CAREER: Multiplexing Biomedical Sensor Systems for Automated Diagnosis by Machine Learning

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The diagnosis of human health conditions has always been challenging due to the complexity of human physiology. Therefore, multiple tests to identify multiple biomarkers are often required for diagnosis and prognosis of diseases. This process takes time and resources, including well-trained health providers, laboratory technicians to run the tests, and clinicians to interpret the results. To address this concern, this project will investigate multiplexed biomedical sensors that are capable of ultrasensitive detection of multiple biomarkers simultaneously so that health concerns could be identified in their early stages. The project will also investigate automated data analytics and diagnosis systems by utilizing machine learning algorithms for an accurate and unbiased interpretation of the test results. Finally, this project integrates research with educational and outreach initiatives designed to excite K-12 students about STEM opportunities and train undergraduate and graduate students in the area of biosensors, machine learning, and nanotechnology for biomedical stewardship.This CAREER project is to develop integrative biomedical sensors for automated diagnosis by synergistic integration of new sensing platforms for biomarker detection, as well as machine learning algorithms for accurate and unbiased interpretation of the results. Four main aims will be pursued: (1) study and fabricate plexcimonic structures, where plexcimons are plasmon-exciton-plasmon hybrid systems exhibiting new energy states that are suitable for multiplexed sensing, (2) develop a multiplexed sensing system by exploring plexcimonic structures with multiple biomarkers, (3) develop algorithms that correlate the sensor results for multiple biomarkers, and (4) create an advanced understanding of light-matter interactions in plexcimonic structures and machine learning in medical applications. This approach offers advantages over the conventional methods such as plasmonic sensors and ELISA that do not provide sensitivity and multiplexing simultaneously. The technology will also advance Point-of-Care systems through automated diagnosis. The educational component of this CAREER plan aims to develop an education program for light-matter interactions for nano-engineered structures. This effort will energize student learning by using nanostructures in laboratories, demonstrate a multidisciplinary perspective of biomedical sensors, and stimulate students' collaborative learning through biosensor projects. This activity will enhance recruitment and retention of underrepresented groups in STEM fields and will motivate students towards lifelong learning and careers related to biomedical disciplines, computer science, and engineering in general.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.