高级检索
当前位置: 首页 > 详情页

Accuracy of breast ultrasound image analysis software in feature analysis: a comparative study with sonographers

文献详情

资源类型:
WOS体系:
Pubmed体系:

收录情况: ◇ SCIE

机构: [1]Department of Public Health, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, 1111 Xianxia Road, Shanghai 200335, China. [2]School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [3]Project Department, Tend.AI Medical Technologies Co., Ltd, Shanghai, China. [4]Department of General Practice, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
出处:

关键词: Breast cancer Ultrasound Artificial intelligence Proportion of agreement Comparative study

摘要:
Breast ultrasound is recommended for early breast cancer detection in China, but the rapid increase in imaging data burdens sonographers. This study evaluated the agreement between artificial intelligence (AI) software and sonographers in analyzing breast nodule features. Breast ultrasound images from two hospitals in Shanghai were analyzed by both the software and the sonographers for features including echotexture, echo pattern, orientation, shape, margin, calcification, and posterior echo attenuation. Agreement between software and sonographers was compared using the proportion of agreement and Kappa, with analysis time also evaluated. A total of 493 images were analyzed. The proportion of agreement between software and sonographers in assessing features was 80.5% for echotexture, 84.4% for echo pattern, 93.7% for orientation, 85.8% for shape, 88.6% for margin, 80.5% for calcification, and 90.5% for posterior echo attenuation, highlighting software's high accuracy. Cohen's kappa for other features indicated moderate to substantial agreement (0.411-0.674), with calcification showing fair agreement (0.335). The software significantly reduced analysis time compared to sonographers (P < 0.001). The software showed high accuracy and time efficiency. AI software presents a viable solution for reducing sonographers' workload and enhance healthcare in underserved areas by automating feature analysis in breast ultrasound images.© 2024. The Author(s).

基金:
语种:
WOS:
PubmedID:
中科院(CAS)分区:
出版当年[2023]版:
大类 | 2 区 综合性期刊
小类 | 2 区 综合性期刊
最新[2023]版:
大类 | 2 区 综合性期刊
小类 | 2 区 综合性期刊
JCR分区:
出版当年[2022]版:
Q2 MULTIDISCIPLINARY SCIENCES
最新[2023]版:
Q1 MULTIDISCIPLINARY SCIENCES

影响因子: 最新[2023版] 最新五年平均 出版当年[2022版] 出版当年五年平均 出版前一年[2021版] 出版后一年[2023版]

第一作者:
第一作者机构: [1]Department of Public Health, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, 1111 Xianxia Road, Shanghai 200335, China. [2]School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
共同第一作者:
通讯作者:
推荐引用方式(GB/T 7714):
APA:
MLA:

资源点击量:21434 今日访问量:0 总访问量:1221 更新日期:2025-02-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 首都医科大学附属北京同仁医院 技术支持:重庆聚合科技有限公司 地址:北京市东城区东交民巷1号(100730)