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.

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.

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.

While the Korean Triage and Acuity Scale (KTAS) was introduced in 2016 as a tool to identify patients at risk of catastrophic events, including death in the ED, the triage system for the pre-hospital stage still lacks evidence. The pre-hospital stage is characterized by time-sensitive and complex scenarios, where rapid and accurate decision-making is paramount to optimize patient outcomes. Despite the vital role of pre-hospital care providers, the invalidated and subjective current triage system consisting of 4-stages is still used at the pre-hospital stage, and hence, it needs to be modified to be more objective, standardized, and reliable. To improve the Korean emergency medical system, the pre-hospital KTAS (Pre-KTAS) was developed in 2020, and then two pilot projects were conducted in 2022 and 2023. This paper not only reveals the results of the first and second pilot projects for Pre-KTAS but also highlights the potential benefits of using this newly developed triage tool in the pre-hospital setting. Furthermore, this paper suggests ways to improve the emergency medical system (EMS) in Korea by improving patient safety, resource allocation, and overall emergency response efficiency.

Purpose: Atrial fibrillation (AF) patients with low to intermediate risk, defined as non-gender CHA2DS2-VASc score of 0–1, are still at risk of stroke. This study verified the usefulness of ABCD score [age (≥60 years), B-type natriuretic peptide (BNP) or N-terminal pro-BNP (≥300 pg/mL), creatinine clearance (<50 mL/min/1.73 m2 ), and dimension of the left atrium (≥45 mm)] for stroke risk stratification in non-gender CHA2DS2-VASc score 0–1. Materials and Methods: This multi-center cohort study retrospectively analyzed AF patients with non-gender CHA2DS2-VASc score 0–1. The primary endpoint was the incidence of stroke with or without antithrombotic therapy (ATT). An ABCD score was validated. Results: Overall, 2694 patients [56.3±9.5 years; female, 726 (26.9%)] were followed-up for 4.0±2.8 years. The overall stroke rate was 0.84/100 person-years (P-Y), stratified as follows: 0.46/100 P-Y for an ABCD score of 0; 1.02/100 P-Y for an ABCD score ≥1. The ABCD score was superior to non-gender CHA2DS2-VASc score in the stroke risk stratification (C-index=0.618, p=0.015; net reclassification improvement=0.576, p=0.040; integrated differential improvement=0.033, p=0.066). ATT was prescribed in 2353 patients (86.5%), and the stroke rate was significantly lower in patients receiving non-vitamin K antagonist oral anticoagulant (NOAC) therapy and an ABCD score ≥1 than in those without ATT (0.44/100 P–Y vs. 1.55/100 P-Y; hazard ratio=0.26, 95% confidence interval 0.11–0.63, p=0.003). Conclusion The biomarker-based ABCD score demonstrated improved stroke risk stratification in AF patients with non-gender CHA2DS2-VASc score 0–1. Furthermore, NOAC with an ABCD score ≥1 was associated with significantly lower stroke rate in AF patients with non-gender CHA2DS2-VASc score 0–1.

Korean Society of Thyroid-Head and Neck Surgery appointed a Task Force to provide guidance on the implementation of a surgical treatment of oral cancer. MEDLINE databases were searched for articles on subjects related to “surgical management of oral cancer” published in English. Results were restricted to systematic reviews, randomized control trials/controlled clinical trials, and observational studies. The quality of evidence was rated with use RoBANS (Risk of Bias Assessment Tool for Nonrandomized Studies) and AMSTAR (A Measurement Tool to Assess the Methodological Quality of Systematic Reviews). Evidence-based recommendations for practice were ranked according to the American College of Physicians grading system. Additional directives are provided as expert opinions and Delphi questionnaire when insufficient evidence existed. The Committee developed 68 evidence-based recommendations in 34 categories intended to assist clinicians and patients and counselors, and health policy-makers. Proper surgical treatment selection for oral cancer, which is directed by patient- and subsite-specific factors, remains the greatest predictor of successful treatment outcomes. These guidelines are intended for use in conjunction with the individual patient's treatment goals.
Advisory Committees ; Bias (Epidemiology) ; Carcinoma, Squamous Cell ; Counseling ; Expert Testimony ; Humans ; Mouth Neoplasms ; Neck ; Republic of Korea

Korean Society of Thyroid-Head and Neck Surgery appointed a Task Force to develop clinical practice guidelines for the surgical treatment of laryngeal cancer. This Task Force conducted a systematic search of the EMBASE, MEDLINE, Cochrane Library, and KoreaMed databases to identify relevant articles, using search terms selected according to the key questions. Evidence-based recommendations were then created on the basis of these articles. An external expert review and Delphi questionnaire were applied to reach consensus regarding the recommendations. The resulting guidelines focus on the surgical treatment of laryngeal cancer with the assumption that surgery is the selected treatment modality after a multidisciplinary discussion in any context. These guidelines do not, therefore, address non-surgical treatment such as radiation therapy or chemotherapy. The committee developed 62 evidence-based recommendations in 32 categories intended to assist clinicians during management of patients with laryngeal cancer and patients with laryngeal cancer, and counselors and health policy-makers.
Advisory Committees ; Consensus ; Counseling ; Drug Therapy ; Glottis ; Humans ; Laryngeal Neoplasms* ; Neck*

OBJECTIVES: The aim of this study is to develop Treatment Guidelines for Alcohol Use Disorder-the Korean Addiction Treatment Guidelines-using the Adaptation method. METHODS AND PROCEDURES: As the Adaptation method of Guidelines, the ADAPTE Manual, which is developed by ADAPTE collaboration is applied. In Part 1 : preparatory work prior to the beginning of Adaptation is performed. In Part 2 : the core phase of the Adaptation in which we searched the guidelines and performed a systematic review of the literature. The two guidelines (of the AU and the UK) are selected by gone through the methodological quality and currency evaluation for guidelines. And, in order to choose the proper recommendations, the contents were evaluated with regard to the applicability and acceptability of guidelines to the domestic. Finally, in Part 3 : the adapted guideline is reviewed externally, with review and plan update. Therefore, in this way, the Korean Guidelines for Treatment of Alcohol Use Disorder, comprised of 55 recommendations, was developed. CONCLUSION: In this study, Korean Guidelines for Treatment of Alcohol Use Disorder were developed by application of the Adaptation method of ADAPTE. These are the first evidence-based Korean Guidelines for Treatment of Alcohol Use Disorder, which were modified and accepted in accordance with Korean circumstances within the range not damaging the validity of the treatment worth, and it is expected to contribute to improvement in quality and efficiency enhancement of Korean treatment of Alcohol Use Disorder.
Cooperative Behavior