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Diagnosing Infectious Diseases Using Deep Learning and Time Series Imaging with Wearable Monitors in Resource-Limited Settings

Speaker: Dr. Ping Lu

Tetanus is a life-threatening infectious disease still common in low- and middle- income countries (LMICs) that kills up to 500,000 people every year. Artificial intelligence, including machine learning and deep learning methods, has revolutionized healthcare for diagnosing and classifying the severity of infectious diseases. Time series imaging is a popular technology which transforms time series data into images.  Deep learning and time series imaging can improve intervention times and symptom classification for infectious diseases with high mortality. This work is highly valuable for inexperienced or overloaded staff in LMICs, such as Vietnam, because it could avoid unnecessary ICU admissions and reduce treatment delays.

Speaker bio

Dr. Ping Lu has been awarded the degree of PhD in Biomedical Engineering at the University of Bern (Switzerland) with the Latin honors magna cum laude. Particular focus was given on advanced medical image analysis of the human facial nerve based on machine learning technologies, in order to improve cochlear implantation image-guided planning.

Having joined the Institute of Biomedical Engineering at the University of Oxford in 2018, Ping worked on the SmartHeart project and developed spatio-temporal convolutional networks for cardiac motion estimation and regional analysis of left ventricular function, to characterize differences between healthy and diseased hearts.

Her current research focuses on the development of medical technologies for improving care in low- and middle- income countries (LMICs). The work is improving timely intervention and rapid triage for infectious diseases with high mortality – as a result the expected impact on quality of life is high particularly for the most disadvantaged populations who are disproportionately affected by the diseases she is working on. She collaborates with clinicians at Oxford University Clinical Research Unit - focusing on deep learning applied to healthcare using time-series sensor data. She leads the development of deep learning models for the prediction of infectious disease.



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