| 何方舟,牛 凯,唐 顺,张熠丹,谢 璐,王冀川,夏楚藜,赵志庆,贺志强,郭 卫.基于X线图像的膝关节周围原发性骨肿瘤辅助诊断的机器学习模型研究[J].,2021,(15):2842-2847 |
| 基于X线图像的膝关节周围原发性骨肿瘤辅助诊断的机器学习模型研究 |
| Study on Machine Learning Model of Primary Bone Tumor Around Knee Joint Assisted Diagnosis Based on X-ray Images |
| 投稿时间：2021-03-21 修订日期：2021-04-15 |
| DOI：10.13241/j.cnki.pmb.2021.15.009 |
| 中文关键词: 骨肿瘤 机器学习 诊断 |
| 英文关键词: Bone tumor Machine learning Diagnosis |
| 基金项目:北京市自然科学基金项目（7182172） |
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| 中文摘要: |
| 摘要 目的：开发机器学习模型，并评估其在膝关节周围原发性骨肿瘤诊断方面的准确性。方法：本文将深度卷积神经网络（DCNN）这一深度学习方法应用于膝关节X线图像的影像组学分析，探讨其辅助诊断膝关节周围原发性骨肿瘤的临床价值。结果：该深度学习模型在区分正常与肿瘤影像方面展现出优异的诊断准确性，使用DCNN模型进行5轮测试的总体准确性为（99.8±0.4）%，而阳性预测值和阴性预测值分别为（100.0±0.0）%和（99.6±0.8）%，各个数据集的曲线下面积（AUC）分别为0.99、1.00、1.00、1.0和1.0，平均AUC为（0.998±0.004）；进一步使用DCNN模型进行了10轮测试显示其在区分良性与恶性骨肿瘤方面的总体准确性为（71.2±1.6）%，且达到了强阳性预测值（91.9±8.5）%，各个数据集的AUC分别为0.63、0.63、0.58、0.69、0.55、0.63、0.54、0.57、0.73、0.63，平均AUC为（0.62±0.06）。结论：本文是首个将人工智能技术应用于骨肿瘤诊断的X线图像影像组学分析方面的研究，人工智能影像组学模型能够帮助医生自动地快速筛查骨肿瘤，确定良性或恶性肿瘤时，阳性预测值较高。 |
| 英文摘要: |
| ABSTRACT Objective: Develop a machine learning model, and to evaluate its accuracy in the diagnosis of primary bone tumor around the knee joint. Methods: In this paper, the deep learning method of deep convolutional neural network (DCNN) was applied to the imagomics analysis of knee X-ray images, its clinical value in assisting the diagnosis of primary bone tumor around the knee joint were explored. Results: The deep learning model showed excellent diagnostic accuracy in differentiating normal images from tumor images. The overall accuracy of 5 rounds of testing using the DCNN model was(99.8±0.4) %, while the positive predictive value and negative predictive value were (100.0±0.0) % and (99.6±0.8) % respectively. The area under the curve (AUC) of each dataset was 0.99, 1.00, 1.00, 1.0 and 1.0 respectively, average AUC was (0.998±0.004). A further 10 rounds of testing using the DCNN model showed an overall accuracy of (71.2±1.6) % in differentiating benign from malignant bone tumors, and a strong positive predictive value (91.9±8.5) % was achieved in differentiating benign and malignant bone tumor, the AUC of each dataset was 0.63, 0.63, 0.58, 0.69, 0.55, 0.63, 0.54, 0.57, 0.73, 0.63 respectively, average AUC was(0.62±0.06). Conclusion: This is the first study on the application of artificial intelligence technology in the imaging analysis of X-ray images for bone tumor diagnosis, Artificial intelligence imaging models can help doctors to automatically and quickly screen bone tumors and identify benign or malignant tumors, with a high positive predictive value. |
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