Important Features Associated with Depression Prediction and Explainable AI

Magboo, Vincent Peter C.; Magboo, Ma. Sheila A.

doi:10.1007/978-3-031-14832-3_2

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…The use of CNN models enhances the SPECT-MPI diagnostic process leading to institution of the much-needed medical therapy to upgrade overall survival rates and/or quality of life of patients with CAD. 13,[15][16][17][18] The visual assessment of myocardial perfusion abnormalities in SPECT-MPI remains an important research challenge to both cardiologists and data scientists since many research studies in clinical medicine now involve applying a variety of machine learning algorithms, deep learning methods, and statistical techniques to improve CAD detection. [19][20][21][22][23] In previous studies involving deep learning methods applied to nuclear medicine imaging, various types of CNN models were applied on bone scans of breast and prostate cancer patients to detect osseous metastasis.…”

Section: Introductionmentioning

confidence: 99%

SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach

2023

Acta Med Philipp

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Background 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. Objective 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). Methods 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. Results 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. Conclusions 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.

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