Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Objectives: Smoking causes 8 million deaths annually and significant socioeconomic burdens. Despite several therapies, cessation rates remain low due to nicotine’s addictive properties and withdrawal symptoms. This study evaluates the efficacy of transcutaneous auricular vagus nerve stimulation (taVNS) in reducing nicotine dependence and improving psychological states. Methods: This pilot study enrolled 23 adult smokers aged 19 to 65 years with the Fagerström Test for Nicotine Dependence (FTND) scores over 4 and daily cigarette consumption exceeding 10. Participants were randomized into treatment and control groups. The treatment group received personalized taVNS stimulation targeting the auricular branch of the vagus nerve, applied for 30 minutes, three times daily, for four weeks. The control group received a low-level fixed-current stimulation. Outcomes, including the FTND, cigarettes per day (CPD), the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), and the Insomnia Severity Index (ISI), were assessed at baseline, 2 weeks, and 4 weeks. Statistical analyses included the Mann–Whitney U-test and Wilcoxon signed-rank test. Results: The treatment group showed significant reductions in the FTND (2.0 points at 2 weeks, p<0.05; 3.0 points at 4 weeks, p<0.05) and CPD (1.0 fewer cigarette at 2 weeks, p<0.05; 2.0 fewer cigarettes at 4 weeks, p<0.05). Additionally, the PHQ-9 scores decreased significantly in the treatment group (3.0 points at 2 weeks, p<0.05; no further improvement at 4 weeks). However, the GAD-7 and the ISI scores showed no statistically significant changes in either group. The control group exhibited slight improvements in the FTND and the CPD, possibly due to placebo effects or motivation induced by study participation. Conclusions This study highlights taVNS as a promising non-invasive treatment for smoking cessation, effective in reducing nicotine dependence and improving depressive symptoms. However, its effects on anxiety and sleep quality remain unclear. Larger studies are needed to confirm its efficacy and explore optimal parameters and underlying mechanisms.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Objectives: Smoking causes 8 million deaths annually and significant socioeconomic burdens. Despite several therapies, cessation rates remain low due to nicotine’s addictive properties and withdrawal symptoms. This study evaluates the efficacy of transcutaneous auricular vagus nerve stimulation (taVNS) in reducing nicotine dependence and improving psychological states. Methods: This pilot study enrolled 23 adult smokers aged 19 to 65 years with the Fagerström Test for Nicotine Dependence (FTND) scores over 4 and daily cigarette consumption exceeding 10. Participants were randomized into treatment and control groups. The treatment group received personalized taVNS stimulation targeting the auricular branch of the vagus nerve, applied for 30 minutes, three times daily, for four weeks. The control group received a low-level fixed-current stimulation. Outcomes, including the FTND, cigarettes per day (CPD), the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), and the Insomnia Severity Index (ISI), were assessed at baseline, 2 weeks, and 4 weeks. Statistical analyses included the Mann–Whitney U-test and Wilcoxon signed-rank test. Results: The treatment group showed significant reductions in the FTND (2.0 points at 2 weeks, p<0.05; 3.0 points at 4 weeks, p<0.05) and CPD (1.0 fewer cigarette at 2 weeks, p<0.05; 2.0 fewer cigarettes at 4 weeks, p<0.05). Additionally, the PHQ-9 scores decreased significantly in the treatment group (3.0 points at 2 weeks, p<0.05; no further improvement at 4 weeks). However, the GAD-7 and the ISI scores showed no statistically significant changes in either group. The control group exhibited slight improvements in the FTND and the CPD, possibly due to placebo effects or motivation induced by study participation. Conclusions This study highlights taVNS as a promising non-invasive treatment for smoking cessation, effective in reducing nicotine dependence and improving depressive symptoms. However, its effects on anxiety and sleep quality remain unclear. Larger studies are needed to confirm its efficacy and explore optimal parameters and underlying mechanisms.

Objectives: To evaluate the factors that influence the survival of dental implants and marginal bone loss (MBL) in patients taking osteoporosis or osteopenia medication. Materials and Methods: This study included patients who underwent dental implant treatment after taking medication for osteoporosis or osteopenia. Electronic medical records were used to collect data of patient age, sex, age at osteoporosis or osteopenia diagnosis, types of medications, age at medication initiation, duration of medication before implant surgery, whether the medication was paused before surgery, paused duration of medication, implant survival status, and MBL before and after prosthetic treatment. Firth’s logistic regression was used to analyze the relationships between each variable and implant survival as well as between MBL before and after prosthetic treatment. Results: Of the 267 patients, 111 with 209 implants were included in the study. The mean observation period was 57.9 months. The survival rate was 92.8% at the patient level and 96.2% at the implant level. No significant associations were found between implant survival and any of the variablesexamined. MBL before prosthetic treatment was significantly associated with use of receptor activator of nuclear factor-κB ligand (RANKL) inhibitors(P=0.032) and bone formation stimulators (P=0.022). Comparing the concurrent and single use of bisphosphonates and RANKL inhibitors, only the use of RANKL inhibitors alone was significantly associated with MBL before prosthetic treatment (P=0.039). MBL after prosthetic treatment was significantly associated with injection method among the routes of drug administration (P=0.011). Conclusion The implant survival rate in patients undergoing medical treatment for osteoporosis or osteopenia was comparable to the general implant survival rate. MBL before prosthetic treatment was associated with type of anti-osteoporotic medication, whereas MBL after prosthetic treatment was correlated with drug administration route. Further studies with larger sample sizes are required.