HDPD Graduate Shining Bright at ICT Contest

Kudos to our Pharmaceutical Dispensing graduate (academic year 2014-2015), Miss NG Hoi Ting, Sheeta, on winning the Best Student Invention Sliver Award at the Hong Kong Information and Communications Technology (ICT) Awards 2016! The Hong Kong ICT Awards were established in 2006, steered by the Office of the Government Chief Information Officer and organised by Hong Kong ICT Industry associations and professional bodies. Being one of eight categories in the competition, the Best Student Invention Award provides a platform for students to demonstrate their creativity and innovation, and recognizes young ICT talents’ effort in invention. This year the Best Student Invention Award category attracted 828 entries, out of which a silver and two bronze awardees were selected, which means Sheeta beat all other competitors and came up top in the competition!

Miss Sheeta Ng (second from left) at the award presentation

Sheeta’s innovation was developing an automatic imaging tool on hand-held smart devices that could identify and verify oral pills. The project was a concerted effort between the Department of Health Sciences at CBCC and the Department of Information Engineering at the Chinese University of Hong Kong (CUHK). The idea conceptualization and pill image capturing were undertaken by our own Pharmaceutical Dispensing team, which includes Ms Windy Chan (Program Leader), Ms Garen Chan (Lecturer), Dr Wong Yuen Fei (Assistant Professor), and Miss Sheeta Ng, while the CUHK team contributed to algorithm development by employing the deep convolutional neural network model. Prof Eric Chan, Dean of Department of Health Sciences, congratulated his team on the effective collaboration and their achievement.

Ms Windy Chan, Ms Garen Chan and Dr Wong are pharmacists by profession. By developing such an automatic imaging tool, the trio wish that the chance of pill misidentification could be minimized, thereby reducing unwanted medical incidents. Dr Wong also stresses the importance of involving pharmaceutical dispensing students in such a project. Not only will it reinforce students’ memory on the physical presentations of the pills, the significance of accuracy in pill identification will also be instilled in students, who upon graduation will become an integral part of the health care system.

A total of 400 pills were captured at various angles for the training of the algorithm

The images were then analyzed using deep convolutional neural network model