The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China; School of Communication and Information Engineering, Shanghai University, Shanghai, China.
Department of Ultrasound, Zhongshan Hospital Fudan University, 200032, Shanghai, China; Shanghai Institute of Medical Imaging, 200032, Shanghai, China.
Med Eng Phys. 2023 Jan;111:103939. doi: 10.1016/j.medengphy.2022.103939. Epub 2022 Dec 6.
Differential diagnosis of enlarged lymph nodes (ELNs) is essential for the treatment of related patients. Though multi-modal ultrasound including B-mode, Doppler ultrasound, elastography and contrast-enhanced ultrasound (CEUS) can enhance diagnostic performance for ELNs, the scenario of having only single or dual modal data is often encountered. In this study, an artificially intelligent diagnosis model based on the learning using privileged information was proposed to aid in differential diagnosis of ELNs in the case of single or dual modal images. In our model, B-mode, or combined with another modality, was used as the standard information (SI) and other modalities were used as the privileged information (PI). The model was constructed through the combination of the SI and PI in the training stage. By learning from the training samples, a random vector functional link network with privileged information (RVFL+) was obtained, which was used to classify the testing samples of solely the SI. Results showed that the accuracy, precision and Youden's index of the RVFL+ model, using B-mode with elastography as the SI and CEUS as the PI, reached 78.4%, 92.4% and 54.9%, increased by 14.0%, 8.4% and 24.5% compared with the model using B-mode as the SI without the PI. The method based on the LUPI can improve the diagnostic performance for ELNs.
鉴别诊断增大的淋巴结（ELNs）对于相关患者的治疗至关重要。尽管多模态超声（包括 B 型、多普勒超声、弹性成像和超声造影）可以提高 ELNs 的诊断性能，但在实际临床中，通常只能获得单模态或双模态的数据。在这项研究中，我们提出了一种基于利用特权信息进行学习的人工智能诊断模型，以辅助单模态或双模态图像情况下的 ELNs 鉴别诊断。在我们的模型中，B 型或与另一种模态结合作为标准信息（SI），其他模态作为特权信息（PI）。该模型在训练阶段通过 SI 和 PI 的组合构建。通过从训练样本中学习，得到了一个具有特权信息的随机向量功能链接网络（RVFL+），用于对仅 SI 的测试样本进行分类。结果表明，当使用 B 型与弹性成像作为 SI、超声造影作为 PI 时，RVFL+模型的准确率、精确率和 Youden 指数分别达到 78.4%、92.4%和 54.9%，与未使用 PI 的仅 B 型作为 SI 的模型相比，分别提高了 14.0%、8.4%和 24.5%。基于 LUPI 的方法可以提高 ELNs 的诊断性能。
