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Background: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. Methods: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A–D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. Results: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7–77.3] vs. 67.1% [95% CI, 58.8–74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9–93.2] vs. 77.6% [95% CI, 61.7–77.9]), PPV (1.5% [95% CI, 1.2–1.9] vs. 0.5% [95% CI, 0.4–0.6]), recall rate (7.1% [95% CI, 6.9–7.2] vs. 22.5% [95% CI, 22.2–22.7]), and AUC values (0.8 [95% CI, 0.76–0.84] vs. 0.74 [95% CI, 0.7–0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. Conclusions: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue. [ABSTRACT FROM AUTHOR]

Aims Mammography, commonly used for breast cancer screening in women, can also predict cardiovascular disease. We developed mammography-based deep learning models for predicting coronary artery calcium (CAC) scores, an established predictor of coronary events. Methods and results We evaluated a subset of Korean adults who underwent image mammography and CAC computed tomography and randomly selected approximately 80% of the participants as the training dataset, used to develop a convolutional neural network (CNN) to predict detectable CAC. The sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), and overall accuracy of the model's performance were evaluated. The training and validation datasets included 5235 and 1208 women, respectively [mean age, 52.6 (±10.2) years], including non-zero cases (46.8%). The CNN-based deep learning prediction model based on the Resnet18 model showed the best performance. The model was further improved using contrastive learning strategies based on positive and negative samples: sensitivity, 0.764 (95% CI, 0.667–0.830); specificity, 0.652 (95% CI, 0.614–0.710); AUROC, 0.761 (95% CI, 0.742–0.780); and accuracy, 70.8% (95% CI, 68.8–72.4). Moreover, including age and menopausal status in the model further improved its performance (AUROC, 0.776; 95% CI, 0.762–0.790). The Framingham risk score yielded an AUROC of 0.736 (95% CI, 0.712–0.761). Conclusion Mammography-based deep learning models showed promising results for predicting CAC, performing comparably to conventional risk models. This indicates mammography's potential for dual-risk assessment in breast cancer and cardiovascular disease. Further research is necessary to validate these findings in diverse populations, with a particular focus on representation from national breast screening programmes. [ABSTRACT FROM AUTHOR]

In this study, we introduce two ortho‐carboranyl compounds, namely ICzCB and mTPCB, which exhibit different planarity in their appended π‐conjugated aromatic groups, to investigate the influence of geometric effects on the regulation of the radiative efficiency. Both the compounds are fully characterized, revealing distinct geometric differences (planarity) in their single‐crystal structures. While ICzCB presents strong ICT‐based emission in the solid (film) state, mTPCB displays dual emission in the high‐ and low‐energy regions that originates from locally excited (LE)‐ and ICT‐based processes, respectively. Quantitative analysis of the photophysical characteristics of the compounds shows that the ICT‐based radiative efficiency in ICzCB is higher than that in mTPCB. Consequently, these findings demonstrate that the planarity of the π‐conjugated aryl group appended on o‐carborane, which is represented by planar ICzCB and distorted mTPCB, is an important key to control the ICT‐based radiative decay efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a control method for improving the dynamic characteristic of battery chargers for personal mobility devices (PMDs) using sliding mode control (SMC). Various types of PMDs have batteries with distinct voltages. Therefore, research on battery chargers for PMDs that can be applied to various types of PMD with distinct voltages has been actively pursued. In battery chargers for PMDs, the output voltage can usually be controlled by a proportional-integral (PI) controller. However, increasing the PI controller gain is necessary to enhance the dynamic characteristic of the output voltage. It results in an overshoot that adversely affects the stability of the battery charger. Therefore, in this paper, a control method using sliding mode control (SMC) is presented for improving the dynamic characteristic without overshoot in the output voltage of battery chargers for PMDs. The validity of the proposed control method is verified by the simulation and experimental results. [ABSTRACT FROM AUTHOR]

Aims: Mammography, commonly used for breast cancer screening in women, can also predict cardiovascular disease. We developed mammography-based deep learning models for predicting coronary artery calcium (CAC) scores, an established predictor of coronary events., Methods and Results: We evaluated a subset of Korean adults who underwent image mammography and CAC computed tomography and randomly selected approximately 80% of the participants as the training dataset, used to develop a convolutional neural network (CNN) to predict detectable CAC. The sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), and overall accuracy of the model's performance were evaluated. The training and validation datasets included 5235 and 1208 women, respectively [mean age, 52.6 (±10.2) years], including non-zero cases (46.8%). The CNN-based deep learning prediction model based on the Resnet18 model showed the best performance. The model was further improved using contrastive learning strategies based on positive and negative samples: sensitivity, 0.764 (95% CI, 0.667-0.830); specificity, 0.652 (95% CI, 0.614-0.710); AUROC, 0.761 (95% CI, 0.742-0.780); and accuracy, 70.8% (95% CI, 68.8-72.4). Moreover, including age and menopausal status in the model further improved its performance (AUROC, 0.776; 95% CI, 0.762-0.790). The Framingham risk score yielded an AUROC of 0.736 (95% CI, 0.712-0.761)., Conclusion: Mammography-based deep learning models showed promising results for predicting CAC, performing comparably to conventional risk models. This indicates mammography's potential for dual-risk assessment in breast cancer and cardiovascular disease. Further research is necessary to validate these findings in diverse populations, with a particular focus on representation from national breast screening programmes., Competing Interests: Conflict of interest: All authors report no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)