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PathoCam: Point-of-Care Medicine with an Android Camera Phone

PathoCam: Point-of-Care Medicine with an Android Camera Phone Honey S. , Joongjae K. , Justin S. School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907 Advisors: JAN P. ALLEBACH, EDWARD J. DELP, RON REIFENBERGER

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PathoCam: Point-of-Care Medicine with an Android Camera Phone

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  1. PathoCam: Point-of-Care Medicine with an Android Camera Phone Honey S. , Joongjae K. , Justin S. School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907 Advisors: JAN P. ALLEBACH, EDWARD J. DELP, RON REIFENBERGER A VIP Project – Fall Semester 2013 • A GLOBAL NEED – PERSONALIZED MEDICAL CARE • Medical diagnostics comprise <5% of total hospital costs, yet they influence 60-70% of healthcare decisions. CAMERA PHONES - A WAY FORWARD? The impressive penetration of mobile cellular phones worldwide is driving rapid advances in telemedicine. Estimated 4.6 billion cell phones have been sold worldwide. QUESTION: Could camera phones be used to meet the increasing demand for inexpensive point-of-care medical tests that are easily administered by an untrained patient? GOAL: Develop a point-of-care medical test that relies on a dedicated ‘cell phone app’. IMPACT: Health care world-wide could be revolutionized. • In developing nations, expenditure on medical diagnostics is minimal. There is little interest in financing diagnostic development due to perceived lack of return on investment. • Highly sensitive tests are expensive and therefore inaccessible at remote point-of-care locations. • An innovative, disruptive, and inexpensive paradigm shift in diagnostic technology is required. PROJECT BACKGROUND – PATHOGEN IDENTIFICATION BY PATTERN RECOGNITION At Purdue an interdisciplinary team (Phil Low and Alex Wei, Chemistry; Yeong Kim and Ron Reifenberger, Physics) has developed a novel approach to point-of-care bacterial identification. Chips patterned with immutable ligands capture targeted bacteria into pre-determined patterns that can be identified remotely using pattern recognition algorithms. The technique is fault-tolerant, robust and simple, requiring only the analysis of camera phone images of the chip after exposure. The five-step approach is summarized below: PATHOCAM 'APP ' 1. Distribute patterned chip 2. Expose to test fluid (15 mins.) 4. Cell Phone Imaging 5. Central location for pattern analysis. Results returned directly to point of origin. 3. Captured bacteria forms pattern . Current Development Use Unique Filename: IMEI_DateTime.jpg Image Saved in PathoCam Folder Input User Email Address Confirm Image Receive Feedback Capture Chip Image Home Screen

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