Background and Objectives: This study investigated whether an artificial intelligence computer-assisted diagnosis (AI-CAD) software recently developed in our institution named the Severance Artificial intelligence program (SERA) could show similar diagnostic performance for thyroid cancers using ultrasonographic (US) images from a mobile phone (SERA_M) compared to using images directly downloaded from the pictures archive and communication system (PACS) (SERA_P). Materials and Methods: From October 2019 to December 2019, 259 thyroid nodules from 259 patients were included. SERA was run on original and mobile images to evaluate SERA_P and SERA_M. Nodules were categorized according to the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS). To compare diagnostic performance, a logistic regression analysis was conducted using the Generalized Estimating Equation. The area under the curve (AUC) was calculated using the receiver operating characteristic (ROC) curve, and compared using the Delong Method. Results: There were 40 cancers (15.4%) and 219 benign lesions (84.6%). The AUC and sensitivity of SERA_M (0.82 and 85%, respectively) were not statistically different from SERA_P (0.8 and 75%, respectively) (p=0.526 and p=0.091, respectively). The AUC of radiologists (0.856) was not significantly different compared to SERA_P and SERA_M (p=0.163 and p=0.414, respectively). The sensitivity of radiologists (77.5%) was not statistically different compared to SERA_P and SERA_M (p=0.739 and p=0.361, respectively). Conclusion AI-CAD software using pictures taken by a mobile phone showed comparable diagnostic performance with the same software using images directly from PACS.

Purpose: To evaluate and analyze the adequacy of breast magnetic resonance imaging (MRI)s taken before publication of the 2018 recommendation in South Korea. Materials and Methods: We enrolled 87 cases of breast MRIs, from January 2010 to November 2013, taken at external hospitals in the study. Breast MRI protocol elements are divided into three categories based on the recommendation by the Breast Imaging Study Group of the Korean Society of Magnetic Resonance: (1) Essential elements for breast MRI protocol; (2) Element to consider when evaluating imaging quality; and (3) Optional element for breast MRI protocol. Also, we divided enrolled cases into three groups based on their conducting locations -- (1) Primary hospitals, (2) Secondary hospitals, and (3) Tertiary hospitals-and analyzed them for the adequacy of imaging protocols based on the 2018 recommendation. We used a Chi-square test and Fisher’s exact test to identify differences between categorical variables. Results: Over 98% of the criteria for 'essential elements for breast MRI protocol' were satisfied when compared with the 2018 Recommendation. Over 96% of the criteria for 'elements to consider when evaluating imaging quality' were also satisfied, except for the slice thickness (83.9%). Optional elements for breast MRI protocol were satisfied with various percentages. There were no statistically significant differences between groups of tertiary, secondary, and primary hospitals; however, 3 tesla of MRI (P = 0.04), subtraction image protocol (P = 0.032), and DWI protocol (P = 0.03) were used more frequently in the tertiary hospitals than in the others. Conclusion We found that the categories of 'essential elements' and 'elements to consider when evaluating imaging quality' were satisfied at 98% and 96%, respectively, when compared with the 2018 Recommendation by the Breast Imaging Study Group of the Korean Society of Magnetic Resonance.

Purpose: To evaluate and analyze the adequacy of breast magnetic resonance imaging (MRI)s taken before publication of the 2018 recommendation in South Korea. Materials and Methods: We enrolled 87 cases of breast MRIs, from January 2010 to November 2013, taken at external hospitals in the study. Breast MRI protocol elements are divided into three categories based on the recommendation by the Breast Imaging Study Group of the Korean Society of Magnetic Resonance: (1) Essential elements for breast MRI protocol; (2) Element to consider when evaluating imaging quality; and (3) Optional element for breast MRI protocol. Also, we divided enrolled cases into three groups based on their conducting locations -- (1) Primary hospitals, (2) Secondary hospitals, and (3) Tertiary hospitals-and analyzed them for the adequacy of imaging protocols based on the 2018 recommendation. We used a Chi-square test and Fisher’s exact test to identify differences between categorical variables. Results: Over 98% of the criteria for 'essential elements for breast MRI protocol' were satisfied when compared with the 2018 Recommendation. Over 96% of the criteria for 'elements to consider when evaluating imaging quality' were also satisfied, except for the slice thickness (83.9%). Optional elements for breast MRI protocol were satisfied with various percentages. There were no statistically significant differences between groups of tertiary, secondary, and primary hospitals; however, 3 tesla of MRI (P = 0.04), subtraction image protocol (P = 0.032), and DWI protocol (P = 0.03) were used more frequently in the tertiary hospitals than in the others. Conclusion We found that the categories of 'essential elements' and 'elements to consider when evaluating imaging quality' were satisfied at 98% and 96%, respectively, when compared with the 2018 Recommendation by the Breast Imaging Study Group of the Korean Society of Magnetic Resonance.

Purpose: This study evaluated how artificial intelligence-based computer-assisted diagnosis (AICAD) for breast ultrasonography (US) influences diagnostic performance and agreement between radiologists with varying experience levels in different workflows. Methods: Images of 492 breast lesions (200 malignant and 292 benign masses) in 472 women taken from April 2017 to June 2018 were included. Six radiologists (three inexperienced [<1 year of experience] and three experienced [10-15 years of experience]) individually reviewed US images with and without the aid of AI-CAD, first sequentially and then simultaneously. Diagnostic performance and interobserver agreement were calculated and compared between radiologists and AI-CAD. Results: After implementing AI-CAD, the specificity, positive predictive value (PPV), and accuracy significantly improved, regardless of experience and workflow (all P<0.001, respectively). The overall area under the receiver operating characteristic curve significantly increased in simultaneous reading, but only for inexperienced radiologists. The agreement for Breast Imaging Reporting and Database System (BI-RADS) descriptors generally increased when AI-CAD was used (κ=0.29-0.63 to 0.35-0.73). Inexperienced radiologists tended to concede to AI-CAD results more easily than experienced radiologists, especially in simultaneous reading (P<0.001). The conversion rates for final assessment changes from BI-RADS 2 or 3 to BI-RADS higher than 4a or vice versa were also significantly higher in simultaneous reading than sequential reading (overall, 15.8% and 6.2%, respectively; P<0.001) for both inexperienced and experienced radiologists. Conclusion Using AI-CAD to interpret breast US improved the specificity, PPV, and accuracy of radiologists regardless of experience level. AI-CAD may work better in simultaneous reading to improve diagnostic performance and agreement between radiologists, especially for inexperienced radiologists.