Deep-Learning-Based Coronary Artery Calcium Detection from CT Image

Lee, Sung‐Jin; Jou, Sung-Shick; Gil, Hyo‐Wook; Jia, Xibin; Lee, Ah Young; Hong, Min

doi:10.3390/s21217059

Cited by 18 publications

(6 citation statements)

References 17 publications

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“…Transfer learning allows for pretraining on a very large dataset of images and then tuning the resulting model using specific samples, which is useful for classification to train a stable, unbiased, and non-overfitting deep learning architecture from the very beginning [ 15 ]. Inception and ResNet models are the most frequently used for medical studies, such as the Inception V3 model, which performed well in pathological classification of NSCLC [ 16 ], breast cancer [ 17 ], and knee injury for MRI images [ 18 ] and the ResNet50 model, which is widely applied in diagnosis o9f brain diseases [ 19 ], classification of skin lesions [ 20 ], and coronary artery calcium detection [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules

Wang

Zhou

et al. 2022

Med Sci Monit

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Background In this study we aimed to establish a new transfer learning model based on noncontrast and thin-layer computed tomography (CT) scans to distinguish between malignant and benign solid lung nodules. Material/Methods CT images from 202 patients with 210 lesions (malignant: 127, benign: 83) manifesting as solid lung nodules from January 2016 to December 2020 from 3 institutions were retrospectively collected, and each nodule was histopathologically confirmed. Two experienced thoracic radiologists reviewed all images and determined the regions of interest (ROIs) in the three-dimensional (3D) images layer-by-layer. We divided the lesions and images into training and testing sets at a ratio of 7: 3. The Inception V3 model was pretrained by the training dataset. Five-fold cross-validation was used to choose the optimal model. Receiver operator characteristic curves (ROC curves) for methods to evaluate the performance of the models were drafted. Results In the validation set, the AUC, accuracy, sensitivity, and specificity of Inception V3 model (lesion-level) were 0.999, 0.989, 0.983, and 1.0, respectively, which is higher than the image-level (0.997, 0.933, 0.922, and 0.948, respectively). The Inception V3 model (lesion-level) performed better than the image-level but there was no significant difference between the models ( P >0.05). The ResNet50 model based on image level achieved AUC, accuracy, sensitivity, and specificity of 0.963, 0.926, 0.916, and 0.944, respectively, which is lower than that of Inception V3. Conclusions Our study developed a novel deep learning model based on noncontrast and thin-layer CT scans to classify benign vs malignant lung nodules, and the Inception V3 model greatly improved the differentiation accuracy and specificity.

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Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules

Wang

Zhou

et al. 2022

Med Sci Monit

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“…For example, Carbaja-Degante et al focused on ventricle segmentation on cardiac CT and MRI [ 36 ]. Additionally, many studies have tried to calculate the calcium scoring of coronary arteries based on cardiac CTs [ 37 , 38 ].…”

Section: Results and Synthesismentioning

confidence: 99%

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

et al. 2022

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Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have been increasingly used for image processing, specifically in the medical domain. Cardiothoracic imaging is one of the early adopters of ML/DL research, and the COVID-19 pandemic resulted in more research focus on the feasibility and applications of ML/DL in cardiothoracic imaging. In this scoping review, we systematically searched available peer-reviewed medical literature on cardiothoracic imaging and quantitatively extracted key data elements in order to get a big picture of how ML/DL have been used in the rapidly evolving cardiothoracic imaging field. During this report, we provide insights on different applications of ML/DL and some nuances pertaining to this specific field of research. Finally, we provide general suggestions on how researchers can make their research more than just a proof-of-concept and move toward clinical adoption.

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“…It utilizes the coronary ostia and cardia chambers for centerline extraction. Figure 2 Equation (1) shows the vessel extraction process.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…CAD is a form of cardiovascular disease that increases global mortality [1]. Hardening and narrowing coronary arteries reduces blood flow to the heart chambers [2,3].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images

Sait,

Awad

2024

Applied Sciences

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Coronary artery disease (CAD) is the most prevalent form of cardiovascular disease that may result in myocardial infarction. Annually, it leads to millions of fatalities and causes billions of dollars in global economic losses. Limited resources and complexities in interpreting results pose challenges to healthcare centers in implementing deep learning (DL)-based CAD detection models. Ensemble learning (EL) allows developers to build an effective CAD detection model by integrating the outcomes of multiple medical imaging models. In this study, the authors build an EL-based CAD detection model to identify CAD from coronary computer tomography angiography (CCTA) images. They employ a feature engineering technique, including MobileNet V3, CatBoost, and LightGBM models. A random forest (RF) classifier is used to ensemble the outcomes of the CatBoost and LightGBM models. The authors generalize the model using two benchmark datasets. The proposed model achieved an accuracy of 99.7 % and 99.6 % with limited computational resources. The generalization results highlight the importance of the proposed model’s efficiency in identifying CAD from the CCTA images. Healthcare centers and cardiologists can benefit from the proposed model to identify CAD in the initial stages. The proposed feature engineering can be extended using a liquid neural network model to reduce computational resources.

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Deep-Learning-Based Coronary Artery Calcium Detection from CT Image

Cited by 18 publications

References 17 publications

A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules

A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images

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