| Info. | Vol.17 - No.1 (2023.03.20) |
|---|---|
| Title | Deep Learning-Assisted Droplet Digital PCR for Quantitative Detection of Human Coronavirus |
| Authors | Young Suh Lee1, Ji Wook Choi1, Taewook Kang2,3, Bong Geun Chung1,3 *Bong Geun Chung bchung@sogang.ac.kr |
| Institutions | 1Department of Mechanical Engineering, Sogang University, Seoul 04107, Korea
2Department of Chemical and Biomolecular Engineering, Sogang University, Seoul 04107, Korea 3Institute of Integrated Biotechnology, Sogang University, Seoul 04107, Korea |
| Abstract | Since coronavirus disease 2019 (COVID-19) pandemic rapidly spread worldwide, there is an urgent demand for accurate and suitable nucleic acid detection technology. Although the conventional threshold-based algorithms have been used for processing images of droplet digital polymerase chain reaction (ddPCR), there are still challenges from noise and irregular size of droplets. Here, we present a combined method of the mask region convolutional neural network (Mask R-CNN)-based image detection algorithm and Gaussian mixture model (GMM)-based thresholding algorithm. This novel approach significantly reduces false detection rate and achieves highly accurate prediction model in a ddPCR image processing. We demonstrated that how deep learning improved the overall performance in a ddPCR image processing. Therefore, our study could be a promising method in nucleic acid detection technology. |
| Keyword | ddPCR, Image processing, Deep learning, Mask R-CNN, GMM clustering |
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