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.

Mutational dysphonia, a condition in which a pre-adolescent voice persists into adulthood, can significantly impact personal and professional life but is treatable with voice therapy. A patient with mutational dysphonia usually has a voice that is weak, breathy, or diplophonic, often classified as a “falsetto” voice. In this case report, we present a 20-year-old male who had a typical voice of a boy before adolescence, making it difficult to diagnose as mutational dysphonia. After voice therapy, he successfully gained his post-adolescent voice, highlighting the importance of thorough diagnosis and personalized treatment for mutational dysphonia.

Mutational dysphonia, a condition in which a pre-adolescent voice persists into adulthood, can significantly impact personal and professional life but is treatable with voice therapy. A patient with mutational dysphonia usually has a voice that is weak, breathy, or diplophonic, often classified as a “falsetto” voice. In this case report, we present a 20-year-old male who had a typical voice of a boy before adolescence, making it difficult to diagnose as mutational dysphonia. After voice therapy, he successfully gained his post-adolescent voice, highlighting the importance of thorough diagnosis and personalized treatment for mutational dysphonia.

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.

Mutational dysphonia, a condition in which a pre-adolescent voice persists into adulthood, can significantly impact personal and professional life but is treatable with voice therapy. A patient with mutational dysphonia usually has a voice that is weak, breathy, or diplophonic, often classified as a “falsetto” voice. In this case report, we present a 20-year-old male who had a typical voice of a boy before adolescence, making it difficult to diagnose as mutational dysphonia. After voice therapy, he successfully gained his post-adolescent voice, highlighting the importance of thorough diagnosis and personalized treatment for mutational dysphonia.

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.

Background: The ongoing medical crisis in Korea has severely impacted the operational environment of intensive care units (ICU), posing significant challenges to quality care for critically ill patients. This study aimed to evaluate the effects of the ongoing crisis on ICUs. Methods: A survey was conducted in July 2024 among intensivists in charge of ICUs at institutions accredited by the Korean Society of Critical Care Medicine for critical care. The survey compared data from January 2024 (pre-crisis) and June 2024 (post-crisis) on the number ICU beds, staffing composition, work hours, and the number and roles of nurse practitioners. Results: Among the total of 71 participating ICUs, 22 experienced a reduction in the number of operational beds, with a median decrease of six beds per unit, totaling 127 beds across these ICUs. The numbers of residents and interns decreased from an average of 2.3 to 0.1 per ICU, and the average weekly working hours of intensivists increased from 62.3 to 78.8 hours. Nurse practitioners helped fill staffing gaps, with their numbers rising from 150 to 242 across ICUs, and their scope of practice expanded accordingly. Conclusions The medical crisis has led to major changes in the critical care system, including staffing shortages, increased workloads, and an expanded role for nurse practitioners. This is a critical moment to foster interest and engage in active discussions aimed at creating a sustainable and resilient ICU system.

Purpose: The leaves of Perilla frutescens var. acuta (PFA) are generally reported to have antioxidant, anti-allergic, anti-inflammatory, and antitumor effects and commonly used as a traditional medicine in East Asia. This study aimed to investigate the protective effect and antioxidant activity of PFA on busulfan-induced testicular dysfunction, histological damage, oxidative stress (OS), sperm quality, and hormone levels using a mouse model. Materials and Methods: C57BL/6 male mice were divided into four groups: control, busulfan-only treated, and varying concentrations of PFA (100 and 200 mg/kg) with busulfan. In the busulfan group, 40 mg/kg of busulfan was intraperitoneally injected to induce azoospermia. Mice were orally administered PFA for 35 consecutive days after busulfan administration.Samples were collected and assessed for testis/body weight, testicular histopathology, sperm quality, serum hormone levels, and OS to evaluate the effects of PFA treatment on spermatogenesis dysfunction induced by busulfan. Results: The busulfan-induced testicular dysfunction model showed reduced testis weight, adverse histological changes, significantly decreased sex hormones and sperm quality, and attenuated OS. These results indicate that PFA treatment significantly increased testis weight, testis/body weight, epididymal sperm count, motility, and testosterone level compared with busulfan alone. PFA treatment also attenuated the busulfan-induced histological changes. Furthermore, compared with mice treated with busulfan alone, PFA supplementation upregulated the testicular mRNA expression of the antioxidant enzymes superoxide dismutase 1 (Sod1) and glutathione peroxidase 1 (Gpx1), with a decrease in malondialdehyde (MDA) production and an increase in SOD and GPx activities. Conclusions This study shows that PFA exerts a protective effect against testicular damage by attenuating OS induced by busulfan. Our results suggest that PFA is a potentially relevant drug used to decrease the side effects induced by busulfan on testicular function and sperm during cancer chemotherapy.