Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

The Korean Association of Urogenital Tract Infection and Inflammation and the Korea Disease Control and Prevention Agency regularly update, revise, and develop new content for the Korean sexually transmitted infection (STI) guidelines. These professional bodies respond to changing epidemiological trends and evolving scientific evidence, and consider advances in laboratory diagnostics and research. The principal recommendations of the 2023 Korean STI guidelines in terms of viral infection follow: 1) If genital herpes recurs more than 4–6 times annually, suppressive therapy with acyclovir 400 mg orally 2 times/day or famciclovir 250 mg orally 2 times/day or valacyclovir 500 mg orally once a day (for patients with <10 episodes/year) or valacyclovir 1 g orally once daily (for patients with ≥10 episodes/year) is recommended to prevent recurrence; 2) molecular human papillomavirus (HPV) testing is not recommended as a routine test for STI status, nor for determination of HPV vaccination status; and 3) patients should inform their current sexual partners about anogenital warts because the types of HPV that cause such warts can be passed to partners. These guidelines will be updated every 5 years and will be revised when new knowledge on STIs becomes available and there is a reasonable need to improve the guidelines. Physicians and other healthcare providers can use the guidelines to assist in the prevention and treatment of STIs.

Cardiac organoids have emerged as invaluable tools for assessing the impact of diverse substances on heart function.This report introduces guidelines for general requirements for manufacturing cardiac organoids and conducting cardiac organoid-based assays, encompassing protocols, analytical methodologies, and ethical considerations. In the quest to employ recently developed three-dimensional cardiac organoid models as substitutes for animal testing, it becomes imperative to establish robust criteria for evaluating organoid quality and conducting toxicity assessments. This guideline addresses this need, catering to regulatory requirements, and describes common standards for organoid quality and toxicity assessment methodologies, commensurate with current technological capabilities. While acknowledging the dynamic nature of technological progress and the potential for future comparative studies, this guideline serves as a foundational framework. It offers a comprehensive approach to standardized cardiac organoid testing, ensuring scientific rigor, reproducibility, and ethical integrity in investigations of cardiotoxicity, particularly through the utilization of human pluripotent stem cell-derived cardiac organoids.

Background: Dental hygienists have a significant risk of infection due to occupational injuries caused by needles and sharpinstruments. This study aimed to evaluate the current status of needle and “sharp-instrument injuries” among dental hygienists and to propose improved preventive guidelines. Methods: A total of 251 dental hygienists completed an online survey between August 1, 2023 and September 2, 2023. Data from245 respondents were analyzed using IBM SPSS version 20, using independent t-tests and one-way analysis of variance to assess the frequency of injuries and their correlation with job characteristics. Results: Among the 251 dental hygienists, 77.6% had experienced needle or sharp-instrument injuries, with an average of 4.97incidents per person. Infection prevention education significantly reduced the number of injuries, and participants with education exhibited better infection control practices than those without. Most injuries occurred during “instrument cleaning or maintenance” and “anesthesia preparation or disposal,” with “scalers, probes, and curettes” being the main culprits. Hands were the most frequently injured body parts. Conclusion Preventive measures, continuous education, and improved guidelines are required to create a safer dental workingenvironment.

Background and Objectives: Concerns remain that early aspirin cessation may be associated with potential harm in subsets at high risk of ischemic events. This study aimed to assess the effects of P2Y12 inhibitor monotherapy after 3-month dual antiplatelet therapy (DAPT) vs.prolonged DAPT (12-month or longer) based on the ischemic risk stratification, the CHADSP2A2RC, after percutaneous coronary intervention (PCI). Methods: This was a sub-study of the SMART-CHOICE trial. The effect of the randomized antiplatelet strategies was assessed across 3 CHADS-P2A2RC risk score categories. The primary outcome was a major adverse cardiac and cerebral event (MACCE), a composite of all-cause death, myocardial infarction, or stroke. Results: Up to 3 years, the high CHADS-P2A2RC risk score group had the highest incidence of MACCE (105 [12.1%], adjusted hazard ratio [HR], 2.927; 95% confidence interval [CI], 1.358–6.309; p=0.006) followed by moderate-risk (40 [1.4%], adjusted HR, 1.786; 95% CI, 0.868–3.674; p=0.115) and low-risk (9 [0.5%], reference). In secondary analyses, P2Y12 inhibitor monotherapy reduced the Bleeding Academic Research Consortium (BARC) types 2, 3, or 5 bleeding without increasing the risk of MACCE as compared with prolonged DAPT across the 3 CHADS-P2A2RC risk strata without significant interaction term (interaction p for MACCE=0.705 and interaction p for BARC types 2, 3, or 5 bleeding=0.055). Conclusions The CHADS-P2A2RC risk score is valuable in discriminating high-ischemicrisk patients. Even in such patients with a high risk of ischemic events, P2Y12 inhibitor monotherapy was associated with a lower incidence of bleeding without increased risk of ischemic events compared with prolonged DAPT.

Purpose: This study aims to examine whether perceived levels of job stress, burnout, and mental health are different according to demographic characteristics and working conditions and to investigate the direct and indirect effects of job stress and burnout on the mental health of medical faculty members. Methods: The study sample consists of 855 faculty members in 40 medical schools nationwide in the 2020 Burnout of Faculty Members of Medical Schools in Korea data with a grant from the Korean Association of Medical Colleges. This study employed structural equation modeling to construct causality among latent variables in addition to t-test, analysis of variance, and correlation coefficients for bivariate analyses. Results: Perceived job stress, burnout, and mental health levels of medical faculty members showed significant group differences by demographic characteristics and working conditions. Job stress directly affected mental health (β=0.215, p<0.01) and indirectly affected mental health via burnout (β=0.493, p<0.001). Thus burnout significantly mediated the relationship between job stress and the mental health of medical faculty members. Conclusion This study found that job stress has direct and indirect effects on the mental health of medical faculty members, and burnout partially mediated this relationship. Further studies need to intervene in job stress and burnout to prevent the adverse mental health of medical faculty members and to introduce proper measures to improve working conditions affecting job stress and burnout.