Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Purpose: Several types of dermal fillers have been recently introduced and used for penile augmentation (PA). However, few studies have compared outcomes after the injection of different fillers. This study aimed to compare the clinical outcomes of hyaluronic acid (HLA), polylactic acid (PLA), and polymethyl methacrylate (PMA) filler injections, which are the most commonly used for aesthetic purposes. Materials and Methods: This prospective study was conducted for 24 weeks after a filler injection by a surgeon between March 2017 and December 2021. Healthy adult men complaining of small penis were enrolled. Penile girth, satisfaction, and injection-associated adverse events (AEs) were assessed at baseline and 4, 12, and 24 weeks after injection. Results: Of the 301 men who received filler injections, 125, 134, and 42 received HLA, PLA, and PMA fillers, respectively. The augmentation effect was in the order of PMA, HLA, and PLA, respectively, at 24 weeks (PMA vs. HLA, p<0.001; HLA vs. PLA, p=0.006). Satisfaction levels increased significantly at 24 weeks in all groups (each with p<0.001). However, the increase in satisfaction levels was smaller in the PMA group (PMA vs. HLA or PLA, p<0.05, for both penile appearance and sexual life). No serious or systemic AEs were recorded. Filler injection-associated local AEs in the HLA, PLA, and PMA groups occurred in 9 (7.2%), 16 (11.9%), and 6 (14.3%) men, respectively. There was no significant difference in AEs among the groups (p=0.299). Conclusions The augmentative effect was greater in the PMA group than in the HLA and PLA groups, whereas the increase in satisfaction levels was smaller in the PMA group. Our study demonstrated the clinical course of different types of fillers and suggests that the filler type should be selected after detailed counseling considering individual characteristics and preferences.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Purpose: Several types of dermal fillers have been recently introduced and used for penile augmentation (PA). However, few studies have compared outcomes after the injection of different fillers. This study aimed to compare the clinical outcomes of hyaluronic acid (HLA), polylactic acid (PLA), and polymethyl methacrylate (PMA) filler injections, which are the most commonly used for aesthetic purposes. Materials and Methods: This prospective study was conducted for 24 weeks after a filler injection by a surgeon between March 2017 and December 2021. Healthy adult men complaining of small penis were enrolled. Penile girth, satisfaction, and injection-associated adverse events (AEs) were assessed at baseline and 4, 12, and 24 weeks after injection. Results: Of the 301 men who received filler injections, 125, 134, and 42 received HLA, PLA, and PMA fillers, respectively. The augmentation effect was in the order of PMA, HLA, and PLA, respectively, at 24 weeks (PMA vs. HLA, p<0.001; HLA vs. PLA, p=0.006). Satisfaction levels increased significantly at 24 weeks in all groups (each with p<0.001). However, the increase in satisfaction levels was smaller in the PMA group (PMA vs. HLA or PLA, p<0.05, for both penile appearance and sexual life). No serious or systemic AEs were recorded. Filler injection-associated local AEs in the HLA, PLA, and PMA groups occurred in 9 (7.2%), 16 (11.9%), and 6 (14.3%) men, respectively. There was no significant difference in AEs among the groups (p=0.299). Conclusions The augmentative effect was greater in the PMA group than in the HLA and PLA groups, whereas the increase in satisfaction levels was smaller in the PMA group. Our study demonstrated the clinical course of different types of fillers and suggests that the filler type should be selected after detailed counseling considering individual characteristics and preferences.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Purpose: Several types of dermal fillers have been recently introduced and used for penile augmentation (PA). However, few studies have compared outcomes after the injection of different fillers. This study aimed to compare the clinical outcomes of hyaluronic acid (HLA), polylactic acid (PLA), and polymethyl methacrylate (PMA) filler injections, which are the most commonly used for aesthetic purposes. Materials and Methods: This prospective study was conducted for 24 weeks after a filler injection by a surgeon between March 2017 and December 2021. Healthy adult men complaining of small penis were enrolled. Penile girth, satisfaction, and injection-associated adverse events (AEs) were assessed at baseline and 4, 12, and 24 weeks after injection. Results: Of the 301 men who received filler injections, 125, 134, and 42 received HLA, PLA, and PMA fillers, respectively. The augmentation effect was in the order of PMA, HLA, and PLA, respectively, at 24 weeks (PMA vs. HLA, p<0.001; HLA vs. PLA, p=0.006). Satisfaction levels increased significantly at 24 weeks in all groups (each with p<0.001). However, the increase in satisfaction levels was smaller in the PMA group (PMA vs. HLA or PLA, p<0.05, for both penile appearance and sexual life). No serious or systemic AEs were recorded. Filler injection-associated local AEs in the HLA, PLA, and PMA groups occurred in 9 (7.2%), 16 (11.9%), and 6 (14.3%) men, respectively. There was no significant difference in AEs among the groups (p=0.299). Conclusions The augmentative effect was greater in the PMA group than in the HLA and PLA groups, whereas the increase in satisfaction levels was smaller in the PMA group. Our study demonstrated the clinical course of different types of fillers and suggests that the filler type should be selected after detailed counseling considering individual characteristics and preferences.

Background: With growing elderly populations, management of patients with acute stroke is increasingly important. In South Korea, the Acute Stroke Quality Assessment Program (ASQAP) has contributed to improving the quality of stroke care and practice behavior in healthcare institutions. While the mortality of hemorrhagic stroke remains high, there are only a few assessment indices associated with hemorrhagic stroke. Considering the need to develop assessment indices to improve the actual quality of care in the field of acute stroke treatment, this study aims to investigate the current status of human resources and practices related to the treatment of patients with acute stroke through a nationwide survey. Methods: For the healthcare institutions included in the Ninth ASQAP of the Health Insurance Review and Assessment Service (HIRA), data from January 2022 to December 2022 were collected through a survey on the current status and practice of healthcare providers related to the treatment of patients with acute stroke. The questionnaire consisted of 19 items, including six items on healthcare providers involved in stroke care and 10 items on the care of patients with acute stroke. Results: In the treatment of patients with hemorrhagic stroke among patients with acute stroke, neurosurgeons were the most common providers. The contribution of neurosurgeons in the treatment of ischemic stroke has also been found to be equivalent to that of neurologists. However, a number of institutions were found to be devoid of healthcare providers who perform definitive treatments, such as intra-arterial thrombectomy for patients with ischemic stroke or cerebral aneurysm clipping for subarachnoid hemorrhage. The intensity of the workload of healthcare providers involved in the care of patients with acute stroke, especially those involved in definitive treatment, was also found to be quite high. Conclusion Currently, there are almost no assessment indices specific to hemorrhagic stroke in the ASQAP for acute stroke. Furthermore, it does not reflect the reality of the healthcare providers and practices that provide definitive treatment for acute stroke. The findings of this study suggest the need for the development of appropriate assessment indices that reflect the realities of acute stroke care.

Background: With growing elderly populations, management of patients with acute stroke is increasingly important. In South Korea, the Acute Stroke Quality Assessment Program (ASQAP) has contributed to improving the quality of stroke care and practice behavior in healthcare institutions. While the mortality of hemorrhagic stroke remains high, there are only a few assessment indices associated with hemorrhagic stroke. Considering the need to develop assessment indices to improve the actual quality of care in the field of acute stroke treatment, this study aims to investigate the current status of human resources and practices related to the treatment of patients with acute stroke through a nationwide survey. Methods: For the healthcare institutions included in the Ninth ASQAP of the Health Insurance Review and Assessment Service (HIRA), data from January 2022 to December 2022 were collected through a survey on the current status and practice of healthcare providers related to the treatment of patients with acute stroke. The questionnaire consisted of 19 items, including six items on healthcare providers involved in stroke care and 10 items on the care of patients with acute stroke. Results: In the treatment of patients with hemorrhagic stroke among patients with acute stroke, neurosurgeons were the most common providers. The contribution of neurosurgeons in the treatment of ischemic stroke has also been found to be equivalent to that of neurologists. However, a number of institutions were found to be devoid of healthcare providers who perform definitive treatments, such as intra-arterial thrombectomy for patients with ischemic stroke or cerebral aneurysm clipping for subarachnoid hemorrhage. The intensity of the workload of healthcare providers involved in the care of patients with acute stroke, especially those involved in definitive treatment, was also found to be quite high. Conclusion Currently, there are almost no assessment indices specific to hemorrhagic stroke in the ASQAP for acute stroke. Furthermore, it does not reflect the reality of the healthcare providers and practices that provide definitive treatment for acute stroke. The findings of this study suggest the need for the development of appropriate assessment indices that reflect the realities of acute stroke care.

Background: With growing elderly populations, management of patients with acute stroke is increasingly important. In South Korea, the Acute Stroke Quality Assessment Program (ASQAP) has contributed to improving the quality of stroke care and practice behavior in healthcare institutions. While the mortality of hemorrhagic stroke remains high, there are only a few assessment indices associated with hemorrhagic stroke. Considering the need to develop assessment indices to improve the actual quality of care in the field of acute stroke treatment, this study aims to investigate the current status of human resources and practices related to the treatment of patients with acute stroke through a nationwide survey. Methods: For the healthcare institutions included in the Ninth ASQAP of the Health Insurance Review and Assessment Service (HIRA), data from January 2022 to December 2022 were collected through a survey on the current status and practice of healthcare providers related to the treatment of patients with acute stroke. The questionnaire consisted of 19 items, including six items on healthcare providers involved in stroke care and 10 items on the care of patients with acute stroke. Results: In the treatment of patients with hemorrhagic stroke among patients with acute stroke, neurosurgeons were the most common providers. The contribution of neurosurgeons in the treatment of ischemic stroke has also been found to be equivalent to that of neurologists. However, a number of institutions were found to be devoid of healthcare providers who perform definitive treatments, such as intra-arterial thrombectomy for patients with ischemic stroke or cerebral aneurysm clipping for subarachnoid hemorrhage. The intensity of the workload of healthcare providers involved in the care of patients with acute stroke, especially those involved in definitive treatment, was also found to be quite high. Conclusion Currently, there are almost no assessment indices specific to hemorrhagic stroke in the ASQAP for acute stroke. Furthermore, it does not reflect the reality of the healthcare providers and practices that provide definitive treatment for acute stroke. The findings of this study suggest the need for the development of appropriate assessment indices that reflect the realities of acute stroke care.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.