人工智能辅助诊断模型在乳腺结节超声诊断中的应用价值  

Application value of artificial intelligence-assisted diagnosis model in ultrasound diagnosis of breast nodules

作　　者：冀鸿涛[1] 朱强[1] 甘从贵 周帅[1] 程昀[1] 赵明昌 Ji Hongtao;Zhu Qiang;Gan Conggui;Zhou Shuai;Cheng Yun;Zhao Mingchang(Department of Ultrasound,Beijing Tongren Hospital of Capital Medical University,Beijing 100730,China;Chison Medical Technologies Co.LTD,Wuxi 214142,China)

机构地区：[1]首都医科大学附属北京同仁医院超声诊断科,100730 [2]无锡祥生医疗科技股份有限公司,214142

出　　处：《肿瘤研究与临床》2019年第10期649-652,共4页Cancer Research and Clinic

基　　金：国家重点研发计划(2016YFC0104803)。

摘　　要：目的探讨基于卷积神经网络(CNN)构建的人工智能辅助诊断模型在乳腺结节良恶性超声鉴别诊断中的应用价值。方法利用CNN构建的人工智能辅助诊断模型,从首都医科大学附属北京同仁医院超声影像数据库中调取2006年12月至2017年7月1351例乳腺结节患者(良性807例,恶性544例)的7334张超声图像,分成训练集(6162张)、验证集(555张)和测试集(617张),对人工智能辅助诊断模型进行训练、验证及测试。将诊断模型测试集输出结果与病理结果对照,计算人工智能辅助诊断模型的敏感性、特异性和准确性。结果经过对测试集中617张图像进行测试,每个结节的模型诊断结果可自动输出,结节位置、良恶性诊断和良恶性概率值结果均以矩形框标示出,每个结节诊断时间约为4 s。该诊断模型对于乳腺结节良恶性诊断的灵敏度为84.1%,特异度为95.0%,准确率为91.2%。结论基于CNN构建的人工智能辅助诊断模型在乳腺结节超声良恶性鉴别诊断中取得了令人满意的结果,具有良好应用前景。Objective To explore the application value of the convolutional neural network(CNN)-based artificial intelligence-assisted diagnosis model in the ultrasound differentiation diagnosis of benign and malignant breast nodules.Methods A total of 7334 ultrasound images from 1351 patients with breast nodules including 807 benign cases and 544 malignant cases were retrieved by using the CNN-based artificial intelligence-assisted diagnosis model from Beijing Tongren Hospital of Capital Medical University ultrasound images database between December 2006 and July 2017.The study included training subset(6162 images),verification subset(555 images),and test subset(617 images),which were performed in the artificial intelligence-assisted diagnosis model.The outcome results of test subset in diagnosis model were compared with the pathological results.The sensitivity,specificity and accuracy of the artificial intelligence-assisted diagnosis model were calculated.Results After the test of 617 images,the model diagnostic results could be automatically output with a rectangular frame indicating the nodule position,benign and malignant diagnosis,benign and malignant probability values.The diagnosis time was approximately 4 seconds for each nodule.The sensitivity,specificity and accuracy of the diagnostic model in differentiating benign and malignant breast nodules were 84.1%,95.0%and 91.2%,respectively.Conclusion The CNN-based artificial intelligence-assisted diagnosis model has satisfactory results in the differentiation diagnosis of the benign breast nodules and the malignant ones,which indicating the promising application prospect.

关 键 词：乳腺肿瘤 卷积神经网络 人工智能 辅助诊断 

分 类 号：R73[医药卫生—肿瘤]