Li Jian, Jin Linyuan, Wang Zhiyuan, Peng Qinghai, Wang Yueai, Luo Jia, Zhou Jiawei, Cao Yingying, Zhang Yanfen, Zhang Min, Qiu Yuewen, Hu Qiang, Chen Liyun, Yu Xiaoyu, Zhou Xiaohui, Li Qiong, Zhou Shu, Huang Si, Luo Dan, Mao Xingxing, Yu Yi, Yang Xiaomeng, Pan Chiling, Li Hongxin, Wang Jingchao, Liao Jieke
Department of Ultrasound, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China.
Department of Ultrasound, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China.
NPJ Digit Med. 2023 Feb 3;6(1):18. doi: 10.1038/s41746-023-00759-1.
We developed a continuous learning system (CLS) based on deep learning and optimization and ensemble approach, and conducted a retrospective data simulated prospective study using ultrasound images of breast masses for precise diagnoses. We extracted 629 breast masses and 2235 images from 561 cases in the institution to train the model in six stages to diagnose benign and malignant tumors, pathological types, and diseases. We randomly selected 180 out of 3098 cases from two external institutions. The CLS was tested with seven independent datasets and compared with 21 physicians, and the system's diagnostic ability exceeded 20 physicians by training stage six. The optimal integrated method we developed is expected accurately diagnose breast masses. This method can also be extended to the intelligent diagnosis of masses in other organs. Overall, our findings have potential value in further promoting the application of AI diagnosis in precision medicine.
我们基于深度学习、优化和集成方法开发了一种持续学习系统（CLS），并使用乳腺肿块的超声图像进行了一项回顾性数据模拟前瞻性研究，以进行精确诊断。我们从该机构的561例病例中提取了629个乳腺肿块和2235张图像，分六个阶段训练模型以诊断良性和恶性肿瘤、病理类型及疾病。我们从两个外部机构的3098例病例中随机选取了180例。CLS在七个独立数据集上进行了测试，并与21名医生进行了比较，到第六训练阶段时该系统的诊断能力超过了20名医生。我们开发的最优集成方法有望准确诊断乳腺肿块。该方法还可扩展至其他器官肿块的智能诊断。总体而言，我们的研究结果在进一步推动人工智能诊断在精准医学中的应用方面具有潜在价值。
