Low-Latency Label-Free Image-Activated Cell Sorting Using Fast Deep Learning and Ai Inferencing

作     者：Tang, Rui Xia, Lin Gutierrez, Bien Gagne, Ivan Munoz, Adonary Eribez, Korina Jagnandan, Nicole Chen, Xinyu Zhang, Zunming Waller, Lauren Alaynick, William Cho, Sung Hwan An, Cheolhong Lo, Yu-Hwa 

作者机构：Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States NanoCellect Biomedical Inc. San DiegoCA92121 United States Department of Bioengineering University of California La Jolla San DiegoCA92093 United States 

出 版 物：《SSRN》 

年 卷 期：2022年

核心收录：

主　　题：Learning algorithms 

摘      要：Classification and sorting of cells using image-activated cell sorting (IACS) systems can bring significant insight to biomedical sciences. Incorporating deep learning algorithms into IACS enables cell classification and isolation based on complex and human-vision uninterpretable morphological features within a heterogeneous cell population. However, the limited capabilities and complicated implementation of deep learning–assisted IACS systems reported to date hinder the adoption of the systems for a wide range of biomedical research. Here, we present image-activated cell sorting by applying fast deep learning algorithms to conduct cell sorting without labeling. The overall sorting latency, including signal processing and AI inferencing, is less than 3 milliseconds, and the training time for the deep learning model is less than 30 minutes with a training dataset of 20,000 images. Both values set the record for IACS with sorting by AI inference. The system provides a compact, sterile, low-cost, label-free, and low-latency cell sorting solution based on real-time AI inferencing and fast training of the deep learning model. © 2022, The Authors. All rights reserved.