# RAPIER: Radiology-Pathology Imaging Exchange Resource

- Date: 2025-07-01
- Who: AI Singapore (AI research and talent-development organisation) · AI Singapore
- Source: https://www.youtube.com/watch?v=3gSNJhrRQt0
- sgai: https://sgai.md/videos/v049/
- License: sgai-authored content (summaries, translations, analysis) is CC BY 4.0 — attribute and link to sgai.md. Verbatim source text (Hansard, speeches, transcripts, policy documents) remains © its original rights holders and is reproduced for reference only. Terms: https://github.com/meltflake/sgai/blob/main/DATA-LICENSE.md

## Why it matters

STEATstat progressed from an AISG project to actual deployment in SGH's pathology department, becoming one of Singapore's rare examples of AI research converting to a clinical tool, particularly crucial for the 30% affected MAFLD population

## Summary

AISG partners with A*STAR, SGH and NCCS to develop AI algorithms that automatically detect, describe and diagnose liver lesions from radiology and pathology archives.

## Key points

- Metabolic dysfunction-associated steatotic liver disease (MASLD) affects roughly 30% of adults globally — the most common chronic liver disease.
- AISG, A*STAR, SGH and NCCS built STEATstat, an AI tool that quantifies liver fat in biopsies and grades disease severity.
- STEATstat is deployed in the Singapore General Hospital anatomical pathology lab, helping pathologists assess fatty liver disease faster and more precisely.

## Full text

© AI Singapore — reproduced for reference only.

Metabolic dysfunction associated stootic liver disease is now the most common chronic liver disease worldwide with an estimated 30% of the global adult population affected. It is also common in Singapore and projections suggest significant increase in the cases in the years to come. Of more concern is the potential associated clinical burden of decompensated cerosis, liver cancer and liver mortality cases. Imaging and hystopathology tools play a critical role in the diagnosis and risk stratification of patients, guiding clinicians to better manage patients and improving outcomes in liver disease. Assessment of the severity of metabolic dysfunction associated stic liver disease or muscle D is performed by a hisystopathologist who will examine the liver biopsy tissue under high magnification with a microscope.

The liver tissue is evaluated for the presence of fat or hippatic sttosis among other features such as fibrosis, inflammation and liver cell damage. Special hisystochemical stains may be utilized to highlight such features. Aside from identifying these features, the hystopathologist will quantify and grade the liver fat into categories of severity. Our assistive AI fat estimation tool atly named STO stat will allow for precise quantification of liver fat and reduces interobserver favorability. This increases the objectivity of hystopathologists allowing for accurate grading of disease which predicts if the patient will be at risk of progressive liver disease such as cerosis or liver cancer ultimately resulting in better patient care.

STEAT is now deployed at the Singapore General Hospital anatomical pathology laboratory assisting pathologist to make faster and more precise assessments of fatty liver disease. [Music]