Update of machine learning for ultrasound diagnosis of metabolic dysfunction-associated steatotic liver disease: a bright future for deep learning
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common liver disease and the burden is increasing around the world. Ultrasound diagnosis of MASLD is the preferred method due to its convenience, absence of radiation, and high accuracy. The application of artificial intell...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-06-01
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| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/19645.pdf |
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| Summary: | Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common liver disease and the burden is increasing around the world. Ultrasound diagnosis of MASLD is the preferred method due to its convenience, absence of radiation, and high accuracy. The application of artificial intelligence (AI) in MASLD diagnosis has been explored across electronic medical records, laboratory tests, ultrasound and radiographic imaging, and liver histopathological data. Notably, AI’s application in ultrasound diagnosis has garnered significant attention. Deep learning (DL), known for its exceptional image recognition and classification capabilities, has become a focal point in ultrasound research. This paper reviews and analyzes the application of various machine learning (ML) algorithms in the ultrasound diagnosis of MASLD, highlighting the advantages and potential of AI in this field. It is intended for clinicians, AI researchers, and healthcare innovators, aiming to enhance diagnostic accuracy, expand MASLD screening in primary care, and support early diagnosis, prevention, and treatment. |
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| ISSN: | 2167-8359 |