Purpose: To investigate the clinical characteristics of South Korean patients with pericentral retinitis pigmentosa (RP) and to identify clinical biomarkers associated with rapid visual acuity decline based on baseline factors. Methods: This retrospective study included 59 eyes of 31 patients diagnosed with pericentral RP. Comprehensive ophthalmological examinations and genetic sequencing were conducted to assess the baseline characteristics. For biomarker analysis, eyes were categorized into two groups based on the annual rate of change in visual acuity. The clinical findings of the two groups were evaluated to identify the biomarkers associated with rapid loss of visual acuity. Results: Patients with pericentral RP in this study exhibited a mean best-corrected visual acuity of 0.17 ± 0.23 in logarithm of the minimum angle of resolution. The visual field test showed annular or semicircular scotoma with relatively preserved periphery and 27 eyes (45.8%) exhibited no macular complications in optical coherence tomography. Genetic analysis identified genes associated with previous typical and pericentral RP studies but also highlighted that many genetic causes of pericentral RP remain unidentified. Of the 55 eyes for which the rate of visual acuity change could be estimated, 18 exhibited an annual decline of ≥10%, whereas 37 showed an annual decline of <10%. Male sex and prolonged b-wave latency on dark-adapted 0.01 electroretinogram correlated with rapid visual acuity decline in the multivariate analysis. Conclusions South Korean patients with pericentral RP exhibited a milder phenotype compared to typical RP patients reported in previous studies. Genetic analysis revealed heterogeneity, with mutations in some genes commonly associated with milder forms of RP. Male sex and prolonged b-wave latency on dark-adapted 0.01 electroretinogram were significant biomarkers for predicting rapid visual acuity decline. Monitoring initial b-wave latency is important for predicting visual decline, particularly in male patients with pericentral RP.

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.

Purpose: To investigate the clinical characteristics of South Korean patients with pericentral retinitis pigmentosa (RP) and to identify clinical biomarkers associated with rapid visual acuity decline based on baseline factors. Methods: This retrospective study included 59 eyes of 31 patients diagnosed with pericentral RP. Comprehensive ophthalmological examinations and genetic sequencing were conducted to assess the baseline characteristics. For biomarker analysis, eyes were categorized into two groups based on the annual rate of change in visual acuity. The clinical findings of the two groups were evaluated to identify the biomarkers associated with rapid loss of visual acuity. Results: Patients with pericentral RP in this study exhibited a mean best-corrected visual acuity of 0.17 ± 0.23 in logarithm of the minimum angle of resolution. The visual field test showed annular or semicircular scotoma with relatively preserved periphery and 27 eyes (45.8%) exhibited no macular complications in optical coherence tomography. Genetic analysis identified genes associated with previous typical and pericentral RP studies but also highlighted that many genetic causes of pericentral RP remain unidentified. Of the 55 eyes for which the rate of visual acuity change could be estimated, 18 exhibited an annual decline of ≥10%, whereas 37 showed an annual decline of <10%. Male sex and prolonged b-wave latency on dark-adapted 0.01 electroretinogram correlated with rapid visual acuity decline in the multivariate analysis. Conclusions South Korean patients with pericentral RP exhibited a milder phenotype compared to typical RP patients reported in previous studies. Genetic analysis revealed heterogeneity, with mutations in some genes commonly associated with milder forms of RP. Male sex and prolonged b-wave latency on dark-adapted 0.01 electroretinogram were significant biomarkers for predicting rapid visual acuity decline. Monitoring initial b-wave latency is important for predicting visual decline, particularly in male patients with pericentral RP.

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.

This study explores the development, roles, and key initiatives of the Regional Environmental Health Centers in Korea, detailing their evolution through four distinct phases and their impact on environmental health policy and local governance. It chronicles the establishment and transformation of these centers from their inception in May 2007, through four developmental stages. Originally named Environmental Disease Research Centers, they were subsequently renamed Environmental Health Centers following legislative changes. The analysis includes the expansion in the number of centers, the transfer of responsibilities to local governments, and the launch of significant projects such as the Korean Children’s Environmental Health Study (Ko-CHENS ). During the initial phase (May 2007–February 2009), the 10 centers concentrated on research-driven activities, shifting from a media-centered to a receptor-centered approach. In the second phase, prompted by the enactment of the Environmental Health Act, six additional centers were established, broadening their scope to address national environmental health issues. The third phase introduced Ko-CHENS, a 20-year national cohort project designed to influence environmental health policy by integrating research findings into policy frameworks. The fourth phase marked a decentralization of authority, empowering local governments and redefining the centers' roles to focus on regional environmental health challenges. The Regional Environmental Health Centers have significantly evolved and now play a crucial role in addressing local environmental health issues and supporting local government policies. Their capacity to adapt and respond to region-specific challenges is essential for the effective implementation of environmental health policies, reflecting geographical, socioeconomic, and demographic differences.

Purpose: To investigate the clinical characteristics of South Korean patients with pericentral retinitis pigmentosa (RP) and to identify clinical biomarkers associated with rapid visual acuity decline based on baseline factors. Methods: This retrospective study included 59 eyes of 31 patients diagnosed with pericentral RP. Comprehensive ophthalmological examinations and genetic sequencing were conducted to assess the baseline characteristics. For biomarker analysis, eyes were categorized into two groups based on the annual rate of change in visual acuity. The clinical findings of the two groups were evaluated to identify the biomarkers associated with rapid loss of visual acuity. Results: Patients with pericentral RP in this study exhibited a mean best-corrected visual acuity of 0.17 ± 0.23 in logarithm of the minimum angle of resolution. The visual field test showed annular or semicircular scotoma with relatively preserved periphery and 27 eyes (45.8%) exhibited no macular complications in optical coherence tomography. Genetic analysis identified genes associated with previous typical and pericentral RP studies but also highlighted that many genetic causes of pericentral RP remain unidentified. Of the 55 eyes for which the rate of visual acuity change could be estimated, 18 exhibited an annual decline of ≥10%, whereas 37 showed an annual decline of <10%. Male sex and prolonged b-wave latency on dark-adapted 0.01 electroretinogram correlated with rapid visual acuity decline in the multivariate analysis. Conclusions South Korean patients with pericentral RP exhibited a milder phenotype compared to typical RP patients reported in previous studies. Genetic analysis revealed heterogeneity, with mutations in some genes commonly associated with milder forms of RP. Male sex and prolonged b-wave latency on dark-adapted 0.01 electroretinogram were significant biomarkers for predicting rapid visual acuity decline. Monitoring initial b-wave latency is important for predicting visual decline, particularly in male patients with pericentral RP.

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.

This study explores the development, roles, and key initiatives of the Regional Environmental Health Centers in Korea, detailing their evolution through four distinct phases and their impact on environmental health policy and local governance. It chronicles the establishment and transformation of these centers from their inception in May 2007, through four developmental stages. Originally named Environmental Disease Research Centers, they were subsequently renamed Environmental Health Centers following legislative changes. The analysis includes the expansion in the number of centers, the transfer of responsibilities to local governments, and the launch of significant projects such as the Korean Children’s Environmental Health Study (Ko-CHENS ). During the initial phase (May 2007–February 2009), the 10 centers concentrated on research-driven activities, shifting from a media-centered to a receptor-centered approach. In the second phase, prompted by the enactment of the Environmental Health Act, six additional centers were established, broadening their scope to address national environmental health issues. The third phase introduced Ko-CHENS, a 20-year national cohort project designed to influence environmental health policy by integrating research findings into policy frameworks. The fourth phase marked a decentralization of authority, empowering local governments and redefining the centers' roles to focus on regional environmental health challenges. The Regional Environmental Health Centers have significantly evolved and now play a crucial role in addressing local environmental health issues and supporting local government policies. Their capacity to adapt and respond to region-specific challenges is essential for the effective implementation of environmental health policies, reflecting geographical, socioeconomic, and demographic differences.

Purpose: To investigate the clinical characteristics of South Korean patients with pericentral retinitis pigmentosa (RP) and to identify clinical biomarkers associated with rapid visual acuity decline based on baseline factors. Methods: This retrospective study included 59 eyes of 31 patients diagnosed with pericentral RP. Comprehensive ophthalmological examinations and genetic sequencing were conducted to assess the baseline characteristics. For biomarker analysis, eyes were categorized into two groups based on the annual rate of change in visual acuity. The clinical findings of the two groups were evaluated to identify the biomarkers associated with rapid loss of visual acuity. Results: Patients with pericentral RP in this study exhibited a mean best-corrected visual acuity of 0.17 ± 0.23 in logarithm of the minimum angle of resolution. The visual field test showed annular or semicircular scotoma with relatively preserved periphery and 27 eyes (45.8%) exhibited no macular complications in optical coherence tomography. Genetic analysis identified genes associated with previous typical and pericentral RP studies but also highlighted that many genetic causes of pericentral RP remain unidentified. Of the 55 eyes for which the rate of visual acuity change could be estimated, 18 exhibited an annual decline of ≥10%, whereas 37 showed an annual decline of <10%. Male sex and prolonged b-wave latency on dark-adapted 0.01 electroretinogram correlated with rapid visual acuity decline in the multivariate analysis. Conclusions South Korean patients with pericentral RP exhibited a milder phenotype compared to typical RP patients reported in previous studies. Genetic analysis revealed heterogeneity, with mutations in some genes commonly associated with milder forms of RP. Male sex and prolonged b-wave latency on dark-adapted 0.01 electroretinogram were significant biomarkers for predicting rapid visual acuity decline. Monitoring initial b-wave latency is important for predicting visual decline, particularly in male patients with pericentral RP.