The revolutionary role of machine learning in predicting, diagnosing, and treating liver disease

Authors’ contributions

Yun-Wen Zheng: Writing e review & editing, Funding acquisi-tion, Conceptualization. Xiang-Long Huang: Writing e review & editing, Writing e original draft, Funding acquisition. Wen-Hao Chen: Writing e review & editing, Writing e original draft. Ming Ding: Writing e review & editing, Visualization. Yu-Mu Song: Writing e review & editing.

Declaration of competing interest

The authors declare the following financial interests or personal relationships which may be considered as potential competing in-terest: Xiang-Long Huang reports financial support was provided by Jiangsu University. Yu-Mu Song reports a relationship with Pro-metheus RegMed Tech (Suzhou) Co., Ltd that includes: employ-ment. Other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 82270697 to Yun-Wen Zheng), the Sci-ence and Technology Planning Project of Guangdong Province of China (No. 2021B1212040016 to Yun-Wen Zheng), the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515012574 to Yun-Wen Zheng), the Jiangsu Provincial Med-ical Key Discipline Cultivation Unit (No. JSDW202229 to Yun-Wen Zheng), Haihe Laboratory of Cell Ecosystem Innovation Fund (No. HH24KYZX0008 to Yun-Wen Zheng), and the Student Research Project of Jiangsu University (No. 23A189 to Xiang-Long Huang).

References

[1] Martinou E, Pericleous M, Stefanova I, Kaur V, Angelidi AM. Diagnostic modal-ities of non-alcoholic fatty liver disease: from biochemical biomarkers to multi-omics non-invasive approaches. Diagnostics (Basel). 2022;12:407. https://doi.org/10.3390/diagnostics12020407.

[2] Song YM, Ge JY, Ding M, Zheng YW. Key factor screening in mouse NASH model using single-cell sequencing combined with machine learning. Heliyon. 2024;10:e33597. https://doi.org/10.1016/j.heliyon.2024.e33597.

[3] Li R, Zhao M, Miao C, Shi X, Lu J. Identification and validation of key biomarkers associated with macrophages in nonalcoholic fatty liver disease based on hdWGCNA and machine learning. Aging (Albany NY). 2023;15:15451-15472. https://doi.org/10.18632/aging.205374.

[4] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convo-lutional neural networks. Commun ACM. 2017;60:84e90. https://doi.org/10.1145/3065386.

[5] Onyema EM, Almuzaini KK, Onu FU, et al. Prospects and challenges of using machine learning for academic forecasting. Comput Intell Neurosci. 2022;2022:5624475. https://doi.org/10.1155/2022/5624475.

[6] Jayatilake SMDAC, Ganegoda GU. Involvement of machine learning tools in healthcare decision making. J Healthc Eng. 2021;2021:6679512. https://doi.org/10.1155/2021/6679512.

[7] Weng S, Hu D, Chen J, Yang Y, Peng D. Prediction of fatty liver disease in a Chi-nese population using machine-learning algorithms. Diagnostics (Basel). 2023;13:1168. https://doi.org/10.3390/diagnostics13061168.

[8] Gao R, Zhao S, Aishanjiang K, et al. Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data. J Hematol Oncol. 2021;14:154. https://doi.org/10.1186/s13045-021-01167-2.

[9] Hamm CA, Wang CJ, Savic LJ, et al. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol. 2019;29:3338-3347. https://doi.org/10.1007/s00330-019-06205-9.

[10] Ji GW, Fan Y, Sun DW, et al. Machine learning to improve prognosis prediction of early hepatocellular carcinoma after surgical resection. J Hepatocell Carci-noma. 2021;8:913-923. https://doi.org/10.2147/JHC.S320172.