Idiopathic nonspecific interstitial pneumonia (iNSIP) is recognized as a distinct entity among various types of idiopathic interstitial pneumonias. It is identified histologically by the nonspecific interstitial pneumonia pattern. A diagnosis of iNSIP is feasible once secondary causes or underlying diseases are ruled out. Usually presenting with respiratory symptoms such as shortness of breath and cough, iNSIP has a subacute or chronic course. It predominantly affects females aged 50 to 60 years who are non-smokers. Key imaging findings on chest high-resolution computed tomography include bilateral reticular opacities in lower lungs, traction bronchiectasis, reduced lung volumes and, ground-glass opacities. Abnormalities are typically diffuse across both lungs with subpleural distributions. Treatment often involves systemic steroids, either alone or in combination with other immunosuppressants, although evidence supporting effectiveness of these treatments is limited. Prognosis is generally more favorable for iNSIP than for idiopathic pulmonary fibrosis, with many studies reporting a 5-year survival rate above 70%. Antifibrotic agents should be considered in a condition, termed progressive pulmonary fibrosis, where pulmonary fibrosis progressively worsens.

Purpose: This study evaluated the educational impact of an artificial intelligence (AI)–based decision support system for breast ultrasonography (US) on medical professionals not specialized in breast imaging. Methods: In this multi-case, multi-reader study, educational materials, including American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors, were provided alongside corresponding AI results during training. The AI system presented results in the form of AIheatmaps, AI scores, and AI-provided BI-RADS assessment categories. Forty-two readers evaluated the test set in three sessions: the first session (S1) occurred before the educational intervention, the second session (S2) followed education without AI assistance, and the third session (S3) took place after education with AI assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and overall performance, were compared between the sessions. Results: The mean sensitivity increased from 66.5% (95% confidence interval [CI], 59.2% to 73.7%) to 88.7% (95% CI, 84.1% to 93.3%), with a statistically significant difference (P<0.001), and the AUC non-significantly increased from 0.664 (95% CI, 0.606 to 0.723) to 0.684 (95% CI, 0.620 to 0.748) (P=0.300). Both measures were higher in S2 than in S1. The AI-achieved AUC was comparable to that of the expert reader (0.747 [95% CI, 0.640 to 0.855] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.217). Additionally, with AI assistance, the mean AUC for inexperienced readers was not significantly different from that of the expert reader (0.745 [95% CI, 0.660 to 0.830] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.120). Conclusion The mean AUC and sensitivity improved after incorporating AI into breast US education and interpretation. AI systems with high-level performance for breast US can potentially be used as educational tools in the interpretation of breast US images.

Purpose: Cognitive behavioral therapy (CBT) has long been recognized as an effective treatment for depression and suicidality.Virtual reality (VR) technology is widely used for cognitive training for conditions such as anxiety disorder and post-traumatic stress disorder, but little research has considered VR-based CBT for depressive symptoms and suicidality. We tested the effectiveness and safety of a VR-based CBT program for depressive disorders. Materials and Methods: We recruited 57 participants from May 2022 through February 2023 using online advertisements. This multi-center, assessor-blinded, randomized, controlled exploratory trial used two groups: VR treatment group and treat as usual (TAU) group. VR treatment group received a VR mental health training/education program. TAU group received standard pharmacotherapy. Assessments were conducted at baseline, immediately after the 6-week treatment period, and 4 weeks after the end of the treatment period in each group. Results: Depression scores decreased significantly over time in both VR treatment and TAU groups, with no differences between the two groups. The suicidality score decreased significantly only in VR group. No group differences were found in the remission or response rate for depression, perceived stress, or clinical severity. No adverse events or motion sickness occurred during the VR treatment program. Conclusion VR CBT treatment for major depressive disorder has the potential to be equivalent to the gold-standard pharmacotherapy in reducing depressive symptoms, suicidality, and related clinical symptoms, with no difference in improvement found in this study. Thus, VR-based CBT might be an effective alternative to pharmacotherapy for depressive disorders.

Background: Studies on predictive markers of insulin resistance (IR) and elevated liver transaminases in children and adolescents are limited. We evaluated the predictive capabilities of the single-point insulin sensitivity estimator (SPISE) index, metabolic score for insulin resistance (METS-IR), homeostasis model assessment of insulin resistance (HOMA-IR), the triglyceride (TG)/ high-density lipoprotein cholesterol (HDL-C) ratio, and the triglyceride-glucose index (TyG) for IR and alanine aminotransferase (ALT) elevation in this population. Methods: Data from 1,593 participants aged 10 to 18 years were analyzed using a nationwide survey. Logistic regression analysis was performed with IR and ALT elevation as dependent variables. Receiver operating characteristic (ROC) curves were generated to assess predictive capability. Proportions of IR and ALT elevation were compared after dividing participants based on parameter cutoff points. Results: All parameters were significantly associated with IR and ALT elevation, even after adjusting for age and sex, and predicted IR and ALT elevation in ROC curves (all P<0.001). The areas under the ROC curve of SPISE and METS-IR were higher than those of TyG and TG/HDL-C for predicting IR and were higher than those of HOMA-IR, TyG, and TG/HDL-C for predicting ALT elevation. The proportions of individuals with IR and ALT elevation were higher among those with METS-IR, TyG, and TG/ HDL-C values higher than the cutoff points, whereas they were lower among those with SPISE higher than the cutoff point. Conclusion SPISE and METS-IR are superior to TG/HDL-C and TyG in predicting IR and ALT elevation. Thus, this study identified valuable predictive markers for young individuals.

Background/Aims: To evaluate the associations between pediatric fatty liver severity, bioelectrical impedance analysis (BIA), and magnetic resonance imaging parameters, including total psoas muscle surface area (tPMSA) and paraspinal muscle fat (PMF). Methods: Children and adolescents who underwent BIA and liver magnetic resonance imaging between September 2022 and November 2023 were included. Linear regression analyses identified predictors of liver proton density fat fraction (PDFF) including BIA parameters, tPMSA, and PMF. Ordinal logistic regression analysis identified the association between these parameters and fatty liver grades. Pearson’s correlation coefficients were used to evaluate the relationships between tPMSA and muscle-related BIA parameters, and between PMF and fat-related BIA parameters. Results: Overall, 74 participants aged 8 to 16 years were included in the study. In the linear regression analyses, the percentage of body fat was positively associated with PDFF in all participants, whereas muscle-related BIA parameters were negatively associated with PDFF in participants with obesity. PMF and the PMF index were positively associated with PDFF in normalweight and overweight participants. In the ordinal logistic regression, percentage of body fat was positively associated with fatty liver grade in normal-weight and overweight participants and those with obesity, whereas muscle-related BIA parameters were negatively associated with fatty liver grade in participants with obesity. The PMF index was positively associated with fatty liver grade in normal/overweight participants. In the Pearson correlation analysis, muscle-related BIA parameters were correlated with tPMSA, and the fat-related BIA parameters were correlated with PMF. Conclusions BIA parameters and PMF are potential screening tools for assessing fatty liver in children.

Idiopathic nonspecific interstitial pneumonia (iNSIP) is recognized as a distinct entity among various types of idiopathic interstitial pneumonias. It is identified histologically by the nonspecific interstitial pneumonia pattern. A diagnosis of iNSIP is feasible once secondary causes or underlying diseases are ruled out. Usually presenting with respiratory symptoms such as shortness of breath and cough, iNSIP has a subacute or chronic course. It predominantly affects females aged 50 to 60 years who are non-smokers. Key imaging findings on chest high-resolution computed tomography include bilateral reticular opacities in lower lungs, traction bronchiectasis, reduced lung volumes and, ground-glass opacities. Abnormalities are typically diffuse across both lungs with subpleural distributions. Treatment often involves systemic steroids, either alone or in combination with other immunosuppressants, although evidence supporting effectiveness of these treatments is limited. Prognosis is generally more favorable for iNSIP than for idiopathic pulmonary fibrosis, with many studies reporting a 5-year survival rate above 70%. Antifibrotic agents should be considered in a condition, termed progressive pulmonary fibrosis, where pulmonary fibrosis progressively worsens.

Purpose: This study evaluated the educational impact of an artificial intelligence (AI)–based decision support system for breast ultrasonography (US) on medical professionals not specialized in breast imaging. Methods: In this multi-case, multi-reader study, educational materials, including American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors, were provided alongside corresponding AI results during training. The AI system presented results in the form of AIheatmaps, AI scores, and AI-provided BI-RADS assessment categories. Forty-two readers evaluated the test set in three sessions: the first session (S1) occurred before the educational intervention, the second session (S2) followed education without AI assistance, and the third session (S3) took place after education with AI assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and overall performance, were compared between the sessions. Results: The mean sensitivity increased from 66.5% (95% confidence interval [CI], 59.2% to 73.7%) to 88.7% (95% CI, 84.1% to 93.3%), with a statistically significant difference (P<0.001), and the AUC non-significantly increased from 0.664 (95% CI, 0.606 to 0.723) to 0.684 (95% CI, 0.620 to 0.748) (P=0.300). Both measures were higher in S2 than in S1. The AI-achieved AUC was comparable to that of the expert reader (0.747 [95% CI, 0.640 to 0.855] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.217). Additionally, with AI assistance, the mean AUC for inexperienced readers was not significantly different from that of the expert reader (0.745 [95% CI, 0.660 to 0.830] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.120). Conclusion The mean AUC and sensitivity improved after incorporating AI into breast US education and interpretation. AI systems with high-level performance for breast US can potentially be used as educational tools in the interpretation of breast US images.

Purpose: Cognitive behavioral therapy (CBT) has long been recognized as an effective treatment for depression and suicidality.Virtual reality (VR) technology is widely used for cognitive training for conditions such as anxiety disorder and post-traumatic stress disorder, but little research has considered VR-based CBT for depressive symptoms and suicidality. We tested the effectiveness and safety of a VR-based CBT program for depressive disorders. Materials and Methods: We recruited 57 participants from May 2022 through February 2023 using online advertisements. This multi-center, assessor-blinded, randomized, controlled exploratory trial used two groups: VR treatment group and treat as usual (TAU) group. VR treatment group received a VR mental health training/education program. TAU group received standard pharmacotherapy. Assessments were conducted at baseline, immediately after the 6-week treatment period, and 4 weeks after the end of the treatment period in each group. Results: Depression scores decreased significantly over time in both VR treatment and TAU groups, with no differences between the two groups. The suicidality score decreased significantly only in VR group. No group differences were found in the remission or response rate for depression, perceived stress, or clinical severity. No adverse events or motion sickness occurred during the VR treatment program. Conclusion VR CBT treatment for major depressive disorder has the potential to be equivalent to the gold-standard pharmacotherapy in reducing depressive symptoms, suicidality, and related clinical symptoms, with no difference in improvement found in this study. Thus, VR-based CBT might be an effective alternative to pharmacotherapy for depressive disorders.

Idiopathic nonspecific interstitial pneumonia (iNSIP) is recognized as a distinct entity among various types of idiopathic interstitial pneumonias. It is identified histologically by the nonspecific interstitial pneumonia pattern. A diagnosis of iNSIP is feasible once secondary causes or underlying diseases are ruled out. Usually presenting with respiratory symptoms such as shortness of breath and cough, iNSIP has a subacute or chronic course. It predominantly affects females aged 50 to 60 years who are non-smokers. Key imaging findings on chest high-resolution computed tomography include bilateral reticular opacities in lower lungs, traction bronchiectasis, reduced lung volumes and, ground-glass opacities. Abnormalities are typically diffuse across both lungs with subpleural distributions. Treatment often involves systemic steroids, either alone or in combination with other immunosuppressants, although evidence supporting effectiveness of these treatments is limited. Prognosis is generally more favorable for iNSIP than for idiopathic pulmonary fibrosis, with many studies reporting a 5-year survival rate above 70%. Antifibrotic agents should be considered in a condition, termed progressive pulmonary fibrosis, where pulmonary fibrosis progressively worsens.

Purpose: This study evaluated the educational impact of an artificial intelligence (AI)–based decision support system for breast ultrasonography (US) on medical professionals not specialized in breast imaging. Methods: In this multi-case, multi-reader study, educational materials, including American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors, were provided alongside corresponding AI results during training. The AI system presented results in the form of AIheatmaps, AI scores, and AI-provided BI-RADS assessment categories. Forty-two readers evaluated the test set in three sessions: the first session (S1) occurred before the educational intervention, the second session (S2) followed education without AI assistance, and the third session (S3) took place after education with AI assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and overall performance, were compared between the sessions. Results: The mean sensitivity increased from 66.5% (95% confidence interval [CI], 59.2% to 73.7%) to 88.7% (95% CI, 84.1% to 93.3%), with a statistically significant difference (P<0.001), and the AUC non-significantly increased from 0.664 (95% CI, 0.606 to 0.723) to 0.684 (95% CI, 0.620 to 0.748) (P=0.300). Both measures were higher in S2 than in S1. The AI-achieved AUC was comparable to that of the expert reader (0.747 [95% CI, 0.640 to 0.855] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.217). Additionally, with AI assistance, the mean AUC for inexperienced readers was not significantly different from that of the expert reader (0.745 [95% CI, 0.660 to 0.830] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.120). Conclusion The mean AUC and sensitivity improved after incorporating AI into breast US education and interpretation. AI systems with high-level performance for breast US can potentially be used as educational tools in the interpretation of breast US images.