Abstract:

Objective To analyze the feasibility of using the YOLOV8 deep learning（DL） model to rapidly identify cell images of acute promyelocytic leukemia（APL）. Methods Bone marrow smears from 56 untreated patients diagnosed with APL at the Affiliated Qingyuan Hospital of Guangzhou Medical University from 2004 to 2023 were collected. Medical image capture software was used to take pictures，and 1 551 clear images were collected and labeled with cell types（ZYLXB and Other） by Make Sense software. A total of 5 041 abnormal promyelocyte images and 1 606 other-type cell images were labeled. Totally，4 033 abnormal promyelocyte images and 1 330 other-type cell images were randomly selected as the training set，and the remaining images after training were included in the validation set. A YOLOV8 DL model was established to evaluate the model's ability to rapidly identify APL cell images. Results The accuracy of the YOLOV8 DL model in identifying abnormal promyelocytes reached 91%，and the precision gradually increased with the increase of the confidence threshold，reaching a peak at approximately 0.9. Conclusions The YOLOV8 DL model can rapidly and accurately identify APL images.

Key words: YOLOV8 deep learning, Acute promyelocytic leukemia, Bone marrow smear