Description of Project: It is estimated that there are more than 30,000 deaths related to accidental poisonings each year. While there are databases of prescription drugs, there have been no robust efforts to automate the identification of medications in clinical practice. We seek to implement and test a mobile application that uses image processing to identify medications.
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Deliverables: We will evaluate a mobile application and corresponding platform for the identification of medications in the SFGH ED.
- Mobile Application With
- Camera functionality to send images to a remote server
- A database of common FDA approved medications
- The ability to create local drug lists that can be used by individual patients to set reminders and find more information about their medications
- A Stand with lighting to optimize image acquisition for precise identification
- A Server that receives images and returns a ranked list of pills most likely to match based on shape, color, and character imprint (http://youtu.be/1APM1ZX1JHk)
Impact on UCSF’s mission and/or community: We have estimated that there are approximately 75 events of unidentifiable medications or medications that are brought to the SFGH ED each month. Such image processing can be used in the hospital, at local pharmacies, and by patients themselves to identify and look up information on medications. We believe there is significant opportunity for commercial integration and acquisition.
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List of team members and their roles:
David Ouyang, MS3 – Project Manager (200 hours)
Aron Yu – Electrical Engineering PhD student – Image Processing Developer (150 hours)
Dennis Qian – Mobile Developer (50 hours)
Janet Chu, MS2 – Designer and IRB Approval Facilitator (50 hours)
Rahul Deo, MD, PhD (Assistant Professor of Cardiology) – Statistical Analysis Expert (20hr)
Derrick Lung, MD, MPH (Assistant Professor of Emergency Medicine) – ER UI Expert (20hr)
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Comments
This project would certainly
Very intriguing project.
Apologies for the
In our search, we have not found any functional systems. The research projects have very controlled environments and are very dependent on lighting, these are things we are actively working on and feel like have a handle on. As you have shown, there is no image based system for pill identification on the market. A careful search from any of the solicitors of the FBO do not turn up any viable systems.
The system is currently focused on one pill at a time now, but there is no reason why it cannot scale to more pills - simply a matter of resources and priorities. We currently do not handle broken or partial pills.
For this project, we specifically want to see our app used in the EDs which can validate our approach and be useful for emergency room physicians.
here's a content based system
That is a great article