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Systematic Review: AI Applications in Liver Imaging with a Focus on Segmentation and Detection.

Mihai Dan Pomohaci^1,2, Mugur Cristian Grasu^1,2, Alexandru-Ştefan Băicoianu-Nițescu^1,2

¹Department 8: Radiology, Discipline of Radiology, Medical Imaging and Interventional Radiology I, University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania.

Life (Basel, Switzerland)

|February 26, 2025

Summary

Artificial intelligence (AI) in liver radiology predominantly uses classification tasks for liver lesions on CT scans. Research often relies on public datasets but lacks external testing and code sharing, hindering clinical AI advancement.

Keywords:

CT MRI US artificial intelligence cholangiocarcinoma deep learning hepatocellular carcinoma liver machine learning

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

The liver's complex pathology makes it a key area for radiological study.
Artificial intelligence (AI) offers potential to enhance liver disease diagnosis and management.
A systematic review is needed to map the landscape of AI applications in liver radiology.

Purpose of the Study:

To systematically review and categorize AI research in liver radiology from 2018-2024.
To classify studies by area of interest, AI task, and imaging modality.
To analyze the use of datasets and code sharing in detection and segmentation studies.

Main Methods:

Systematic literature search across PubMed/Medline, Scopus, and Web of Science databases.
Study selection based on PRISMA guidelines, focusing on AI in liver radiology.
Detailed analysis of 329 studies for detection/segmentation tasks, including dataset and code sharing practices.

Main Results:

Classification tasks for liver lesions using CT imaging were the most common AI application.
Detection and/or segmentation studies frequently utilized public datasets, often a single one.
Limited external testing and code sharing were observed in detection/segmentation research.

Conclusions:

AI in liver radiology is dominated by classification tasks, particularly for lesions on CT scans.
The reliance on public datasets for detection/segmentation tasks, coupled with low code sharing, presents challenges.
Future research should focus on multi-task models and increasing dataset availability to improve AI's clinical utility in liver imaging.