Xu Bin, Luo Weidong, Chen Xin, Jia Yiping, Wang Mengyuan, Tian Lulu, Liu Yi, Lei Bowen, Li Jiayuan
Department of Epidemiology and Health Statistics, West China School of Public Health, Sichuan University, Chengdu, China.
West China Fourth Hospital, Sichuan University, Chengdu, China.
Front Oncol. 2023 Jun 15;13:1207260. doi: 10.3389/fonc.2023.1207260. eCollection 2023.
To compare the accuracy of Artificial Intelligent Breast Ultrasound (AIBUS) with hand-held breast ultrasound (HHUS) in asymptomatic women and to offer recommendations for screening in regions with limited medical resources.
852 participants who underwent both HHUS and AIBUS were enrolled between December 2020 and June 2021. Two radiologists, who were unaware of the HHUS results, reviewed the AIBUS data and scored the image quality on a separate workstation. Breast imaging reporting and data system (BI-RADS) final recall assessment, breast density category, quantified lesion features, and examination time were evaluated for both devices. The statistical analysis included McNemar's test, paired t-test, and Wilcoxon test. The kappa coefficient and consistency rate were calculated in different subgroups.
Subjective satisfaction with AIBUS image quality reached 70%. Moderate agreements were found between AIBUS with good quality images and HHUS for the BI-RADS final recall assessment ( = 0.47, consistency rate = 73.9%) and breast density category ( = 0.50, consistency rate = 74.8%). The lesions measured by AIBUS were statistically smaller and deeper than those measured by HHUS ( < 0.001), though they were not significant in clinical diagnosis (all < 3 mm). The total time required for the AIBUS examination and image interpretation was 1.03 (95% (0.57, 1.50)) minutes shorter than that of HHUS per case.
Moderate agreement was obtained for the description of the BI-RADS final recall assessment and breast density category. With image quality comparable to that of HHUS, AIBUS was superior for the efficiency of primary screening.
比较人工智能乳腺超声(AIBUS)与手持式乳腺超声(HHUS)在无症状女性中的准确性,并为医疗资源有限地区的筛查提供建议。
2020年12月至2021年6月期间,852名同时接受了HHUS和AIBUS检查的参与者被纳入研究。两名不知HHUS检查结果的放射科医生在独立的工作站上查看AIBUS数据并对图像质量进行评分。对两种设备都评估了乳腺影像报告和数据系统(BI-RADS)最终召回评估、乳腺密度类别、量化的病变特征以及检查时间。统计分析包括McNemar检验、配对t检验和Wilcoxon检验。在不同亚组中计算kappa系数和一致性率。
对AIBUS图像质量的主观满意度达到70%。在图像质量良好的AIBUS与HHUS之间,对于BI-RADS最终召回评估(κ = 0.47,一致性率 = 73.9%)和乳腺密度类别(κ = 0.50,一致性率 = 74.8%)发现了中度一致性。AIBUS测量的病变在统计学上比HHUS测量的病变更小且更深(P < 0.001),尽管在临床诊断中并不显著(均 < 3 mm)。AIBUS检查和图像解读所需的总时间比HHUS每例短1.03(95%CI(0.57,1.50))分钟。
在BI-RADS最终召回评估和乳腺密度类别的描述方面获得了中度一致性。AIBUS图像质量与HHUS相当,在初筛效率方面更具优势。