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Publicity Highlights
RAPIER – Radiology Pathology Information Exchange Resource
01 Jul 2024
Project RAPIER (Radiology Pathology Information Exchange Resource) aims to create a RadPath datalake on liver lesions and myriad deployable AI-driven applications which can detect, describe features and diagnose liver abnormalities, catalysing next generation AI-enabled clinical decision support, predictive analytics, and precision medical care.
Image credit: AI Singapore
Problem Scope
Radiology and pathology archives are huge treasure troves of clinical images and text reports. However, as such data are mostly in silos, the potential for AI to tap such information is limited.
Project RAPIER aims to create a datalake hosting the RadPath archives of images and reports for liver pathology in SGH campus. This datalake will be used to develop AI algorithms that can detect, describe lesion features and diagnose liver abnormalities on RadPath images.
The key objectives of this project are to develop AI-assisted data labeling tools, liver lesion analysis, reporting of CT/MRI and digital pathology images, and liver lesion progression prediction.
Design Approach
An AI-assisted data labelling solution will first be developed to assist clinicians in labelling medical RadPath images. The final labels applied by clinicians would be used for training to iteratively improve the system. Longitudinal RadPath data of patients over time will also allow AI algorithms to develop predictive analytic capability for disease progression.
Potential Impact/Benefits to Target Sector
The tools from this project will unlock previously untapped medical data across the RadPath domains to facilitate AI model training and validation. The suite of well-trained AI algorithms can augment specialists for early detection, accurate diagnosis, and prompt treatment of liver disease, thus improving accuracy and report turnaround, and healthcare outcomes. In addition, the capabilities developed in RAPIER can be applied to many other diseases beyond the liver.
Lead PI: Dr Liu Yong (A*STAR)
Co-PIs
Xu Xinxing (IHPC, A*STAR)
Fu Huazhu (IHPC, A*STAR)
Zhen Liangli (IHPC, A*STAR)
Li Bing (IHPC, A*STAR)
Rick Goh Siow Mong (IHPC, A*STAR)
Li Shaohua (IHPC, A*STAR)
Host Institution: Institute of High Performance Computing (IHPC), A*STAR
Industry Partners
Singapore General Hospital (SGH)
National Cancer Centre Singapore (NCCS)
Medical Principal Investigators
Dr Lionel Cheng Tim-Ee, Department of Diagnostic Radiology, SGH
Dr Tony Lim Kiat Hon, Department of Anatomical Pathology, SGH
Medical Investigators
Dr Gideon Ooi Su Kai, Division of Oncologic Imaging, NCCS
Dr Thng Choon Hua, Division of Oncologic Imaging, NCCS
Dr Khor Li Yan, Department of Anatomical Pathology, SGH
Dr Tran Nguyen Tuan Anh, Diagnostic Radiology, SGH
Dr Leow Wei Qiang, Department of Anatomical Pathology, SGH
Dr Shi Ruoyu, Department of Anatomical Pathology, SGH
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