Читать книгу Predicting Heart Failure - Группа авторов - Страница 78

CT scans are mainly used to view parts of the body in detail. In the case of a cardiac CT scan, it displays the heart and the blood vessels clearly which helps experts to diagnose or detect any abnormality. CT scans can detect the early signs of heart disease by scanning the heart’s arteries for any calcified plaque formation to create a coronary artery calcium (CAC) score, which has been proven to be a strong predictor for CVDs (Figure 2.9).

Figure 2.9 Plaque formation in the heart arteries.

The data captured by a CT scan generates a 3D model of a patient’s heart. Cardiac segmentation in chest CT images facilitates partitioning the entire chest CT image into numbers of anatomically significant regions that focus on the four chambers of the heart. The manual process of segmentation is becoming replaced by computer-aided techniques such as graph-based segmenting, mean-thresholding, fuzzy clustering methods, and the latest deep learning approach, which has shown promising results. The deep learning system can be trained to identify and quantify coronary artery calcium. This makes an effective early detection device for coronary calcification from atherosclerosis as it detects calcification before symptoms develop. This forms a powerful predictor of future heart problems.

One of the works proposed in the literature regarding CAC was about developing an algorithm that excludes CT scans with negative CAC scores and segments CAC in cases where positive scores are found. The segmented region can further proceed to radiologists for accurate detection. The proposed method highly reduces the workload of radiologists [32]. The proposed method utilized an integrated architecture model which consists of two CNNs where one is for classification and the other for segmentation. The model was able to exclude 86% of the negative cases and segmentation achieved a Dice coefficient of 0.63 and 0.84, for internal and external validation, respectively.

Another research group developed a deep learning model for CAC classification and segmentation [33]. The group focused on calcium quantification of chest CT scans and compared it to manual evaluation. The model was a combination of CNNs along with a ResNet architecture for image feature extraction, as well as an FCN for spatial coordinate features. Their method displayed a high correlation with the manual evaluation for detecting the presence of calcification, with results of 91% sensitivity and 92% specificity. In the case of calcium volume determination, the AI model was able to produce an excellent correlation with the manual determination with no significant difference found. The method proved to be fully automated, reducing the time for evaluation while optimizing clinical calcium scoring.

The work by Zhang et al. [34] utilized deep learning for calcium quantification from CT scan images. They developed a fully automated framework to detect CAC, using 3D CT scans as input for the model and extracting features to apply segmentation and calcification. The obtained results of the proposed method have no significant difference compared to the manual method.

Dekker et al. [35], implemented the deep learning technique to calculate the CAC score during myocardial perfusion imaging (MPI) assessment. They defined a threshold for CAC scores where it was low (<400) and high (≥400). The deep learning model used in the proposed method was previously validated in other approaches. The outcomes of the research work highlighted that high CAC scores presented higher accumulative event rates. They also proved that their method could progress risk stratification leading to customized treatments for patients.

In conclusion, AI methods showed better automation and higher accuracy in diagnosis in some applications. Some of the outcomes are even open to further enhancement which could lead to higher performance. This could help to reduce the effort of physicians and radiologists in diagnosing heart disease in the future. AI based methods can’t replace clinical methods such as ECG, EEG, or CT scans; however, they can automate the decision-making task.