Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Background/Aims: To analyze the characteristics of the sputum microbiota of patients with nontuberculous mycobacteria pulmonary disease (NTM-PD) based on treatment status. Methods: Twenty-eight sputum samples from 14 patients with NTM-PD, including 14 samples from the microbiologically cured group (7 at baseline and 7 during follow-up) and 14 from the treatment-refractory group (7 at baseline and 7 during follow-up) were included in this study. Bacterial microbiota was analyzed by sequencing the V3–V4 region of the 16S rRNA gene. Results: Among the 14 patients, most had infections with Mycobacterium avium complex (n = 6), followed by Mycobacterium abscessus (n = 5); three patients exhibited mixed infection with both organisms. Alpha-diversity was higher in the cured group than in the treatment refractory group in both the baseline sputum (ACE, p = 0.005; Chao1, p = 0.010; Jackknife, p = 0.022, 0.043; Shannon, p = 0.048) and follow-up sputum (ACE, p = 0.018). Linear discriminant analysis effect size revealed that several taxa showed differential distributions based on treatment status. At the species level, Streptococcus pneumoniae, Prevotella melaninogenica, Haemophilus parahaemolyticus, Haemophilus haemolyticus, Fusobacterium nucleatum, Neisseria elongata, and Prevotella denticola were more abundant in sputum from the microbiologically cured group than in that from the refractory group (all p < 0.05). Conclusions In contrast to patients with treatment-refractory NTM-PD, those with stable disease without recurrence had higher microbial diversity in their sputum, including several predominant taxa.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study focused on analyzing the contact pressure and area on different patellar component designs in total knee arthroplasty (TKA) to evaluate biomechanics related to the patellofemoral (PF) joint. Materials and Methods: The patellar components studied included the dome design, modified dome design, and anatomical design implants. Using finite element analysis and mechanical testing, the pressure and area were evaluated. The first loading condition was simulated at flexion angles of 0°, 15°, 45°, 90°, 120°, and 150°. The second loading condition was simulated for a clinically relevant scenario, involving a 2-mm medial shift at a flexion angle of 45°. Results: For both the modified dome and anatomical designs, the contact area and pressure increased with the flexion angle. The dome design reached its maximum contact area at a flexion angle of 120°. Among the designs, the anatomical design had the largest contact area and a lower contact pressure compared to the dome and modified dome designs. However, when a medial shift of 2 mm was simulated at a 45° flexion angle, which can occur clinically, the anatomical design showed edge contact, leading to higher contact pressure and reduced contact area. In contrast, the modified dome design demonstrated the lowest contact pressure and the greatest contact area under the same shifted conditions. Conclusion These findings suggest that the design of the patellar component significantly affects patellar biomechanics and stability. Specifically, the modified dome design showed improved biomechanical effects in clinically relevant scenarios. Therefore, patellar components with a modified dome design are expected to better manage PF joint pain and reduce complications in TKA.

Purpose: The impact of vitamin D deficiency on nonalcoholic fatty liver disease (NAFLD) risk in individuals without obesity or insulin resistance has not been thoroughly evaluated. We aimed to identify whether low serum levels of 25(OH)D independently contribute to NAFLD risk in nonobese or lean individuals. Materials and Methods: This study analyzed 241208 asymptomatic health check-up examinees who had abdominal ultrasonography. NAFLD risk was evaluated based on obesity status and serum 25(OH)D levels. Results: The overall NAFLD prevalence was 25.5%. Among the 178630 nonobese and 126909 lean participants, the prevalence rates were 13.4% and 6.7%, respectively. The multivariable adjusted odds ratios (ORs) [95% confidence intervals (CI)] for the prevalence of NAFLD, comparing serum 25(OH)D levels of 10–19 and ≥20 ng/mL with <10 ng/mL, were 0.96 (0.93–0.99) and 0.80 (0.77–0.83), respectively. Among nonobese participants, the corresponding adjusted ORs (95% CI) were 0.94 (0.90–0.99) and 0.77 (0.73–0.81), respectively. Similar results were observed among lean participants, with those having a 25(OH)D level of ≥20 ng/mL demonstrating a significantly lower odds of NAFLD (adjusted OR, 0.76; 95% CI, 0.70–0.83). Moreover, these results were consistent even among nonobese and lean individuals who showed no signs of insulin resistance. Conclusion Insufficient 25(OH)D levels independently increased the risk of NAFLD, suggesting its role in the NAFLD pathogenesis, regardless of obesity or insulin resistance status. Considering the established relationship between vitamin D deficiency and nonobese/lean NAFLD, maintaining adequate 25(OH)D levels may aid in preventing the development of NAFLD, even among nonobese or lean individuals.

Background: This study compares ultrasound and surgical findings of anatomical variations in de Quervain’s disease. Methods: Seventy-four wrists from patients with unilateral de Quervain’s disease were examined through ultrasonography and surgery. Presence of intracompartment septum, abductor pollicis longus (APL) slips, and selective stenosis were verified by both methods. Two orthopedic surgeons assessed ultrasound findings for intra- and interobserver reliability. Results: Amongst 74 participants (43 women and 31 men), 60.8% had a complete septum, 31.1% had an incomplete septum, and 8.1% had no septum; 70.3% had multi-slip APL and 66.2% had extensor pollicis brevis stenosis. Surgical and ultrasonographic findings displayed a high level of sensitivity and specificity. Intraobserver reliability was high, and interobserver reliability was substantial. Conclusions The study confirms ultrasonography’s reliability in identifying anatomical variations in de Quervain’s disease, with high sensitivity, specificity, and substantial intra- and interobserver reliability, emphasizing its usefulness in preoperative assessment and planning.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Background/Aims: To analyze the characteristics of the sputum microbiota of patients with nontuberculous mycobacteria pulmonary disease (NTM-PD) based on treatment status. Methods: Twenty-eight sputum samples from 14 patients with NTM-PD, including 14 samples from the microbiologically cured group (7 at baseline and 7 during follow-up) and 14 from the treatment-refractory group (7 at baseline and 7 during follow-up) were included in this study. Bacterial microbiota was analyzed by sequencing the V3–V4 region of the 16S rRNA gene. Results: Among the 14 patients, most had infections with Mycobacterium avium complex (n = 6), followed by Mycobacterium abscessus (n = 5); three patients exhibited mixed infection with both organisms. Alpha-diversity was higher in the cured group than in the treatment refractory group in both the baseline sputum (ACE, p = 0.005; Chao1, p = 0.010; Jackknife, p = 0.022, 0.043; Shannon, p = 0.048) and follow-up sputum (ACE, p = 0.018). Linear discriminant analysis effect size revealed that several taxa showed differential distributions based on treatment status. At the species level, Streptococcus pneumoniae, Prevotella melaninogenica, Haemophilus parahaemolyticus, Haemophilus haemolyticus, Fusobacterium nucleatum, Neisseria elongata, and Prevotella denticola were more abundant in sputum from the microbiologically cured group than in that from the refractory group (all p < 0.05). Conclusions In contrast to patients with treatment-refractory NTM-PD, those with stable disease without recurrence had higher microbial diversity in their sputum, including several predominant taxa.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)