The prevalence of prediabetes has steadily increased in Korea, driven by rising obesity rates and lifestyle changes. A structured lifestyle modification program has been shown to significantly reduce the prevalence of diabetes among adults with prediabetes, with this effect sustained over time. Lifestyle modifications including medical nutrition, exercise, and behavioral psychological therapy are effective for preventing and managing diabetes, with medical nutrition therapy playing a crucial role. This study reviews the latest academic guidelines and medical nutrition therapy for diabetes prevention and to introduce the KDPS-hLSM (Korean Diabetes Prevention Study hospital-based lifestyle modification).

Gestational diabetes mellitus (GDM) affects 2~10% of pregnant women worldwide, with an increasing prevalence in South Korea that reached 18.2% in 2021. Unmanaged GDM can lead to serious complications for both mother and fetus. Effective management requires blood glucose monitoring and lifestyle changes, but many women lack sufficient knowledge about the condition. This study explores the use of smart platforms, like continuous glucose monitoring devices and mobile apps, to improve GDM education. Devices such as Dexcom G7 and FreeStyle Libre 2 enable real-time blood glucose monitoring, while apps like Health2Sync help patients share data with healthcare providers. Although these technologies offer major benefits, challenges remain in patient engagement, data accuracy, and accessibility. In conclusion, while smart platforms can enhance GDM management, they must be combined with ongoing professional support to improve health outcomes.

The prevalence of prediabetes has steadily increased in Korea, driven by rising obesity rates and lifestyle changes. A structured lifestyle modification program has been shown to significantly reduce the prevalence of diabetes among adults with prediabetes, with this effect sustained over time. Lifestyle modifications including medical nutrition, exercise, and behavioral psychological therapy are effective for preventing and managing diabetes, with medical nutrition therapy playing a crucial role. This study reviews the latest academic guidelines and medical nutrition therapy for diabetes prevention and to introduce the KDPS-hLSM (Korean Diabetes Prevention Study hospital-based lifestyle modification).

Gestational diabetes mellitus (GDM) affects 2~10% of pregnant women worldwide, with an increasing prevalence in South Korea that reached 18.2% in 2021. Unmanaged GDM can lead to serious complications for both mother and fetus. Effective management requires blood glucose monitoring and lifestyle changes, but many women lack sufficient knowledge about the condition. This study explores the use of smart platforms, like continuous glucose monitoring devices and mobile apps, to improve GDM education. Devices such as Dexcom G7 and FreeStyle Libre 2 enable real-time blood glucose monitoring, while apps like Health2Sync help patients share data with healthcare providers. Although these technologies offer major benefits, challenges remain in patient engagement, data accuracy, and accessibility. In conclusion, while smart platforms can enhance GDM management, they must be combined with ongoing professional support to improve health outcomes.

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.

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.