An Artificial Intelligence System For The Detection And Characterization Of Early Liver Cancer
Grant Data
Project Title
An Artificial Intelligence System For The Detection And Characterization Of Early Liver Cancer
Principal Investigator
Professor Seto, Wai Kay Walter
(Project Coordinator (PC))
Co-Investigator(s)
Dr Chiu Wan Hang Keith
(Co-Investigator)
Professor Yuen Richard Man Fung
(Co-Investigator)
Professor Yu Philip Leung Ho
(Co-Investigator)
Emeritus Professor Li Wai Keung
(Co-Investigator)
Duration
23
Start Date
2019-05-02
Completion Date
2021-04-30
Amount
452990
Conference Title
An Artificial Intelligence System For The Detection And Characterization Of Early Liver Cancer
Keywords
An Artificial Intelligence System, Early Liver Cancer, The Detection And Characterization
Discipline
Others - Computing Science and Information Technology
HKU Project Code
InP/074/19
Grant Type
Innovation and Technology Fund Internship Programme
Funding Year
2018
Status
Completed
Objectives
Liver cancer, the third most common cause of cancer deaths in Hong Kong, is currently a disease of unmet needs with poor prognosis. Radiological diagnosis of early liver cancer is highly challenging, and often leads to delay in treatment. Our objective is to improve detection of liver cancer by developing an artificial intelligence algorithm that can automatically detect liver cancers on computed tomography (CT) scans. The algorithm, as an integrated computer-aided detection software, can be used to improve diagnostic accuracy of early liver cancer. There is currently no similar product in the market. Artificial intelligence algorithm will be developed by University of Hong Kong researchers with a track record in deep learning, based on anonymised images retrieved from 6 different institutes, and placed on a dedicated secured cloud server via a virtual private network. Processed scans will be fed back to the Radiology Information System as a fully end-to-end automatic output interface, with priority for reporting upgraded if liver cancer is suspected. The software has a high potential for commercialization and patent application. Applying in large healthcare systems e.g. Hospital Authority may lead to significant efficiency saving, reduction in diagnostic errors, improving patient care, and ultimately reducing liver cancer deaths.