Anatomically realistic aortic dissection simulator as a potential training tool for point-of-care ultrasound.

Aim: Simulators for aortic dissection diagnosis are limited by complex anatomy influencing the accuracy of point-of-care ultrasound for diagnosing aortic dissection. Therefore, this study aimed to create a healthy ascending aorta and class DeBakey, type II aortic dissection simulator as a potential point-of-care ultrasound training model. Material and methods: 3D mould simulators were created based on computed tomography images of one healthy and one DeBakey type II aortic dissection patient. In the next step, two polyvinyl alcohol-based and two silicone-based simulators were synthesised. Results: The results of the scanning electron microscope assessment showed an aortic dissection simulator's surface with disorganised surface texture and higher root mean square (RMS or Rq) value than the healthy model of polyvinyl alcohol (RqAD = 20.28 > RqAAo = 10.26) and silicone (RqAD = 33.8 > RqAAo = 23.07). The ultrasound assessment of diameter aortic dissection showed higher than the healthy ascending aorta in polyvinyl alcohol (dAD = 28.2 mm > dAAo = 20.2 mm) and Si (dAD = 31.0 mm > dAAo = 22.4 mm), while the wall thickness of aortic dissection showed thinner than the healthy aorta in polyvinyl alcohol, which is comparable with the actual aorta measurement. The intimal flap of aortic dissection was able to replicate and showed a false lumen in the ultrasound images. The flap was measured quantitatively, indicating that the intimal flap was hyperechoic. Conclusions: The simulators were able to replicate the surface morphology and echogenicity of the intimal flap, which is a linear hyperechoic area representing the separation of the aorta wall.

Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images.

Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning. To implement each pre-trained CNN architecture, we deployed the corresponded feature vector available from the TensorFlowHub, integrating it with dropout and dense layers to form a complete CNN model. Our findings indicated that the EfficientNetV2B0-21k (0.72B Floating-Point Operations and 7.1 M parameters) outperformed other CNN models in the IDC grading task. Nevertheless, we discovered that practically all selected CNN models perform well in the IDC grading task, with an average balanced accuracy of 0.936 ± 0.0189 on the cross-validation set and 0.9308 ± 0.0211on the test set.

Thermal distribution analysis of three-dimensional tumor-embedded breast models with different breast density compositions.

Breast cancer is the most common cancer among women globally, and the number of young women diagnosed with this disease is gradually increasing over the years. Mammography is the current gold-standard technique although it is known to be less sensitive in detecting tumors in woman with dense breast tissue. Detecting an early-stage tumor in young women is very crucial for better survival chance and treatment. The thermography technique has the capability to provide an additional functional information on physiological changes to mammography by describing thermal and vascular properties of the tissues. Studies on breast thermography have been carried out to improve the accuracy level of the thermography technique in various perspectives. However, the limitation of gathering women affected by cancer in different age groups had necessitated this comprehensive study which is aimed to investigate the effect of different density levels on the surface temperature distribution profile of the breast models. These models, namely extremely dense (ED), heterogeneously dense (HD), scattered fibroglandular (SF), and predominantly fatty (PF), with embedded tumors were developed using the finite element method. A conventional Pennes' bioheat model was used to perform the numerical simulation on different case studies, and the results obtained were then compared using a hypothesis statistical analysis method to the reference breast model developed previously. The results obtained show that ED, SF, and PF breast models had significant mean differences in surface temperature profile with a p value <0.025, while HD breast model data pair agreed with the null hypothesis formulated due to the comparable tissue composition percentage to the reference model. The findings suggested that various breast density levels should be considered as a contributing factor to the surface thermal distribution profile alteration in both breast cancer detection and analysis when using the thermography technique.