Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand.
Institute of Field Robotics, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
BMC Med Imaging. 2022 Mar 16;22(1):46. doi: 10.1186/s12880-022-00767-9.
Artificial intelligence, particularly the deep learning (DL) model, can provide reliable results for automated cardiothoracic ratio (CTR) measurement on chest X-ray (CXR) images. In everyday clinical use, however, this technology is usually implemented in a non-automated (AI-assisted) capacity because it still requires approval from radiologists. We investigated the performance and efficiency of our recently proposed models for the AI-assisted method intended for clinical practice.
We validated four proposed DL models (AlbuNet, SegNet, VGG-11, and VGG-16) to find the best model for clinical implementation using a dataset of 7517 CXR images from manual operations. These models were investigated in single-model and combined-model modes to find the model with the highest percentage of results where the user could accept the results without further interaction (excellent grade), and with measurement variation within ± 1.8% of the human-operating range. The best model from the validation study was then tested on an evaluation dataset of 9386 CXR images using the AI-assisted method with two radiologists to measure the yield of excellent grade results, observer variation, and operating time. A Bland-Altman plot with coefficient of variation (CV) was employed to evaluate agreement between measurements.
The VGG-16 gave the highest excellent grade result (68.9%) of any single-model mode with a CV comparable to manual operation (2.12% vs 2.13%). No DL model produced a failure-grade result. The combined-model mode of AlbuNet + VGG-11 model yielded excellent grades in 82.7% of images and a CV of 1.36%. Using the evaluation dataset, the AlbuNet + VGG-11 model produced excellent grade results in 77.8% of images, a CV of 1.55%, and reduced CTR measurement time by almost ten-fold (1.07 ± 2.62 s vs 10.6 ± 1.5 s) compared with manual operation.
Due to its excellent accuracy and speed, the AlbuNet + VGG-11 model could be clinically implemented to assist radiologists with CTR measurement.
人工智能，尤其是深度学习（DL）模型，可以为胸部 X 射线（CXR）图像的自动心胸比（CTR）测量提供可靠的结果。然而，在日常临床应用中，该技术通常以非自动化（AI 辅助）的方式实施，因为它仍然需要放射科医生的批准。我们研究了我们最近提出的用于临床实践的 AI 辅助方法的模型的性能和效率。
我们使用来自手动操作的 7517 张 CXR 图像的数据集验证了四个提出的 DL 模型（AlbuNet、SegNet、VGG-11 和 VGG-16），以找到最适合临床实施的模型。这些模型在单模型和组合模型模式下进行了研究，以找到用户可以接受结果而无需进一步交互（优秀等级）的结果的模型，并且测量值在人类操作范围内的±1.8%内变化。然后，在使用两名放射科医生的 AI 辅助方法的 9386 张 CXR 图像评估数据集上测试了验证研究中最好的模型，以测量优秀等级结果的产量、观察者变化和操作时间。使用 Bland-Altman 图和变异系数（CV）评估测量值之间的一致性。
VGG-16 在任何单模型模式下都获得了最高的优秀等级结果（68.9%），CV 与手动操作相当（2.12%对 2.13%）。没有 DL 模型产生失败等级的结果。AlbuNet+VGG-11 模型的组合模型模式在 82.7%的图像中产生了优秀等级的结果，CV 为 1.36%。在使用评估数据集时，AlbuNet+VGG-11 模型在 77.8%的图像中产生了优秀等级的结果，CV 为 1.55%，并且与手动操作相比，将 CTR 测量时间缩短了近十倍（1.07±2.62 秒对 10.6±1.5 秒）。
由于其出色的准确性和速度，AlbuNet+VGG-11 模型可临床实施以协助放射科医生进行 CTR 测量。
