AI‐Based Platelet‐Independent Noninvasive Test for Liver Fibrosis in MASLD Patients
ABSTRACT Background and Aim Noninvasive tests (NITs), such as platelet‐based indices and ultrasound/MRI elastography, are widely used to assess liver fibrosis in metabolic dysfunction‐associated steatotic liver disease (MASLD). However, platelet counts are not routinely included in Japanese health c...
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | JGH Open |
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| Online Access: | https://doi.org/10.1002/jgh3.70150 |
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| author | Shun‐ichi Wakabayashi Takefumi Kimura Nobuharu Tamaki Takanobu Iwadare Taiki Okumura Hiroyuki Kobayashi Yuki Yamashita Naoki Tanaka Masayuki Kurosaki Takeji Umemura |
| author_facet | Shun‐ichi Wakabayashi Takefumi Kimura Nobuharu Tamaki Takanobu Iwadare Taiki Okumura Hiroyuki Kobayashi Yuki Yamashita Naoki Tanaka Masayuki Kurosaki Takeji Umemura |
| author_sort | Shun‐ichi Wakabayashi |
| collection | DOAJ |
| description | ABSTRACT Background and Aim Noninvasive tests (NITs), such as platelet‐based indices and ultrasound/MRI elastography, are widely used to assess liver fibrosis in metabolic dysfunction‐associated steatotic liver disease (MASLD). However, platelet counts are not routinely included in Japanese health check‐ups, limiting their utility in large‐scale screenings. Additionally, elastography, while effective, is costly and less accessible in routine practice. Most existing AI‐based models incorporate these markers, restricting their applicability. This study aimed to develop a simple yet accurate AI model for liver fibrosis staging using only routine demographic and biochemical markers. Methods This retrospective study analyzed biopsy‐proven data from 463 Japanese MASLD patients. Patients were randomly assigned to training (N = 370, 80%) and test (N = 93, 20%) cohorts. The AI model incorporated age, sex, BMI, diabetes, hypertension, hyperlipidemia, and routine blood markers (AST, ALT, γ‐GTP, HbA1c, glucose, triglycerides, cholesterol). Results The Support Vector Machine model demonstrated high diagnostic performance, with an area under the curve (AUC) of 0.886 for detecting significant fibrosis (≥ F2). The AUCs for advanced fibrosis (≥ F3) and cirrhosis (F4) were 0.882 and 0.916, respectively. Compared to FIB‐4, APRI, and FAST score (0.80–0.96), SVM achieved comparable accuracy while eliminating the need for platelet count or elastography. Conclusion This AI model accurately assesses liver fibrosis in MASLD patients without requiring platelet count or elastography. Its simplicity, cost‐effectiveness, and strong diagnostic performance make it well‐suited for large‐scale health screenings and routine clinical use. |
| format | Article |
| id | doaj-art-415a4f555f294ceb9fcd21e7d5b8d0b1 |
| institution | OA Journals |
| issn | 2397-9070 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | JGH Open |
| spelling | doaj-art-415a4f555f294ceb9fcd21e7d5b8d0b12025-08-20T02:17:24ZengWileyJGH Open2397-90702025-04-0194n/an/a10.1002/jgh3.70150AI‐Based Platelet‐Independent Noninvasive Test for Liver Fibrosis in MASLD PatientsShun‐ichi Wakabayashi0Takefumi Kimura1Nobuharu Tamaki2Takanobu Iwadare3Taiki Okumura4Hiroyuki Kobayashi5Yuki Yamashita6Naoki Tanaka7Masayuki Kurosaki8Takeji Umemura9Department of Medicine, Division of Gastroenterology Shinshu University School of Medicine Matsumoto JapanDepartment of Medicine, Division of Gastroenterology Shinshu University School of Medicine Matsumoto JapanDepartment of Gastroenterology and Hepatology Musashino Red Cross Hospital Tokyo JapanDepartment of Medicine, Division of Gastroenterology Shinshu University School of Medicine Matsumoto JapanDepartment of Medicine, Division of Gastroenterology Shinshu University School of Medicine Matsumoto JapanDepartment of Medicine, Division of Gastroenterology Shinshu University School of Medicine Matsumoto JapanDepartment of Medicine, Division of Gastroenterology Shinshu University School of Medicine Matsumoto JapanDepartment of Global Medical Research Promotion Shinshu University Graduate School of Medicine Matsumoto JapanDepartment of Gastroenterology and Hepatology Musashino Red Cross Hospital Tokyo JapanDepartment of Medicine, Division of Gastroenterology Shinshu University School of Medicine Matsumoto JapanABSTRACT Background and Aim Noninvasive tests (NITs), such as platelet‐based indices and ultrasound/MRI elastography, are widely used to assess liver fibrosis in metabolic dysfunction‐associated steatotic liver disease (MASLD). However, platelet counts are not routinely included in Japanese health check‐ups, limiting their utility in large‐scale screenings. Additionally, elastography, while effective, is costly and less accessible in routine practice. Most existing AI‐based models incorporate these markers, restricting their applicability. This study aimed to develop a simple yet accurate AI model for liver fibrosis staging using only routine demographic and biochemical markers. Methods This retrospective study analyzed biopsy‐proven data from 463 Japanese MASLD patients. Patients were randomly assigned to training (N = 370, 80%) and test (N = 93, 20%) cohorts. The AI model incorporated age, sex, BMI, diabetes, hypertension, hyperlipidemia, and routine blood markers (AST, ALT, γ‐GTP, HbA1c, glucose, triglycerides, cholesterol). Results The Support Vector Machine model demonstrated high diagnostic performance, with an area under the curve (AUC) of 0.886 for detecting significant fibrosis (≥ F2). The AUCs for advanced fibrosis (≥ F3) and cirrhosis (F4) were 0.882 and 0.916, respectively. Compared to FIB‐4, APRI, and FAST score (0.80–0.96), SVM achieved comparable accuracy while eliminating the need for platelet count or elastography. Conclusion This AI model accurately assesses liver fibrosis in MASLD patients without requiring platelet count or elastography. Its simplicity, cost‐effectiveness, and strong diagnostic performance make it well‐suited for large‐scale health screenings and routine clinical use.https://doi.org/10.1002/jgh3.70150AIliver fibrosisMASHMASLDnoninvasive testplatelet‐independent |
| spellingShingle | Shun‐ichi Wakabayashi Takefumi Kimura Nobuharu Tamaki Takanobu Iwadare Taiki Okumura Hiroyuki Kobayashi Yuki Yamashita Naoki Tanaka Masayuki Kurosaki Takeji Umemura AI‐Based Platelet‐Independent Noninvasive Test for Liver Fibrosis in MASLD Patients JGH Open AI liver fibrosis MASH MASLD noninvasive test platelet‐independent |
| title | AI‐Based Platelet‐Independent Noninvasive Test for Liver Fibrosis in MASLD Patients |
| title_full | AI‐Based Platelet‐Independent Noninvasive Test for Liver Fibrosis in MASLD Patients |
| title_fullStr | AI‐Based Platelet‐Independent Noninvasive Test for Liver Fibrosis in MASLD Patients |
| title_full_unstemmed | AI‐Based Platelet‐Independent Noninvasive Test for Liver Fibrosis in MASLD Patients |
| title_short | AI‐Based Platelet‐Independent Noninvasive Test for Liver Fibrosis in MASLD Patients |
| title_sort | ai based platelet independent noninvasive test for liver fibrosis in masld patients |
| topic | AI liver fibrosis MASH MASLD noninvasive test platelet‐independent |
| url | https://doi.org/10.1002/jgh3.70150 |
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