(277d) Digital Single Cell Profiling for Point-of-Care Cancer Diagnosis

The global burden of cancer, severe diagnostic bottlenecks in underserved regions, and underfunded health care systems are fueling the need for inexpensive, rapid and treatment-informative diagnostics. Based on advances in computational optics and deep learning, we have developed a low-cost digital system for breast cancer diagnosis of fine needle aspirates. Here, we show high accuracy in classifying breast cancer types using deep-learning based analysis of sample aspirates. The image algorithm is fast, enabling cellular analyses at high throughput, and the unsupervised processing allows use by lower skill health care workers. The system could be further developed for other cancers and thus find widespread use in resource limited settings to improve global health.

Reference
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[2] Kim S, Wang C, Zhao B, Im H, Min J, Choi H, Tadros J, Choi N, Castro CM, Weissleder R, Lee H*, Lee K*, Deep transfer learning-based hologram classification for molecular diagnostics, Scientific Reports, 8:17003 (2018).