Ultrasound Image-Based Liver Disease Diagnosis Using Machine Learning
A dataset comprising liver B-mode ultrasound image sequences is processed to construct a system to automatically detect non-alcoholic fatty liver disease (NAFLD) - also called metabolic dysfunction-associated steatotic liver disease (MASLD) - via machine learning-based binary classification. Owing to the paucity of samples in the dataset, alternatives to the widely used convolutional neural network (CNN) and similar deep learning approaches are adopted since harnessing CNN and comparative systems is likely to result in overfitting of the data. Simpler alternative machine learning approaches such as random forest classifier, logistic regression and decision tree classifier are employed, compared and contrasted. For the minimal datasets utilized, these simpler approaches resulted in reasonable performance metrics. Further refinement of the techniques could permit improvements in robustness and performance that could warrant incorporation of the resulting machine learning models into modules for the automated detection of liver disease based on ultrasound image sequences in a comprehensive artificial intelligence-driven healthcare system.
1. NHS – Non-alcoholic Fatty Liver Disease (NAFLD): https://www.nhs.uk/conditions/non-alcoholic-fatty-liver-disease. Retrieved (2025).
2. Nomura, A., Noguchi, M., Kometani, M., Furukawa, K., Yoneda, T. Artificial Intelligence in Current Diabetes Management and Prediction, Curr Diab Rep. 21(12):61 (2021).
3. Kumar, Y., Koul, A., Singla, R., Ijaz, M. F. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda, Journal of Ambient Intelligence and Humanized Computing 14:8459–8486 (2023).
4. Ansari, S., Shafi, I., Ansari, A., Ahmad, J., Shah, S. I. Diagnosis of liver disease induced by hepatitis virus using artificial neural network, IEEE Int Multitopic. https://doi.org/10.1109/INMIC.2011.6151515 (2011).
5. Battineni, G., Sagaro, G. G., Chinatalapudi, N., Amenta, F. Applications of machine learning predictive models in the chronic disease diagnosis, J Personal Med. https://doi.org/10.3390/jpm10020021 (2020).
6. Abdar, M., Yen, N., Hung, J. Improving the diagnosis of liver disease using multilayer perceptron neural network and boosted decision tree, J Med Biol Eng 38:953–965 (2018).
7. Chaikijurajai, T., Laffin, L., Tang, W. Artificial intelligence and hypertension: recent advances and future outlook, Am J Hypertens 33:967–974 (2020).
8. Fujita, S., Hagiwara, A., Otsuka, Y., Hori, M., Takei, N., Hwang, K. P., Irie, R., Andica, C., Kamagata, K., Akashi, T., Kumamaru, K. K., Suzuki, M., Wada, A., Abe, O., Aoki, S. Deep Learning Approach for Generating MRA Images From 3D Quantitative Synthetic MRI Without Additional Scans, Invest Radiol 55:249–256 (2020).
9. Juarez-Chambi, R. M., Kut, C., Rico-Jimenez, J. J., Chaichana, L. K., Xi, J., Campos-Delgado, D. U., Rodriguez, F. J., Quinones-Hinojosa, A., Li, X., Jo, J. A. AI-Assisted In Situ Detection of Human Glioma Infiltration Using a Novel Computational Method for Optical Coherence Tomography, Clin Cancer Res 25(21):6329–6338 (2019).
10. Nashif, S., Raihan, R., Islam, R., Imam, M. H. Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System, World Journal of Engineering and Technology Vol 6, No. 4 (2018).
11. Chen, P. H. C., Gadepalli, K., MacDonald, R., Liu, Kadowaki, S., Nagpal, K., Kohlberger, T., Dean, J., Corrado, G. S., Hipp, J. D., Mermel, C. H., Stumpe, M. C. An augmented reality microscope with real time artificial intelligence integration for cancer diagnosis, Nat Med 25:1453–1457 (2019).
12. Gouda, W., Yasin, R. COVID-19 disease: CT Pneumonia Analysis prototype by using artificial intelligence, predicting the disease severity, Egypt J Radiol Nucl Med 51(1):196 (2020).
13. Han, Y., Han, Z., Wu, J., Yu, Y., Gao, S., Hua, D., Yang, A. Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology, IEEE Access 8:44924–44935 (2020).
14. Chui, C. S., Lee, N. P., Adeoye, J., Thomson, P., Choi, S. W. Machine learning and treatment outcome prediction for oral cancer, J Oral Pathol Med 49(10):977–985 (2020).
15. Koshimizu, H., Kojima, R., Okuno, Y. Future possibilities for artificial intelligence in the practical management of hypertension, Hypertens Res 43:1327–1337 (2020).
16. Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., Grabsch, H. I., Yoshikawa, T., Brenner, H., Chang-Claude, J., Hoffmeister, M., Trautwein, C., Luedde, T. Deep learning microsatellite instability directly from histology in gastrointestinal cancer, Nat Med 25:1054–1056 (2019).
17. Kwon, J. M., Jeon, K. H., Kim, H. M., Kim, M. J., Lim, S. M., Kim, K. H., Song, P. S., Park, J., Choi, R. K., Oh, B. H. Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography, EP Europace 22(3):412–419 (2020).
18. Khan, M. A. An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier, IEEE Access 8:34717–34727 (2020).
19. Oikonomou, E. K., Williams, M. C., Kotanidis, C. P., Desai, M. Y., Marwan, M., Antonopoulos, A. S., Thomas, K. E., Thomas, S., Akoumianakis, I., Fan, L. M., Kesavan, S., Herdman, L., Alashi, A., Centeno, E. H., Lyasheva, M., Griffin, B. P., Flamm, S. D., Shirodaria, C. Sabharwal, N., Kelion, A., Dweck, M. R., Van Beek, E. J. R., Deanfield, J., Hopewell, J. C., Neubauer, S., Channon, K. M., Achenbach, S., Newby, D. E., Antoniades, C. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography, Eur Heart J 40(43):3529–3543 (2019).
20. Sabottke, C. F., Spieler, B. M. The Effect of Image Resolution on Deep Learning in Radiography, Radiology: Artificial Intelligence Vol. 2. No. 1, 2:e190015 (2020).
21. Ekpar, F. E. A Comprehensive Artificial Intelligence-Driven Healthcare System, European Journal of Electrical Engineering and Computer Science, 8(3), Article 617. (2024).
22. Ekpar, F. E. Diagnosis of Chronic Kidney Disease Within a Comprehensive Artificial Intelligence-Driven Healthcare System, International Journal of Advanced Research in Computer and Communication Engineering, 13(9). (2024).
23. Ekpar, F. E. Image-based Chronic Disease Diagnosis Using 2D Convolutional Neural Networks in the Context of a Comprehensive Artificial Intelligence-Driven Healthcare System, Molecular Sciences and Applications, 4(13). (2024).
24. Ekpar, F. E. Leveraging Generative Artificial Intelligence Recommendations for Image-based Chronic Kidney Disease Diagnosis, International Journal of Advanced Research in Computer and Communication Engineering, 14(1). (2025).
25. Ekpar, F. E. A Novel Three-dimensional Multilayer Electroencephalography Paradigm, Fortune Journal of Health Sciences, 7(3). (2024).
26. Ekpar, F. E. System for Nature-Inspired Signal Processing: Principles and Practice, European Journal of Electrical Engineering and Computer Science, 3(6), pp. 1-10, (2019).
27. Ekpar, F. E. Nature-inspired Signal Processing, United States Patent and Trademark Office, US Patent Application Number: 13/674,035 (Filed: November 11, 2012, Priority Date: December 24, 2011), Document ID: US 20140135642 A1: Published: (2014).
28. Byra, M., Styczynski, G., Szmigielski, C., Kalinowski, P., Michałowski, L., Paluszkiewicz, R., Ziarkiewicz-Wróblewska, B., Zieniewicz, K., Sobieraj, P., Nowicki, A. Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images, International Journal of Computer Assisted Radiology and Surgery, 13:1895-1903. (2018).
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