Smail Lauren C, Dhindsa Kiret, Braga Luis H, Becker Suzanna, Sonnadara Ranil R
Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada.
Office of Education Science, McMaster University, Hamilton, ON, Canada.
Front Pediatr. 2020 Jan 29;8:1. doi: 10.3389/fped.2020.00001. eCollection 2020.
Grading hydronephrosis severity relies on subjective interpretation of renal ultrasound images. Deep learning is a data-driven algorithmic approach to classifying data, including images, presenting a promising option for grading hydronephrosis. The current study explored the potential of deep convolutional neural networks (CNN), a type of deep learning algorithm, to grade hydronephrosis ultrasound images according to the 5-point Society for Fetal Urology (SFU) classification system, and discusses its potential applications in developing decision and teaching aids for clinical practice. We developed a five-layer CNN to grade 2,420 sagittal hydronephrosis ultrasound images [191 SFU 0 (8%), 407 SFU I (17%), 666 SFU II (28%), 833 SFU III (34%), and 323 SFU IV (13%)], from 673 patients ranging from 0 to 116.29 months old ( = 16.53, = 17.80). Five-way (all grades) and two-way classification problems [i.e., II vs. III, and low (0-II) vs. high (III-IV)] were explored. The CNN classified 94% (95% CI, 93-95%) of the images correctly or within one grade of the provided label in the five-way classification problem. Fifty-one percent of these images (95% CI, 49-53%) were correctly predicted, with an average weighted F1 score of 0.49 (95% CI, 0.47-0.51). The CNN achieved an average accuracy of 78% (95% CI, 75-82%) with an average weighted F1 of 0.78 (95% CI, 0.74-0.82) when classifying low vs. high grades, and an average accuracy of 71% (95% CI, 68-74%) with an average weighted F1 score of 0.71 (95% CI, 0.68-0.75) when discriminating between grades II vs. III. Our model performs well above chance level, and classifies almost all images either correctly or within one grade of the provided label. We have demonstrated the applicability of a CNN approach to hydronephrosis ultrasound image classification. Further investigation into a deep learning-based clinical adjunct for hydronephrosis is warranted.
肾积水严重程度的分级依赖于对肾脏超声图像的主观解读。深度学习是一种数据驱动的算法方法，用于对包括图像在内的数据进行分类，为肾积水分级提供了一个有前景的选择。本研究探讨了深度学习算法中的深度卷积神经网络（CNN）根据胎儿泌尿外科学会（SFU）的5分制分类系统对肾积水超声图像进行分级的潜力，并讨论了其在开发临床实践决策辅助工具和教学辅助工具方面的潜在应用。我们开发了一个五层的CNN，用于对来自673名年龄在0至116.29个月（平均年龄16.53岁，标准差17.80）患者的2420张矢状位肾积水超声图像进行分级[191张SFU 0级（8%），407张SFU I级（17%），666张SFU II级（28%），833张SFU III级（34%），以及323张SFU IV级（13%）]。研究探讨了五路（所有级别）和两路分类问题[即II级与III级，以及低级别（0-II级）与高级别（III-IV级）]。在五路分类问题中，CNN对94%（95%置信区间，93-95%）的图像分类正确或与提供的标签相差不超过一个级别。其中51%的图像（95%置信区间，49-53%）被正确预测，平均加权F1分数为0.49（95%置信区间，0.47-0.51）。在区分低级别与高级别时，CNN的平均准确率为78%（95%置信区间，75-82%），平均加权F1分数为0.78（95%置信区间，0.74-0.82）；在区分II级与III级时，平均准确率为71%（95%置信区间，68-74%），平均加权F1分数为0.71（95%置信区间，0.68-0.75）。我们的模型表现远高于随机水平，并且几乎将所有图像都正确分类或与提供的标签相差不超过一个级别。我们已经证明了CNN方法在肾积水超声图像分类中的适用性。有必要对基于深度学习的肾积水临床辅助工具进行进一步研究。
