Department of Orthopedics, Jintan Hospital Affiliated to Jiangsu University, Changzhou, China.
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
J Int Med Res. 2024 Sep;52(9):3000605241274576. doi: 10.1177/03000605241274576.
We developed a few-shot learning (FSL) framework for the diagnosis of osteopenia and osteoporosis in knee X-ray images.
Computer vision models containing deep convolutional neural networks were fine-tuned to enable generalization from natural images (ImageNet) to chest X-ray images (normal vs. pneumonia, base images). Then, a series of automated machine learning classifiers based on the Euclidean distances of base images were developed to make predictions for novel images (normal vs. osteopenia vs. osteoporosis). The performance of the FSL framework was compared with that of junior and senior radiologists. In addition, the gradient-weighted class activation mapping algorithm was used for visual interpretation.
In Cohort #1, the mean accuracy (0.728) and sensitivity (0.774) of the FSL models were higher than those of the radiologists (0.512 and 0.448). A diagnostic pipeline of FSL model (first)-radiologists (second) achieved better performance (0.653 accuracy, 0.582 sensitivity, and 0.816 specificity) than radiologists alone. In Cohort #2, the diagnostic pipeline also showed improved performance.
The FSL framework yielded practical performance with respect to the diagnosis of osteopenia and osteoporosis in comparison with radiologists. This retrospective study supports the use of promising FSL methods in computer-aided diagnosis tasks involving limited samples.
我们开发了一种用于膝关节 X 射线图像中骨质疏松症和骨量减少诊断的少样本学习（FSL）框架。
对包含深度卷积神经网络的计算机视觉模型进行微调，以实现从自然图像（ImageNet）到胸部 X 射线图像（正常与肺炎、基础图像）的泛化。然后，开发了一系列基于基础图像欧几里得距离的自动化机器学习分类器，用于对新图像（正常与骨量减少与骨质疏松症）进行预测。将 FSL 框架的性能与初级和高级放射科医生进行了比较。此外，还使用梯度加权类激活映射算法进行了可视化解释。
在队列 #1 中，FSL 模型的平均准确率（0.728）和敏感度（0.774）均高于放射科医生（0.512 和 0.448）。FSL 模型（第一）-放射科医生（第二）的诊断流水线的表现优于单独的放射科医生（0.653 的准确率、0.582 的敏感度和 0.816 的特异性）。在队列 #2 中，该诊断流水线也表现出了改进的性能。
与放射科医生相比，FSL 框架在骨质疏松症和骨量减少的诊断方面具有实用的性能。这项回顾性研究支持在涉及有限样本的计算机辅助诊断任务中使用有前途的 FSL 方法。
