For accurate measurement of lung nodule volume, it is critical to select a section thickness and/or segmentation threshold appropriate for the size of a nodule.

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Background Artificial Intelligence (AI) is a promising tool for cardiothoracic ratio (CTR) measurement that has been technically validated but not clinically evaluated on a large dataset. We observed and validated AI and manual methods for CTR measurement using a large dataset and investigated the clinical utility of the AI method. Methods Five thousand normal chest x-rays and 2,517 images with cardiomegaly and CTR values, were analyzed using manual, AI-assisted, and AI-only methods. AI-only methods obtained CTR values from a VGG-16 U-Net model. An in-house software was used to aid the manual and AI-assisted measurements and to record operating time. Intra and inter-observer experiments were performed on manual and AI-assisted methods and the averages were used in a method variation study. AI outcomes were graded in the AI-assisted method as excellent (accepted by both users independently), good (required adjustment), and poor (failed outcome). Bland–Altman plot with coefficient of variation (CV), and coefficient of determination (R-squared) were used to evaluate agreement and correlation between measurements. Finally, the performance of a cardiomegaly classification test was evaluated using a CTR cutoff at the standard (0.5), optimum, and maximum sensitivity. Results Manual CTR measurements on cardiomegaly data were comparable to previous radiologist reports (CV of 2.13% vs 2.04%). The observer and method variations from the AI-only method were about three times higher than from the manual method (CV of 5.78% vs 2.13%). AI assistance resulted in 40% excellent, 56% good, and 4% poor grading. AI assistance significantly improved agreement on inter-observer measurement compared to manual methods (CV; bias: 1.72%; − 0.61% vs 2.13%; − 1.62%) and was faster to perform (2.2 ± 2.4 secs vs 10.6 ± 1.5 secs). The R-squared and classification-test were not reliable indicators to verify that the AI-only method could replace manual operation. Conclusions AI alone is not yet suitable to replace manual operations due to its high variation, but it is useful to assist the radiologist because it can reduce observer variation and operation time. Agreement of measurement should be used to compare AI and manual methods, rather than R-square or classification performance tests.

. Purpose: The outbreak of COVID-19 or coronavirus was first reported in 2019. It has widely and rapidly spread around the world. The detection of COVID-19 cases is one of the important factors to stop the epidemic, because the infected individuals must be quarantined. One reliable way to detect COVID-19 cases is using chest x-ray images, where signals of the infection are located in lung areas. We propose a solution to automatically classify COVID-19 cases in chest x-ray images. Approach: The ResNet-101 architecture is adopted as the main network with more than 44 millions parameters. The whole net is trained using the large size of x-ray images. The heatmap under the region of interest of segmented lung is constructed to visualize and emphasize signals of COVID-19 in each input x-ray image. Lungs are segmented using the pretrained U-Net. The confidence score of being COVID-19 is also calculated for each classification result. Results: The proposed solution is evaluated based on COVID-19 and normal cases. It is also tested on unseen classes to validate a regularization of the constructed model. They include other normal cases where chest x-ray images are normal without any disease but with some small remarks, and other abnormal cases where chest x-ray images are abnormal with some other diseases containing remarks similar to COVID-19. The proposed method can achieve the sensitivity, specificity, and accuracy of 97%, 98%, and 98%, respectively. Conclusions: It can be concluded that the proposed solution can detect COVID-19 in a chest x-ray image. The heatmap and confidence score of the detection are also demonstrated, such that users or human experts can use them for a final diagnosis in practical usages.

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MDCT angiography provides excellent correlation in vascular stenosis as compared with DSA in hemodialysis access. Complete assessment of entire vascular segments could be performing with MDCT angiography in planning before endovascular intervention or surgical correction.

The proximal part of the radius has a complex shape and dimension that cannot be precisely determined by standard roentgenogram for real three-dimensional anatomical shape which is important for prosthesis design. This study presents a method by which computer tomography (CT) images are combined with the reverse engineering technique to obtain and analyse the three-dimensional inner and outer geometry of the proximal radius. The three-dimensional models were reconstructed from CT images obtained from 40 radial bones and approximated with two- and three-dimensional fitting algorithms based on reverse engineering methods. The mean total length of the radius was 240.0 mm [standard deviation (SD) = 17.3]. The radial head in this study is more likely to be circular with an average diameter of 20.5 mm (SD = 1.9). The outer diameter of the radial neck averages 14.7 mm (SD = 1.0). The thickness of the radial head averages 12.9 mm (SD = 1.4). The intramedullary canal diameter of the radial neck averages 7.4 mm (SD = 1.4). The depth of the fossa at the articular surface averages 1.5 mm (SD = 0.4).

Background Artificial Intelligence (AI) technique for cardiothoracic ratio (CTR) measurement is a promising tool that has been technically validated but not clinically evaluated on a large dataset. This study observes and validates AI and manual methods for CTR measurement on a large dataset and investigates the clinical utility of the AI method. Results Five thousand normal chest x-rays and 2,517 images with cardiomegaly and CTR values, were analyzed using manual, AI-assisted, and AI only methods. AI methods obtained CTR values from a VGG-16 U-Net model. An in-house software was used to aid the study and to record measurement time. Intra and inter-observer experiments were performed on manual and AI-assisted methods and the average of each method was employed in a method variation study. AI outcomes were graded in the AI-assisted method as excellent (accepted by both users independently), good (required adjustment), and poor (failed outcome). Bland-Altman plot with coefficient of variation (CV), and coefficient of determination (R-squared) were employed to evaluate agreement and correlation between measurements. Finally, the performance of a cardiomegaly classification test was evaluated using a CTR cutoff at the standard (0.5), optimum, and maximum sensitivity. Manual CTR measurements on cardiomegaly data were comparable to the previous radiologist reports (CV of 2.13% vs 2.04%). The observer and method variations from the AI method were about three times higher than from the manual method (CV of 5.78% vs 2.13%). AI assistance resulted in 40% excellent, 56% good, and 4% poor grading. AI assistance significantly improved agreement on inter-observer measurement compared to manual methods (CV; bias: 1.72%; -0.61% vs 2.13%; -1.62%) and was faster to perform (2.2 ± 2.4 secs vs 10.6 ± 1.5 secs). R-squared and classification-test were not reliable indicators to verify that the AI method could replace manual operation. Conclusion AI alone is not suitable to replace manual operation due to its high variation, but it is useful to assist the radiologist because it can reduce observer variation and operation time. Agreement of measurement should be used to compare AI and manual methods, rather than R-square or classification performance tests.