1.New-Onset Neuromyelitis Optica Spectrum Disorder during Pregnancy: A Case Report
So Hee LEE ; Seongheon KIM ; Se Jin LEE ; Sung Hun KIM ; Sunghun NA
Perinatology 2025;36(1):32-36
Neuromyelitis optica spectrum disorder (NMOSD) is a rare inflammatory disease that most often affects the optic nerves and spinal cord. We describe a case of 36-year-old woman presented at 13 weeks of gestation with 4 extremities paresthesia and weakness that had lasted for two months at her first visit to our hospital. She had two previous uncomplicated full-term vaginal deliveries and no significant medical or family history. Spine magnetic resonance imaging (MRI) showed extensive cervical cord lesion and aquaporin-4 antibodies were strongly positive, confirming the diagnosis of NMOSD. Initial management with high-dose corticosteroids and plasmapheresis was done and she showed substantial improvement, but she revisited hospital at 26 weeks of gestational age due to visual disturbance and aggravated weakness. Relapse of NMOSD was confirmed by spine MRI, so rituximab therapy was initiated at 28 weeks of gestational age for prevention of recurrence.The patient showed clinical improvement with no adverse effects and relapse of symptoms. She successfully delivered a healthy male infant at 39 weeks and 3 days of gestational age through uncomplicated vaginal delivery. This case demonstrates successful management of new-onset NMOSD during pregnancy using a multi-modal treatment approach including rituximab.
2.New-Onset Neuromyelitis Optica Spectrum Disorder during Pregnancy: A Case Report
So Hee LEE ; Seongheon KIM ; Se Jin LEE ; Sung Hun KIM ; Sunghun NA
Perinatology 2025;36(1):32-36
Neuromyelitis optica spectrum disorder (NMOSD) is a rare inflammatory disease that most often affects the optic nerves and spinal cord. We describe a case of 36-year-old woman presented at 13 weeks of gestation with 4 extremities paresthesia and weakness that had lasted for two months at her first visit to our hospital. She had two previous uncomplicated full-term vaginal deliveries and no significant medical or family history. Spine magnetic resonance imaging (MRI) showed extensive cervical cord lesion and aquaporin-4 antibodies were strongly positive, confirming the diagnosis of NMOSD. Initial management with high-dose corticosteroids and plasmapheresis was done and she showed substantial improvement, but she revisited hospital at 26 weeks of gestational age due to visual disturbance and aggravated weakness. Relapse of NMOSD was confirmed by spine MRI, so rituximab therapy was initiated at 28 weeks of gestational age for prevention of recurrence.The patient showed clinical improvement with no adverse effects and relapse of symptoms. She successfully delivered a healthy male infant at 39 weeks and 3 days of gestational age through uncomplicated vaginal delivery. This case demonstrates successful management of new-onset NMOSD during pregnancy using a multi-modal treatment approach including rituximab.
3.New-Onset Neuromyelitis Optica Spectrum Disorder during Pregnancy: A Case Report
So Hee LEE ; Seongheon KIM ; Se Jin LEE ; Sung Hun KIM ; Sunghun NA
Perinatology 2025;36(1):32-36
Neuromyelitis optica spectrum disorder (NMOSD) is a rare inflammatory disease that most often affects the optic nerves and spinal cord. We describe a case of 36-year-old woman presented at 13 weeks of gestation with 4 extremities paresthesia and weakness that had lasted for two months at her first visit to our hospital. She had two previous uncomplicated full-term vaginal deliveries and no significant medical or family history. Spine magnetic resonance imaging (MRI) showed extensive cervical cord lesion and aquaporin-4 antibodies were strongly positive, confirming the diagnosis of NMOSD. Initial management with high-dose corticosteroids and plasmapheresis was done and she showed substantial improvement, but she revisited hospital at 26 weeks of gestational age due to visual disturbance and aggravated weakness. Relapse of NMOSD was confirmed by spine MRI, so rituximab therapy was initiated at 28 weeks of gestational age for prevention of recurrence.The patient showed clinical improvement with no adverse effects and relapse of symptoms. She successfully delivered a healthy male infant at 39 weeks and 3 days of gestational age through uncomplicated vaginal delivery. This case demonstrates successful management of new-onset NMOSD during pregnancy using a multi-modal treatment approach including rituximab.
4.New-Onset Neuromyelitis Optica Spectrum Disorder during Pregnancy: A Case Report
So Hee LEE ; Seongheon KIM ; Se Jin LEE ; Sung Hun KIM ; Sunghun NA
Perinatology 2025;36(1):32-36
Neuromyelitis optica spectrum disorder (NMOSD) is a rare inflammatory disease that most often affects the optic nerves and spinal cord. We describe a case of 36-year-old woman presented at 13 weeks of gestation with 4 extremities paresthesia and weakness that had lasted for two months at her first visit to our hospital. She had two previous uncomplicated full-term vaginal deliveries and no significant medical or family history. Spine magnetic resonance imaging (MRI) showed extensive cervical cord lesion and aquaporin-4 antibodies were strongly positive, confirming the diagnosis of NMOSD. Initial management with high-dose corticosteroids and plasmapheresis was done and she showed substantial improvement, but she revisited hospital at 26 weeks of gestational age due to visual disturbance and aggravated weakness. Relapse of NMOSD was confirmed by spine MRI, so rituximab therapy was initiated at 28 weeks of gestational age for prevention of recurrence.The patient showed clinical improvement with no adverse effects and relapse of symptoms. She successfully delivered a healthy male infant at 39 weeks and 3 days of gestational age through uncomplicated vaginal delivery. This case demonstrates successful management of new-onset NMOSD during pregnancy using a multi-modal treatment approach including rituximab.
5.New-Onset Neuromyelitis Optica Spectrum Disorder during Pregnancy: A Case Report
So Hee LEE ; Seongheon KIM ; Se Jin LEE ; Sung Hun KIM ; Sunghun NA
Perinatology 2025;36(1):32-36
Neuromyelitis optica spectrum disorder (NMOSD) is a rare inflammatory disease that most often affects the optic nerves and spinal cord. We describe a case of 36-year-old woman presented at 13 weeks of gestation with 4 extremities paresthesia and weakness that had lasted for two months at her first visit to our hospital. She had two previous uncomplicated full-term vaginal deliveries and no significant medical or family history. Spine magnetic resonance imaging (MRI) showed extensive cervical cord lesion and aquaporin-4 antibodies were strongly positive, confirming the diagnosis of NMOSD. Initial management with high-dose corticosteroids and plasmapheresis was done and she showed substantial improvement, but she revisited hospital at 26 weeks of gestational age due to visual disturbance and aggravated weakness. Relapse of NMOSD was confirmed by spine MRI, so rituximab therapy was initiated at 28 weeks of gestational age for prevention of recurrence.The patient showed clinical improvement with no adverse effects and relapse of symptoms. She successfully delivered a healthy male infant at 39 weeks and 3 days of gestational age through uncomplicated vaginal delivery. This case demonstrates successful management of new-onset NMOSD during pregnancy using a multi-modal treatment approach including rituximab.
6.Feasibility of a deep learning artificial intelligence model for the diagnosis of pediatric ileocolic intussusception with grayscale ultrasonography
Se Woo KIM ; Jung-Eun CHEON ; Young Hun CHOI ; Jae-Yeon HWANG ; Su-Mi SHIN ; Yeon Jin CHO ; Seunghyun LEE ; Seul Bi LEE
Ultrasonography 2024;43(1):57-67
Purpose:
This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images.
Methods:
This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS.
Results:
The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision.
Conclusion
The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.
7.Feasibility of a deep learning artificial intelligence model for the diagnosis of pediatric ileocolic intussusception with grayscale ultrasonography
Se Woo KIM ; Jung-Eun CHEON ; Young Hun CHOI ; Jae-Yeon HWANG ; Su-Mi SHIN ; Yeon Jin CHO ; Seunghyun LEE ; Seul Bi LEE
Ultrasonography 2024;43(1):57-67
Purpose:
This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images.
Methods:
This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS.
Results:
The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision.
Conclusion
The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.
8.Feasibility of a deep learning artificial intelligence model for the diagnosis of pediatric ileocolic intussusception with grayscale ultrasonography
Se Woo KIM ; Jung-Eun CHEON ; Young Hun CHOI ; Jae-Yeon HWANG ; Su-Mi SHIN ; Yeon Jin CHO ; Seunghyun LEE ; Seul Bi LEE
Ultrasonography 2024;43(1):57-67
Purpose:
This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images.
Methods:
This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS.
Results:
The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision.
Conclusion
The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.
9.Characteristics of High-Risk Groups for Suicide in Korea Before and After the COVID-19 Pandemic: K-COMPASS Cohort Study
Jeong Hun YANG ; Dae Hun KANG ; C. Hyung Keun PARK ; Min Ji KIM ; Sang Jin RHEE ; Min-Hyuk KIM ; Jinhee LEE ; Sang Yeol LEE ; Won Sub KANG ; Seong-Jin CHO ; Shin Gyeom KIM ; Se-Hoon SHIM ; Jung-Joon MOON ; Jieun YOO ; Weon-Young LEE ; Yong Min AHN
Journal of Korean Neuropsychiatric Association 2024;63(4):246-259
Objectives:
This study examined the changes in the characteristics of high-risk suicide groups in South Korea before and after the COVID-19 pandemic using the Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior (K-COMPASS) cohort.
Methods:
The K-COMPASS is a longitudinal cohort study that started in 2015. The participants included suicide attempters and individuals with suicidal ideation from various hospitals and mental health centers in South Korea. This study compared the sociodemographic and psychiatric characteristics of 800 participants from the first cohort (2015–2019) with 511 participants from the second and third cohorts (2019–2024). Data were collected through structured interviews and validated scales.
Results:
The second and third cohort participants were younger, had a higher proportion of females, and exhibited more severe psychiatric symptoms and higher suicidal risk than the first cohort. The prevalence of physical illnesses decreased, while the use of psychiatric medications and the severity of mental health issues increased. In addition, significant sociodemographic changes were observed, such as higher educational levels and urban residency.
Conclusion
Significant shifts in the characteristics of high-risk suicide groups were observed during the COVID-19 pandemic, highlighting the need for targeted mental health interventions focusing on younger individuals and females to prevent suicide in high-risk groups.
10.Characteristics of High-Risk Groups for Suicide in Korea Before and After the COVID-19 Pandemic: K-COMPASS Cohort Study
Jeong Hun YANG ; Dae Hun KANG ; C. Hyung Keun PARK ; Min Ji KIM ; Sang Jin RHEE ; Min-Hyuk KIM ; Jinhee LEE ; Sang Yeol LEE ; Won Sub KANG ; Seong-Jin CHO ; Shin Gyeom KIM ; Se-Hoon SHIM ; Jung-Joon MOON ; Jieun YOO ; Weon-Young LEE ; Yong Min AHN
Journal of Korean Neuropsychiatric Association 2024;63(4):246-259
Objectives:
This study examined the changes in the characteristics of high-risk suicide groups in South Korea before and after the COVID-19 pandemic using the Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior (K-COMPASS) cohort.
Methods:
The K-COMPASS is a longitudinal cohort study that started in 2015. The participants included suicide attempters and individuals with suicidal ideation from various hospitals and mental health centers in South Korea. This study compared the sociodemographic and psychiatric characteristics of 800 participants from the first cohort (2015–2019) with 511 participants from the second and third cohorts (2019–2024). Data were collected through structured interviews and validated scales.
Results:
The second and third cohort participants were younger, had a higher proportion of females, and exhibited more severe psychiatric symptoms and higher suicidal risk than the first cohort. The prevalence of physical illnesses decreased, while the use of psychiatric medications and the severity of mental health issues increased. In addition, significant sociodemographic changes were observed, such as higher educational levels and urban residency.
Conclusion
Significant shifts in the characteristics of high-risk suicide groups were observed during the COVID-19 pandemic, highlighting the need for targeted mental health interventions focusing on younger individuals and females to prevent suicide in high-risk groups.

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