Study on the characteristics of coronary calcification by Intravascular Ultrasound (IVUS)

Background: Percutaneous coronary angiography is considered the "gold standard" for the diagnosis of coronary artery disease and provides the necessary anatomical information to provide appropriate treatment. The limitation of coronary angiography is the accurate assessment of calcified coronary lesions. Intravascular ultrasonography has many advantages in the assessment of calcified coronary lesions. Methods: The descriptive clinical study. Evaluation of calcified coronary artery lesions by intravascular ultrasound Results: From January 2019 to December 2019, at the Hanoi Heart Institute, 64 patients had 64 coronary artery lesions surveyed by intravascular ultrasound. There were 42 (65,6%) calcified lesions assessed by IVUS and 25 (39,1%) calcified lesions were detected by coronary angiography. In addition, the location of calcified were revealed more in the LAD compared to other: LAD 60%, LCx 24%, RCA 12% and LM 4%. Conclusion: IVUS calcification detection rate is higher than coronary angiography. The most common site of calcification in the LAD.

Impact of Different Evaluation Methods for Diagnostic Tasks in Panoramic Radiography

The aim of this study was to evaluate the observers’ diagnostic performance in panoramic radiography using monitor, tablet, X-ray image view box, and against window daylight as a visualization method in different diagnostic tasks. Thirty panoramic radiography were assessed by three calibrated observers for each visualization method, in standardized light conditions, concerning dental caries, widened periodontal ligament space, and periapical bone defects from the four first molars; mucosal thickening and retention cysts in maxillary sinus; and stylo-hyoid ligament calcification and atheroma. A five-point confidence scale was used. The standard-reference was performed by two experienced observers. Diagnostic values using window light were significantly lower for caries and periapical bone defect and retention cyst, stylo-hyoid ligament calcification detection (p<0.05). For atheroma detection, X-ray image view box, tablet, and widow light had lower accuracy than the evaluation on the monitor (p<0.05). Observer’s diagnostic performances are worsened using window light as an evaluation method for panoramic radiography for dental, sinus, and calcification disorders, while the monitor was the most reliable method.

Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image

Coronary artery calcification is a calcium buildup within the walls of the arteries. It is considered a predominant marker for coronary artery disease. Thus many approaches have been developed for the automatic detection of calcification. The previous calcification detection was on segmentation of other structures as pre-processing steps or using the fact that the calcification often appears as a bright region. In this paper, an automated system proposed using a deep learning approach to detect the calcification absence and calcification presence in coronary artery IVUS image. A useful advantage of deep learning, compared to other methods is, it uses representations and features directly from the raw data, bypassing the need to manually extract features, a common that required in the traditional machine learning framework. The type of deep learning architecture used is 27 layers of convolutional neural networks (CNNs) using Direct Acyclic Graph. The proposed system used 2175 images and achieved an accuracy of 98.16% for Cartesian coordinate images and 99.08% for Polar Reconstructed Coordinate images.

Mitral Valve Abnormality Detection by fully end to end with Deep Neural Network

The purpose of this research is to automatically identify normal and abnormal mitral leaflets in an apical four-chamber view.one of the widely spread valvular diseases is mitral valve disease in underdeveloped countries, still a burden for health sociality as well as countries. around 80 percent of valvular diseases are mitral valve disease problems. As far as World Heart Foundation Guidelines are concerned, It is totally based on mitral leaflets morphology. Due to the dependency on the sonographer's experience, it is highly subjective for argument. Measurement of thickness of leaflets, calcification detection, the pliability of leaflets required high experience about echocardiography as well as morphology. The motive of this research is to automatically identify the normal or abnormal mitral valve. If there is an abnormality in mitral leaflets then further investigation needed otherwise there is no further investigation that means measuring thickness, mitral valve area should not be required to measure. This research consists of two parts first automatically localize the region of interest second classifies the mitral leaflets whether normal or abnormal for localization yolo3 model mechanism with custom backend instead of darknet is used for taking area of interest automatically and for classification of normal and abnormal mitral leaflets, proposed pipeline is used, having f1, mAp score, and other matrices have measured.PR and ROC curves are drowned to support the results in the evaluation. the motive of this research is to serve nonexpert to identify abnormalities in mitral leaflets and sonographers to assess more efficiently. We used the Apical four-chamber view for this research.