AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver Disease Using a Novel Cascade Model and Information Fusion Techniques

Niloufar Delfan; Pardis Ketabi Moghadam; Mohammad Khoshnevisan; Mehdi Hosseini Chagahi; Behzad Hatami; Melika Asgharzadeh; Mohammadreza Zali; Behzad Moshiri; Amin Momeni Moghaddam; Mohammad Amin Khalafi; Khosrow Dehnad

arXiv · 2412.04884

AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver Disease Using a Novel Cascade Model and Information Fusion Techniques

Niloufar Delfan·Pardis Ketabi Moghadam·Mohammad Khoshnevisan·Mehdi Hosseini Chagahi·Behzad Hatami·Melika Asgharzadeh·Mohammadreza Zali·Behzad Moshiri·Amin Momeni Moghaddam·Mohammad Amin Khalafi·Khosrow Dehnad

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Abstract

Non-alcoholic fatty liver disease (NAFLD) is one of the most widespread liver disorders on a global scale, posing a significant threat of progressing to more severe conditions like nonalcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis, and hepatocellular carcinoma. Diagnosing and staging NAFLD presents challenges due to its non-specific symptoms and the invasive nature of liver biopsies. Our research introduces a novel artificial intelligence cascade model employing ensemble learning and feature fusion techniques. We developed a non-invasive, robust, and reliable diagnostic artificial intelligence tool that utilizes anthropometric and laboratory parameters, facilitating early detection and intervention in NAFLD progression. Our novel artificial intelligence achieved an 86% accuracy rate for the NASH steatosis staging task (non-NASH, steatosis grade 1, steatosis grade 2, and steatosis grade 3) and an impressive 96% AUC-ROC for distinguishing between NASH (steatosis grade 1, grade 2, and grade3) and non-NASH cases, outperforming current state-of-the-art models. This notable improvement in diagnostic performance underscores the potential application of artificial intelligence in the early diagnosis and treatment of NAFLD, leading to better patient outcomes and a reduced healthcare burden associated with advanced liver disease.

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Niloufar Delfan, Pardis Ketabi Moghadam, Mohammad Khoshnevisan, Mehdi Hosseini Chagahi, Behzad Hatami, Melika Asgharzadeh, Mohammadreza Zali, Behzad Moshiri, Amin Momeni Moghaddam, Mohammad Amin Khalafi, Khosrow Dehnad. 2024-12-06. AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver Disease Using a Novel Cascade Model and Information Fusion Techniques. https://arxiv.org/abs/2412.04884

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