Next Article in Journal
Emerging Pathways to Non-Invasive Diagnosis in Endometriosis: Integrating Machine Learning, Deep Learning and Multi-Omics Biomarkers
Previous Article in Journal
AI-Based Prediction of Post-ERCP Pancreatitis: A Comparative Study Using Tabular, Image, and Multimodal Data
Previous Article in Special Issue
Artificial Intelligence-Assisted Quantification of Longitudinal HRCT Changes During Treatment of Pulmonary Tuberculosis: An Exploratory Proof-of-Concept Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 16
Issue 12
10.3390/diagnostics16121825
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Real-World Insights in Designing SteatoStat: An End-to-End Deep Learning Pipeline for Hepatic Steatosis Quantification

by
Nagalakshmi Jegannathan
1,†,
Xiaoman Zhang
2,†,
Jia Xuan Seow
1,
Menghan Zhou
2,
Long Wang
2,
Guo Lin Goh
3,
Seow Ye Heng
1,
Tony De Rong Ng
4,
Rick Siow Mong Goh
2,
Huazhu Fu
2,
Yong Liu
2,
Lionel Tim-Ee Cheng
5,6,
George Boon Bee Goh
7,
Dean Tai
8,
Chee Leong Cheng
1,
Wei Keat Wan
1,
Tony Kiat Hon Lim
1,6,
Li Yan Khor
1,6 and
Wei Qiang Leow
1,3,6,*
1
Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
2
Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore 138632, Singapore
3
School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore
4
Ministry of Health Holdings, Singapore 139691, Singapore
5
Department of Cardiothoracic and Abdominal Radiology, Singapore General Hospital, Singapore 169608, Singapore
6
Duke-NUS Medical School, Singapore 169857, Singapore
7
Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore 169608, Singapore
8
HistoIndex Pte., Ltd., Singapore 117674, Singapore
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diagnostics 2026, 16(12), 1825; https://doi.org/10.3390/diagnostics16121825 (registering DOI)
Submission received: 28 January 2026 / Revised: 9 June 2026 / Accepted: 9 June 2026 / Published: 12 June 2026
(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine—2nd Edition)
Browse Figure
Versions Notes

Abstract

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a significant and escalating global health concern, with an estimated prevalence of 30%. Current assessments of hepatic steatosis, a hallmark of MASLD, rely on semi-quantitative grading by pathologists, which is inherently limited by inter-observer variability. Objective: To address this limitation, we developed a novel deep learning pipeline, named SteatoStat, to standardize and enhance the quantification of hepatic steatosis in patients with MASLD. Method: The SteatoStat pipeline employs and integrates multiple components such as file format standardization, rule-based cell filtering, and multiple segmentation models across various liver structures, resulting in an output of a continuous quantitative measure of steatosis percentage and translated into steatosis grades. Results: We report a high degree of accuracy and reliability with SteatoStat achieving the following performance metrics (DICE score = 0.8955, AUROC = 0.9928, F1 score = 0.8990). When benchmarked against expert pathologists, the weighted Kappa coefficient is 0.837. Furthermore, in comparison with an existing, well-established model, SteatoStat demonstrated a weighted Kappa coefficient = 0.765. Conclusions: These robust findings underscore its potential clinical utility in providing a standardized objective quantification of hepatic steatosis. Future directions include enhancing the model’s generalizability and its clinical integration through validation on independent, multi-institutional datasets.
Keywords: deep learning pipeline; hepatic steatosis; MASLD; segmentation model deep learning pipeline; hepatic steatosis; MASLD; segmentation model
Graphical Abstract

Share and Cite

MDPI and ACS Style

Jegannathan, N.; Zhang, X.; Seow, J.X.; Zhou, M.; Wang, L.; Goh, G.L.; Heng, S.Y.; Ng, T.D.R.; Goh, R.S.M.; Fu, H.; et al. Real-World Insights in Designing SteatoStat: An End-to-End Deep Learning Pipeline for Hepatic Steatosis Quantification. Diagnostics 2026, 16, 1825. https://doi.org/10.3390/diagnostics16121825

AMA Style

Jegannathan N, Zhang X, Seow JX, Zhou M, Wang L, Goh GL, Heng SY, Ng TDR, Goh RSM, Fu H, et al. Real-World Insights in Designing SteatoStat: An End-to-End Deep Learning Pipeline for Hepatic Steatosis Quantification. Diagnostics. 2026; 16(12):1825. https://doi.org/10.3390/diagnostics16121825

Chicago/Turabian Style

Jegannathan, Nagalakshmi, Xiaoman Zhang, Jia Xuan Seow, Menghan Zhou, Long Wang, Guo Lin Goh, Seow Ye Heng, Tony De Rong Ng, Rick Siow Mong Goh, Huazhu Fu, and et al. 2026. "Real-World Insights in Designing SteatoStat: An End-to-End Deep Learning Pipeline for Hepatic Steatosis Quantification" Diagnostics 16, no. 12: 1825. https://doi.org/10.3390/diagnostics16121825

APA Style

Jegannathan, N., Zhang, X., Seow, J. X., Zhou, M., Wang, L., Goh, G. L., Heng, S. Y., Ng, T. D. R., Goh, R. S. M., Fu, H., Liu, Y., Cheng, L. T.-E., Goh, G. B. B., Tai, D., Cheng, C. L., Wan, W. K., Lim, T. K. H., Khor, L. Y., & Leow, W. Q. (2026). Real-World Insights in Designing SteatoStat: An End-to-End Deep Learning Pipeline for Hepatic Steatosis Quantification. Diagnostics, 16(12), 1825. https://doi.org/10.3390/diagnostics16121825

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop