YOLOv5x deep learning network model based on SPECT whole body bone scanning for diagnosing benign and malignant bone lesions
10.13929/j.issn.1003-3289.2023.12.028
- VernacularTitle:基于SPECT全身骨扫描的YOLOv5x深度学习网络模型诊断良、恶性骨病灶
- Author:
Zonglin LI
1
;
Zheng ZHAO
;
Shidong LIAN
Author Information
1. 广西医科大学第二附属医院核医学科,广西南宁 530000
- Keywords:
boneand bones;
deep learning;
positron-emission tomography
- From:
Chinese Journal of Medical Imaging Technology
2023;39(12):1867-1871
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct YOLOv5x deep learning network model based on SPECT whole body bone scanning,and to observe its value for diagnosing benign and malignant bone lesions.Methods Totally 699 patient who underwent SPECT bone scanning were enrolled,with a total of 5 182 bone lesions,including 3 105 malignant and 2 077 benign lesions.Then 1 121 bone images were divided into training set(n=897),validation set(n=112)or test set(n=112)at the ratio of 8∶1∶1.After augmentation on training set and validation set,the data were inputted to YOLOv5x deep learning network for training to obtain a recognition model.The sensitivity,specificity and accuracy of this model for diagnosing benign and malignant bone lesions were analyzed,and the consistency between its diagnosis results and gold standards was evaluated based on test set.Results The sensitivity,specificity and accuracy of bone scanning YOLOv5x deep learning network model for identifying malignant bone lesions was 95.75％,87.87％and 91.60％,respectively,and for identifying benign bone lesions was 91.62％,94.38％and 93.14％,respectively.The area under the curve(AUC)of this model for identifying bone lesions on bone scanning images was 0.98,for malignant and benign bone lesions was 0.97 and 0.98,respectively.The consistency between the diagnosis results of this model for malignant and benign bone lesions and the gold standards were both good(Kappa=0.83,0.86,both P＜0.05).Conclusion YOLOv5x deep learning network model based on SPECT whole body bone scanning was helpful for diagnosing benign and malignant bone lesions.
