Efficient evaluation of photodynamic therapy on tumor based on deep learning

Abstract

Photodynamic therapy (PDT) is a non-invasive treatment method for treating tumors. Under laser irradiation, photosensitizers in tumor tissues generate biotoxic reactive oxygen, which can kill tumor cells. The traditional live/dead staining method of evaluating the cell mortality caused by PDT mainly depends on manual counting, which is time-consuming and relies on dye quality. In this paper, we have constructed a dataset of cells after PDT treatment and trained the cell detection model YOLOv3 to count both the dead and live cells. YOLO is a real time AI object detection algorithm. The achieved results demonstrate that the proposed method has a good performance in cell detection, with a mean average precision (mAP) of 94% for live cells and 71.3% for dead cells. This approach can efficiently evaluate the effectiveness of PDT treatment, thus speeding up treatment development effectively.

Keywords: Deep learning; Nanomedicine; Object detection; PDT; Tumor cells.