Efficient evaluation of photodynamic therapy on tumor based on deep learning.

Journal: Photodiagnosis and photodynamic therapy
PMID: 37339692

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.

Authors

  • Shuangshuang Lv
    College of Electronic Engineering, Beijing University of Posts and Telecommunications, Xitucheng Road. Haidian Dist, Beijing 100876, China.
  • Xiaohui Wang
    School of Kinesiology, Shanghai University of Sport, Shanghai 200438, China.
  • Guisheng Wang
    From the Department of Radiology, Wuhan Huangpi People's Hospital, Wuhan, China (L.L., Z.X., X.F., S.Z., Juan Xia); Jianghan University Affiliated Huangpi People's Hospital, Wuhan, China (L.L.); Department of Radiology, Wuhan Pulmonary Hospital, Wuhan, China (L.Q.); Keya Medical Technology Co, Ltd, Shenzhen, China (Y.Y., X.W., B.K., J.B., Y.L., Z.F., Q.S., K.C.); Department of Radiology, Liaocheng People's Hospital, Liaocheng, China (D.L.); Department of CT, The Third Medical Center of Chinese PLA General Hospital, Beijing, China (G.W.); and Department of Radiology, Shenzhen Second People's Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China 518035 (Q.X., Jun Xia).
  • Wei Yang
    Key Laboratory of Structure-Based Drug Design and Discovery (Shenyang Pharmaceutical University), Ministry of Education, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Wenhua Road 103, Shenyang 110016, PR China. Electronic address: 421063202@qq.com.
  • Kun Cheng
    Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China.

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