on Computational Collective Intelligence, ICCCI 2021 tenutosi a Online nel 2021) [10.1007/978-3-030-88113-9_57].

A Deep Learning Approach for Hepatic Steatosis Estimation from Ultrasound Imaging

De Rosa L.;
2021-01-01

Abstract

This paper proposes a simple convolutional neural model as a novel method to predict the level of hepatic steatosis from ultrasound data. Hepatic steatosis is the major histologic feature of non-alcoholic fatty liver disease (NAFLD), which has become a major global health challenge. Recently a new definition for FLD, that take into account the risk factors and clinical characteristics of subjects, has been suggested; the proposed criteria for Metabolic Disfunction-Associated Fatty Liver Disease (MAFLD) are based on histological (biopsy), imaging or blood biomarker evidence of fat accumulation in the liver (hepatic steatosis), in subjects with overweight/obesity or presence of type 2 diabetes mellitus. In lean or normal weight, non-diabetic individuals with steatosis, MAFLD is diagnosed when at least two metabolic abnormalities are present. Ultrasound examinations are the most used technique to non-invasively identify liver steatosis in a screening settings. However, the diagnosis is operator dependent, as accurate image processing techniques have not entered yet in the diagnostic routine. In this paper, we discuss the adoption of simple convolutional neural models to estimate the degree of steatosis from echographic images in accordance with the state-of-the-art magnetic resonance spectroscopy measurements (expressed as percentage of the estimated liver fat). More than 22,000 ultrasound images were used to train three networks, and results show promising performances in our study (150 subjects).
2021
Communications in Computer and Information Science
Rhodes, Greece
Springer Science and Business Media Deutschland GmbH
9783030881122
9783030881139
Colantonio, S.; Salvati, A.; Caudai, C.; Bonino, F.; De Rosa, L.; Pascali, M. A.; Germanese, D.; Brunetto, M. R.; Faita, F.
A Deep Learning Approach for Hepatic Steatosis Estimation from Ultrasound Imaging / Colantonio, S.; Salvati, A.; Caudai, C.; Bonino, F.; De Rosa, L.; Pascali, M. A.; Germanese, D.; Brunetto, M. R.; Faita, F.. - 1463:(2021), pp. 703-714. (Intervento presentato al convegno 13th International Conference on Computational Collective Intelligence, ICCCI 2021 tenutosi a Online nel 2021) [10.1007/978-3-030-88113-9_57].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/410932
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