Magboo Vincent Peter C, Magboo Ma Sheila A
Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila.
Acta Med Philipp. 2024 May 15;58(8):67-75. doi: 10.47895/amp.vi0.7582. eCollection 2024.
Worldwide, coronary artery disease (CAD) is a leading cause of mortality and morbidity and remains to be a top health priority in many countries. A non-invasive imaging modality for diagnosis of CAD such as single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI) is usually requested by cardiologists as it displays radiotracer distribution in the heart reflecting myocardial perfusion. The interpretation of SPECT-MPI is done visually by a nuclear medicine physician and is largely dependent on his clinical experience and showing significant inter-observer variability.
The aim of the study is to apply a deep learning approach in the classification of SPECT-MPI for perfusion abnormalities using convolutional neural networks (CNN).
A publicly available anonymized SPECT-MPI from a machine learning repository (https://www.kaggle.com/selcankaplan/spect-mpi) was used in this study involving 192 patients who underwent stress-test-rest Tc99m MPI. An exploratory approach of CNN hyperparameter selection to search for optimum neural network model was utilized with particular focus on various dropouts (0.2, 0.5, 0.7), batch sizes (8, 16, 32, 64), and number of dense nodes (32, 64, 128, 256). The base CNN model was also compared with the commonly used pre-trained CNNs in medical images such as VGG16, InceptionV3, DenseNet121 and ResNet50. All simulations experiments were performed in Kaggle using TensorFlow 2.6.0., Keras 2.6.0, and Python language 3.7.10.
The best performing base CNN model with parameters consisting of 0.7 dropout, batch size 8, and 32 dense nodes generated the highest normalized Matthews Correlation Coefficient at 0.909 and obtained 93.75% accuracy, 96.00% sensitivity, 96.00% precision, and 96.00% F1-score. It also obtained higher classification performance as compared to the pre-trained architectures.
The results suggest that deep learning approaches through the use of CNN models can be deployed by nuclear medicine physicians in their clinical practice to further augment their decision skills in the interpretation of SPECT-MPI tests. These CNN models can also be used as a dependable and valid second opinion that can aid physicians as a decision-support tool as well as serve as teaching or learning materials for the less-experienced physicians particularly those still in their training career. These highlights the clinical utility of deep learning approaches through CNN models in the practice of nuclear cardiology.
在全球范围内，冠状动脉疾病（CAD）是导致死亡和发病的主要原因，在许多国家仍然是首要的健康问题。心脏病专家通常会要求使用单光子发射计算机断层扫描 - 心肌灌注成像（SPECT - MPI）等非侵入性成像方式来诊断CAD，因为它可以显示心脏中放射性示踪剂的分布，反映心肌灌注情况。SPECT - MPI的解读由核医学医师通过视觉完成，很大程度上依赖于其临床经验，并且观察者之间存在显著差异。
本研究的目的是应用深度学习方法，使用卷积神经网络（CNN）对SPECT - MPI的灌注异常进行分类。
本研究使用了来自机器学习存储库（https://www.kaggle.com/selcankaplan/spect - mpi）的公开可用匿名SPECT - MPI数据，涉及192例接受负荷 - 静息Tc99m MPI检查的患者。采用探索性方法进行CNN超参数选择，以寻找最佳神经网络模型，特别关注各种辍学率（0.2、0.5、0.7）、批量大小（8、16、32、64）和密集节点数量（32、64、128、256）。还将基础CNN模型与医学图像中常用的预训练CNN（如VGG16、InceptionV3、DenseNet121和ResNet50）进行了比较。所有模拟实验均在Kaggle上使用TensorFlow 2.6.0、Keras 2.6.0和Python语言3.7.10进行。
表现最佳的基础CNN模型，其参数包括辍学率0.7、批量大小8和32个密集节点，产生了最高的归一化马修斯相关系数，为0.909，准确率为93.75%，灵敏度为96.00%，精确率为96.00%，F1分数为96.00%。与预训练架构相比，它还获得了更高的分类性能。
结果表明，核医学医师在临床实践中可以采用通过使用CNN模型的深度学习方法，以进一步提高他们在解读SPECT - MPI检查时的决策能力。这些CNN模型还可以作为可靠且有效的第二意见，作为决策支持工具帮助医生，也可以作为经验较少的医生（特别是仍处于培训阶段的医生）的教学或学习材料。这突出了通过CNN模型的深度学习方法在核心脏病学实践中的临床实用性。
