Study on artificial intelligence-based ultrasound diagnosis and auxiliary decision-making for ovarian tumors
10.3760/cma.j.cn131148-20250401-00179
- VernacularTitle:超声人工智能辅助诊断卵巢肿瘤良恶性的应用价值
- Author:
Chunli QIU
1
;
Yanlin CHEN
;
Yuanji ZHANG
;
Haotian LIN
;
Xiaoyi PAN
;
Siying LIANG
;
Xiang CONG
;
Xin LIU
;
Zhen MA
;
Cai ZANG
;
Xin YANG
;
Dong NI
;
Guowei TAO
Author Information
1. 山东大学齐鲁医院超声科，济南 250012
- Publication Type:Journal Article
- Keywords:
Ovarian tumor;
Ultrasonography;
Artificial intelligence;
Model assistance
- From:
Chinese Journal of Ultrasonography
2025;34(7):608-615
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.
