Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Background/Aims: Adiponectin, a hormone primarily produced by adipocytes, typically shows an inverse relationship with body mass index (BMI). However, some studies have reported a positive correlation between the two. Thus, this study aimed to examine the relationship between adiponectin level and BMI in diabetic patients, focusing on the impact of past obesity on current adiponectin levels. Methods: We conducted an observational study analyzing data from 323 diabetic patients at Kyungpook National University Hospital. Based on past and current BMIs, participants were categorized into never-obese (nn, n = 106), previously obese (on, n = 43), and persistently obese (oo, n = 73) groups based on a BMI threshold of 25 kg/m2. Adiponectin level and BMI were key variables. Kaplan–Meier analysis assessed their impact on all-cause mortality up to August 2023, with survival differences based on adiponectin quartiles and follow-up starting from patient enrollment (2010–2015). Results: The analysis revealed a significant inverse correlation between adiponectin level and past maximum BMI. The on group exhibited approximately 10% lower adiponectin levels compared to the nn group. This association remained significant after adjusting for current BMI, age, and sex, highlighting the lasting influence of previous obesity on adiponectin levels. Furthermore, survival analysis indicated that patients in the lowest adiponectin quartile had reduced survival, with a statistically significant trend (p = 0.062). Conclusions Findings of this study suggest that lower adiponectin levels, potentially reflecting past obesity, are associated with decreased survival in diabetic patients, underscoring a critical role of adiponectin in long-term health outcomes.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Purpose: This study aimed to evaluate the impact of declining surgical residency program enrollment on patient care and outcomes in colorectal cancer surgeries. Methods: This retrospective observational study included 676 patients (410 males; median age: 69 years) who underwent colorectal cancer surgery at Pusan National University Hospital between January 2018 and June 2024. Patients were divided into Group A (before December 31, 2023; with residents) and Group B (after January 1, 2024; without residents). All surgeries were performed by a single attending surgeon. Results: Preoperative variables were comparable between groups. Group A had more emergency and open surgeries, and a higher proportion of advanced-stage cancers. Overall complication rates were similar, but Group B had a longer hospital stay (9.72 days vs. 11.95 days). Specific complications such as anastomotic leakage and surgical site infections differed significantly. The overall number of surgical procedures declined markedly in 2024 compared to 2018 (77.1% vs. 49.9%). Conclusion The absence of residents did not increase overall complication rates but was associated with longer hospital stays and shifts in clinical practice. Greater reliance on attending surgeons contributed to more defensive decision-making and conservative patient management. Addressing these issues requires systemic reforms, including multidisciplinary collaboration and legal protections to improve surgical care.

Background: Skipping breakfast is associated with an increased risk of chronic inflammatory diseases. This study aimed to examine the association between breakfast-eating habits and inflammation, using high-sensitivity C-reactive protein (hs-CRP) as a marker. Methods: A total of 4,000 Korean adult males with no history of myocardial infarction, angina, stroke, diabetes, rheumatoid arthritis, cancer, or current smoking were included. Data from the 2016–2018 Korea National Health and Nutrition Examination Survey were used for analysis. The frequency of breakfast consumption was assessed through a questionnaire item in the dietary survey section asking participants about their weekly breakfast consumption routines over the past year. Participants were categorized into two groups, namely “0–2 breakfasts per week” and “3–7 breakfasts per week”; hs-CRP concentrations were measured through blood tests. Results: Comparing between the “infrequent breakfast consumption (0–2 breakfasts per week)” and “frequent breakfast consumption (3–7 breakfasts per week)” groups, the mean hs-CRP was found to be significantly higher in the “infrequent breakfast consumption” group, even after adjusting for age, body mass index, physical activity, alcohol consumption, systolic blood pressure, blood pressure medication, fasting blood glucose, and triglycerides (mean hs-CRP: frequent breakfast consumption, 1.36±0.09 mg/L; infrequent breakfast consumption, 1.17±0.05 mg/L; P-value=0.036). Conclusion Less frequent breakfast consumption was associated with elevated hs-CRP levels. Further large-scale studies incorporating adjusted measures of daily eating patterns as well as food quality and quantity are required for a deeper understanding of the role of breakfast in the primary prevention of chronic inflammatory diseases.

Purpose: To examine the factors impacting cone contrast sensitivity in Koreans aged > 60 years who are scheduled for cataract surgery and do not have congenital color vision deficiencies. Methods: The ColorDx Cone contrast test HD® (CCT-HD; Konan Medical, Inc., CA, USA) was administered to 33 Korean individuals (33 eyes) to evaluate CCT scores before and after cataract surgery, as well as changes in CCT scores according to the degree of progression by cataract type. Correlations between CCT scores and age, best corrected visual acuity (BCVA), regional retinal thickness, and length of the photoreceptor outer segment (PROS) at the fovea were analyzed in pseudophakic eyes. Results: Average scores for short-, medium-, and long-wavelength cone contrast sensitivity tests (S-CCT, M-CCT, and L-CCT, respectively) improved after surgery (p = 0.010, p = 0.001, and p = 0.028, respectively). Comparing CCT score changes before and after surgery by cataract progression, higher cataract grades were associated with greater CCT score changes, though the differences were not statistically significant (p > 0.05). In pseudophakic eyes, S-CCT scores negatively correlated with age (p = 0.017). No significant correlations were found between S-CCT, M-CCT, and L-CCT scores and BCVA, whereas S-CCT, M-CCT, and L-CCT scores positively correlated with PROS at the fovea (p < 0.001). Conclusions Cone contrast sensitivity in individuals aged > 60 years is influenced by age and cataract status and may serve as a valuable indicator of visual function in clinical research.

Background: In 2023, we experienced an outbreak from a case of undiagnosed crusted scabies, resulting in a significant number of exposed individuals and secondary cases. In this report, we describe the outbreak control measures, the attack rate, and the risk factors for acquisition of scabies among healthcare workers (HCWs). Methods: This study was conducted in a 2,700-bed tertiary care hospital in Seoul, South Korea. The attack rate was defined both for microscopic proven cases per exposed individuals and as the sum of proven and probable cases per exposed individuals. Outbreak control measures included identifying and treating all potentially exposed individuals with or without symptoms, as well as environmental disinfection. Results: From the index, there was potential quinary transmission resulting in 63 proven cases, 142 probable cases, and a total of 1,820 exposed individuals, including 734 contacts from the index case. The attack rate from the index was 7% (50/734) based on proven cases and 19% (138/734) based on proven and probable cases. Among the 526 HCWs who received preemptive topical treatment with permethrin applied once, 21 (4%) were later diagnosed as scabies. In addition, 5 of 20 HCWs (25%) with initial proven scabies had a persistent positive microscopic exam after four permethrin treatments. In the case group, there were significantly more nurses (60% vs. 43%, P = 0.007) and nurse assistants (20% vs. 9%, P = 0.006). There were significantly more cases than controls involving direct contact with the index case (94% vs. 64%, P < 0.001). Conclusion Lowering the threshold for suspicion of crusted scabies is important, as a single missed case could lead to a large outbreak. Simultaneously applying preemptive permethrin cream to all potentially exposed individuals might have been effective in preventing further transmission. However, caution is needed because the development of scabies or persistent scabies is possible even with preemptive or therapeutic treatment.

Objective: : In degenerative lumbar spondylolisthesis, interbody fusion surgery (IFS) has long been recommended as the gold standard of surgical management. However, IFS is less recommended for high-risk patients such as the elderly because it involves extensive surgery, with a long operation time and high volumes of blood loss, which lead to marked perioperative morbidity. We report an alternative primary and salvage treatment technique for high-risk lumbar spondylolisthesis through posterior lumbar element reinforcement using interspinous fixation and decompression alone without interbody fusion. Methods: : Plain radiographs, computed tomography scans, and magnetic resonance imaging, taken at different intervals, were used to measure local disc height (DH), vertebral body slippage (BS), and segmental motion angle (SMA). A Visual analogue scale and the Oswestry disability index (ODI) were applied pre-operation and at the last follow-up. Results: : The local SMA decreased significantly by 3.46°±3.07°, from 10.61°±3.42° preoperatively to 7.15±3.70 at the last follow-up (p<0.001). The DH decreased from 8.61±2.88 mm preoperatively to 8.41±2.48 mm at the last follow-up (p=0.074). The BS decreased from 3.49±4.29 mm preoperatively to 3.41±4.91 mm at the last follow-up (p=0.092). None of the patients reported worsening pain or an increased ODI after surgery, and there were no surgery-related complications. Conclusion : Posterior lumbar element reinforcement by decompression alone with SPIRE™ fixation is an alternative primary and salvage treatment option for select patients with spondylolisthesis.