Title: Machine Learning-Based Disease Risk Stratification and Prediction of Metabolic
Dysfunction-Associated Fatty Liver Disease Using Vibration-Controlled Transient
Elastography: Result from NHANES 2021–2023
Version: 1 Date: 03 Mar 2025
Reviewer's report:
Machine learning and risk stratification for model predicting MAFLD.
This is a well written paper thank you. It adds to the growing body of work in the area. I have
a query:
Did you compare your model’s performance on the validation data set to other AI models? Or
with non-invasive biomarkers that are in use? Such as ELF, FIB-4 etc. It would interesting to
compare and comment on the differences in performance. See below:
1. Blanes-Vidal, V., Lindvig, K.P., Thiele, M. et al. Artificial intelligence outperforms standard
blood-based scores in identifying liver fibrosis patients in primary care. Sci Rep 12, 2914 (2022).
https://doi.org/10.1038/s41598-022-06998-8
2. Wong GL, Yuen PC, Ma AJ, Chan AW, Leung HH, Wong VW. Artificial intelligence
in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol. 2021
Mar;36(3):543-550. doi: 10.1111/jgh.15385. PMID: 33709607.
Title: Machine Learning-Based Disease Risk Stratification and Prediction of Metabolic
Dysfunction-Associated Fatty Liver Disease Using Vibration-Controlled Transient
Elastography: Result from NHANES 2021–2023
Version: 1 Date: 03 Mar 2025
Reviewer's report:
Machine learning and risk stratification for model predicting MAFLD.
This is a well written paper thank you. It adds to the growing body of work in the area. I have
a query:
Did you compare your model’s performance on the validation data set to other AI models? Or
with non-invasive biomarkers that are in use? Such as ELF, FIB-4 etc. It would interesting to
compare and comment on the differences in performance. See below:
1. Blanes-Vidal, V., Lindvig, K.P., Thiele, M. et al. Artificial intelligence outperforms standard
blood-based scores in identifying liver fibrosis patients in primary care. Sci Rep 12, 2914 (2022).
https://doi.org/10.1038/s41598-022-06998-8
2. Wong GL, Yuen PC, Ma AJ, Chan AW, Leung HH, Wong VW. Artificial intelligence
in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol. 2021
Mar;36(3):543-550. doi: 10.1111/jgh.15385. PMID: 33709607.
Please review this paper.
user
Title: Machine Learning-Based Disease Risk Stratification and Prediction of Metabolic
Dysfunction-Associated Fatty Liver Disease Using Vibration-Controlled Transient
Elastography: Result from NHANES 2021–2023
Version: 1 Date: 03 Mar 2025
Reviewer's report:
Machine learning and risk stratification for model predicting MAFLD.
This is a well written paper thank you. It adds to the growing body of work in the area. I have
a query:
Did you compare your model’s performance on the validation data set to other AI models? Or
with non-invasive biomarkers that are in use? Such as ELF, FIB-4 etc. It would interesting to
compare and comment on the differences in performance. See below:
1. Blanes-Vidal, V., Lindvig, K.P., Thiele, M. et al. Artificial intelligence outperforms standard
blood-based scores in identifying liver fibrosis patients in primary care. Sci Rep 12, 2914 (2022).
https://doi.org/10.1038/s41598-022-06998-8
2. Wong GL, Yuen PC, Ma AJ, Chan AW, Leung HH, Wong VW. Artificial intelligence
in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol. 2021
Mar;36(3):543-550. doi: 10.1111/jgh.15385. PMID: 33709607.
assistant