| For: | Xiong FX, Sun L, Zhang XJ, Chen JL, Zhou Y, Ji XM, Meng PP, Wu T, Wang XB, Hou YX. Machine learning-based models for advanced fibrosis in non-alcoholic steatohepatitis patients: A cohort study. World J Gastroenterol 2025; 31(9): 101383 [PMID: 40061588 DOI: 10.3748/wjg.v31.i9.101383] |
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| URL: | https://www.wjgnet.com/1007-9327/full/v31/i9/101383.htm |
| Number | Citing Articles |
| 1 |
Annabel Koivu, Ghazal Azarfar, Maryam Shojaee, Naomi K T Hlaing, Sameera Rizvi, Divya Sharma, Saba Maleki, Mamatha Bhat. Machine learning model integrating transient elastography and clinical data for prediction of graft fibrosis after liver transplantation. World Journal of Hepatology 2026; 18(6): 120258 doi: 10.4254/wjh.120258
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| 2 |
Huan Zhang, Xiangyu Wu, Wenjing Ni, Jiali Wu, Sisi Zhou, Leyao Jia, Mingyang Jin, Sitian Zhao, Zhenyao Jiang, Chao Wu, Yuxiang Sun, Junping Shi, Jie Li. Application of machine learning and deep learning in metabolic dysfunction-associated steatotic liver disease: a systematic review and meta-analysis. Journal of Advanced Research 2026; 83 doi: 10.1016/j.jare.2025.08.042
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| 3 |
Pablo Guillermo Hernández-Almonacid, Ximena Marín-Quintero. Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches. World Journal of Gastroenterology 2026; 32(2): 111737 doi: 10.3748/wjg.v32.i2.111737
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Syed Muhammad Hassan Bukhari, Ushna Shahid, Wahhaj Qayyum, Aminah Amjad, Salwa Qayyum, Abdul Aleem. Prediction Models Versus Fibrosis-4 Index (FIB-4) for Advanced Fibrosis in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): A Verification-Audited Systematic Review and Meta-Analysis. Cureus 2026; doi: 10.7759/cureus.117908
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| 5 |
Neilson Silveira de Souza, Théo Cordeiro Veiga Vitório, Raphael Augusto de Souza, Marcos Antônio Dórea Machado, Helma Pinchemel Cotrim. Artificial Intelligence for Fibrosis Diagnosis in Metabolic-Dysfunction-Associated Steatotic Liver Disease: A Systematic Review. Diagnostics 2026; 16(2) doi: 10.3390/diagnostics16020261
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| 6 |
Min Cho, Yanzhen Qu. Enhancing Precision Oncology: Deep Learning Models vs. Classical Machine Learning Models in Multi-Label Breast Cancer Classification. European Journal of Electrical Engineering and Computer Science 2025; 9(3) doi: 10.24018/ejece.2025.9.3.711
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| 7 |
Shumin Li, Ziyi Yang, Xin Huang, Mingwei Zhong, Shibo Lin, Teng Liu, Han Zhao, Shaozhuang Liu, Shigang Zhao, Zi-Jiang Chen. A simple clinical model for predicting multiple liver histologic injuries in obese individuals undergoing bariatric surgery. Hepatology International 2026; doi: 10.1007/s12072-026-11159-4
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| 8 |
Catherine Lalman, Kylie R. Stabler, Yimin Yang, Janice L. Walker. Supervised Machine-Based Learning and Computational Analysis to Reveal Unique Molecular Signatures Associated with Wound Healing and Fibrotic Outcomes to Lens Injury. International Journal of Molecular Sciences 2025; 26(15) doi: 10.3390/ijms26157422
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| 9 |
Pooja Limbola, Abhijit Dwivedi, Namrata Shrivastava, Ajit Kumar Shrivastava. Advanced Sentiment Analysis of IMDB Movie Reviews Using NLP Preprocessing, TF-IDF and Classical Machine Learning Models. 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS) 2026; doi: 10.1109/ICETICS69505.2026.11651294
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| 10 |
Haiping Zhang, Xinming Li, Kechi Fang, Yinxue Ma, Lijuan Li, Huiping Yan, Yanmin Liu, Yanhua Yu, Jing Wang. Large Language Models for Heterogeneous Data Mining in Liver Disease: Framework Development and Retrospective Validation Study. Journal of Medical Internet Research 2026; 28 doi: 10.2196/92921
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| 11 |
Azra Bashir, Renuka Arora, Deepti Mehrotra, Manju Bala, Arshed H Parry, Asif Iqball, Shabir A Bhat, Zeeshan A Wani. Non-invasive prediction of significant hepatic fibrosis in individuals with chronic hepatitis C infection using fibrosis risk score and machine learning models. World Journal of Hepatology 2026; 18(3): 117465 doi: 10.4254/wjh.v18.i3.117465
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