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Article: Intravesical Bladder Treatment and Deep Learning Applications to Improve Irritative Voiding Symptoms Caused by Interstitial Cystitis: A Literature Review.

Cho, Yongwon / Youn, Seunghyun

2023 Volume 27, Issue Suppl 1, Page(s) S13–20

Abstract: Our comprehension of interstitial cystitis/painful bladder syndrome (IC/PBS) has evolved over time. The term painful bladder syndrome, preferred by the International Continence Society, is characterized as "a syndrome marked by suprapubic pain during ... ...

Abstract	Our comprehension of interstitial cystitis/painful bladder syndrome (IC/PBS) has evolved over time. The term painful bladder syndrome, preferred by the International Continence Society, is characterized as "a syndrome marked by suprapubic pain during bladder filling, alongside increased daytime and nighttime frequency, in the absence of any proven urinary infection or other pathology." The diagnosis of IC/PBS primarily relies on symptoms of urgency/frequency and bladder/pelvic pain. The exact pathogenesis of IC/PBS remains a mystery, but it is postulated to be multifactorial. Theories range from bladder urothelial abnormalities, mast cell degranulation in the bladder, bladder inflammation, to altered bladder innervation. Therapeutic strategies encompass patient education, dietary and lifestyle modifications, medication, intravesical therapy, and surgical intervention. This article delves into the diagnosis, treatment, and prognosis prediction of IC/PBS, presenting the latest research findings, artificial intelligence technology applications in diagnosing major diseases in IC/PBS, and emerging treatment alternatives.
Language	English
Publishing date	2023-05-31
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2584447-7
ISSN	2093-6931 ; 2093-4777
ISSN (online)	2093-6931
ISSN	2093-4777
DOI	10.5213/inj.2346106.053
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: General Overview of Artificial Intelligence for Interstitial Cystitis in Urology.

Cho, Yongwon / Park, Jong Mok / Youn, Seunghyun

International neurourology journal

2023 Volume 27, Issue Suppl 2, Page(s) S64–72

Abstract: Our understanding of interstitial cystitis/bladder pain syndrome (IC/BPS) has evolved over time. The diagnosis of IC/BPS is primarily based on symptoms such as urgency, frequency, and bladder or pelvic pain. While the exact causes of IC/BPS remain ... ...

Abstract	Our understanding of interstitial cystitis/bladder pain syndrome (IC/BPS) has evolved over time. The diagnosis of IC/BPS is primarily based on symptoms such as urgency, frequency, and bladder or pelvic pain. While the exact causes of IC/BPS remain unclear, it is thought to involve several factors, including abnormalities in the bladder's urothelium, mast cell degranulation within the bladder, inflammation of the bladder, and altered innervation of the bladder. Treatment options include patient education, dietary and lifestyle modifications, medications, intravesical therapy, and surgical interventions. This review article provides insights into IC/BPS, including aspects of treatment, prognosis prediction, and emerging therapeutic options. Additionally, it explores the application of deep learning for diagnosing major diseases associated with IC/BPS.
Language	English
Publishing date	2023-11-30
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2584447-7
ISSN	2093-6931 ; 2093-4777
ISSN (online)	2093-6931
ISSN	2093-4777
DOI	10.5213/inj.2346294.147
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Article ; Online: Downward adjustment of rehabilitation goals may facilitate post-stroke arm motor recovery.

Cho, Yongwon / Hamm, Jeremy M / Heckhausen, Jutta / Cramer, Steven C

Psychology & health

2023 , Page(s) 1–17

Abstract: Objective: ...

Abstract	Objective:
Language	English
Publishing date	2023-05-14
Publishing country	England
Document type	Journal Article
ZDB-ID	625255-2
ISSN	1476-8321 ; 0887-0446
ISSN (online)	1476-8321
ISSN	0887-0446
DOI	10.1080/08870446.2023.2211991
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Article: Tumor Heterogeneity of Breast Cancer Assessed with Computed Tomography Texture Analysis: Association with Disease-Free Survival and Clinicopathological Prognostic Factor.

Yoo, Hyeongyu / Cho, Kyu Ran / Song, Sung Eun / Cho, Yongwon / Jung, Seung Pil / Sung, Kihoon

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 23

Abstract: Breast cancer is a heterogeneous disease, and computed tomography texture analysis (CTTA), which reflects the tumor heterogeneity, may predict the prognosis. We investigated the usefulness of CTTA for the prediction of disease-free survival (DFS) and ... ...

Abstract	Breast cancer is a heterogeneous disease, and computed tomography texture analysis (CTTA), which reflects the tumor heterogeneity, may predict the prognosis. We investigated the usefulness of CTTA for the prediction of disease-free survival (DFS) and prognostic factors in patients with invasive breast cancer. A total of 256 consecutive women who underwent preoperative chest CT and surgery in our institution were included. The Cox proportional hazards model was used to determine the relationship between textural features and DFS. Logistic regression analysis was used to reveal the relationship between textural features and prognostic factors. Of 256 patients, 21 (8.2%) had disease recurrence over a median follow-up of 60 months. For the prediction of shorter DFS, higher histological grade (hazard ratio [HR], 6.12;
Language	English
Publishing date	2023-11-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13233569
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Article ; Online: Multimodal integration of neuroimaging and genetic data for the diagnosis of mood disorders based on computer vision models.

Lee, Seungeun / Cho, Yongwon / Ji, Yuyoung / Jeon, Minhyek / Kim, Aram / Ham, Byung-Joo / Joo, Yoonjung Yoonie

Journal of psychiatric research

2024 Volume 172, Page(s) 144–155

Abstract: Mood disorders, particularly major depressive disorder (MDD) and bipolar disorder (BD), are often underdiagnosed, leading to substantial morbidity. Harnessing the potential of emerging methodologies, we propose a novel multimodal fusion approach that ... ...

Abstract	Mood disorders, particularly major depressive disorder (MDD) and bipolar disorder (BD), are often underdiagnosed, leading to substantial morbidity. Harnessing the potential of emerging methodologies, we propose a novel multimodal fusion approach that integrates patient-oriented brain structural magnetic resonance imaging (sMRI) scans with DNA whole-exome sequencing (WES) data. Multimodal data fusion aims to improve the detection of mood disorders by employing established deep-learning architectures for computer vision and machine-learning strategies. We analyzed brain imaging genetic data of 321 East Asian individuals, including 147 patients with MDD, 78 patients with BD, and 96 healthy controls. We developed and evaluated six fusion models by leveraging common computer vision models in image classification: Vision Transformer (ViT), Inception-V3, and ResNet50, in conjunction with advanced machine-learning techniques (XGBoost and LightGBM) known for high-dimensional data analysis. Model validation was performed using a 10-fold cross-validation. Our ViT ⊕ XGBoost fusion model with MRI scans, genomic Single Nucleotide polymorphism (SNP) data, and unweighted polygenic risk score (PRS) outperformed baseline models, achieving an incremental area under the curve (AUC) of 0.2162 (32.03% increase) and 0.0675 (+8.19%) and incremental accuracy of 0.1455 (+25.14%) and 0.0849 (+13.28%) compared to SNP-only and image-only baseline models, respectively. Our findings highlight the opportunity to refine mood disorder diagnostics by demonstrating the transformative potential of integrating diverse, yet complementary, data modalities and methodologies.
MeSH term(s)	Humans ; Mood Disorders/diagnostic imaging ; Mood Disorders/genetics ; Mood Disorders/pathology ; Depressive Disorder, Major/genetics ; Bipolar Disorder/diagnostic imaging ; Bipolar Disorder/genetics ; Brain/pathology ; Neuroimaging/methods ; Magnetic Resonance Imaging/methods
Language	English
Publishing date	2024-02-15
Publishing country	England
Document type	Journal Article
ZDB-ID	3148-3
ISSN	1879-1379 ; 0022-3956
ISSN (online)	1879-1379
ISSN	0022-3956
DOI	10.1016/j.jpsychires.2024.02.036
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Article: Prediction of Mortality after Burn Surgery in Critically Ill Burn Patients Using Machine Learning Models.

Park, Ji Hyun / Cho, Yongwon / Shin, Donghyeok / Choi, Seong-Soo

Journal of personalized medicine

2022 Volume 12, Issue 8

Abstract: Severe burns may lead to a series of pathophysiological processes that result in death. Machine learning models that demonstrate prognostic performance can be used to build analytical models to predict postoperative mortality. This study aimed to ... ...

Abstract	Severe burns may lead to a series of pathophysiological processes that result in death. Machine learning models that demonstrate prognostic performance can be used to build analytical models to predict postoperative mortality. This study aimed to identify machine learning models with the best diagnostic performance for predicting mortality in critically ill burn patients after burn surgery, and then compare them. Clinically important features for predicting mortality in patients after burn surgery were selected using a random forest (RF) regressor. The area under the receiver operating characteristic curve (AUC) and classifier accuracy were evaluated to compare the predictive accuracy of different machine learning algorithms, including RF, adaptive boosting, decision tree, linear support vector machine, and logistic regression. A total of 731 patients met the inclusion and exclusion criteria. The 90-day mortality of the critically ill burn patients after burn surgery was 27.1% (198/731). RF showed the highest AUC (0.922, 95% confidence interval = 0.902-0.942) among the models, with sensitivity and specificity of 66.2% and 93.8%, respectively. The most significant predictors for mortality after burn surgery as per machine learning models were total body surface area burned, red cell distribution width, and age. The RF algorithm showed the best performance for predicting mortality.
Language	English
Publishing date	2022-08-06
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662248-8
ISSN	2075-4426
ISSN	2075-4426
DOI	10.3390/jpm12081293
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Article ; Online: The role of goal adjustment during rehabilitation from stroke.

Cho, Yongwon / Hamm, Jeremy M / Heckhausen, Jutta / Cramer, Steven C

Applied psychology. Health and well-being

2021 Volume 14, Issue 1, Page(s) 26–43

Abstract: We investigated motivational regulation involving adjustment of recovery goals in post-stroke rehabilitation via standard in-clinic physiotherapy and in-home telerehabilitation (TR). We used a secondary dataset collected at 11 US sites as part of a ... ...

Abstract	We investigated motivational regulation involving adjustment of recovery goals in post-stroke rehabilitation via standard in-clinic physiotherapy and in-home telerehabilitation (TR). We used a secondary dataset collected at 11 US sites as part of a clinical trial using video games and game control pads designed to induce certain arm movements required for recovery (n = 124; M
MeSH term(s)	Goals ; Humans ; Middle Aged ; Motivation ; Stroke/therapy ; Stroke Rehabilitation/methods ; Telerehabilitation/methods
Language	English
Publishing date	2021-06-14
Publishing country	England
Document type	Journal Article ; Randomized Controlled Trial
ZDB-ID	2483053-7
ISSN	1758-0854 ; 1758-0846
ISSN (online)	1758-0854
ISSN	1758-0846
DOI	10.1111/aphw.12288
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Article: The effect of metabolically obese but normal weight on the labor market in South Korea

Cho, Yongwon

KIET industrial economic review Vol. 19, No. 3 , p. 26-42

2014 Volume 19, Issue 3, Page(s) 26–42

Author's details	Yongwon Cho
Language	English
Size	graph. Darst.
Publishing place	Seoul
Document type	Article
ZDB-ID	2024189-6
ISSN	1598-947X
Database	ECONomics Information System

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Article ; Online: Synthetic Time of Flight Magnetic Resonance Angiography Generation Model Based on Cycle-Consistent Generative Adversarial Network Using PETRA-MRA in the Patients With Treated Intracranial Aneurysm.

You, Sung-Hye / Cho, Yongwon / Kim, Byungjun / Yang, Kyung-Sook / Kim, Bo Kyu / Park, Sang Eun

Journal of magnetic resonance imaging : JMRI

2022 Volume 56, Issue 5, Page(s) 1513–1528

Abstract: Background: Pointwise encoding time reduction with radial acquisition (PETRA) magnetic resonance angiography (MRA) is useful for evaluating intracranial aneurysm recurrence, but the problem of severe background noise and low peripheral signal-to-noise ... ...

Abstract	Background: Pointwise encoding time reduction with radial acquisition (PETRA) magnetic resonance angiography (MRA) is useful for evaluating intracranial aneurysm recurrence, but the problem of severe background noise and low peripheral signal-to-noise ratio (SNR) remain. Deep learning could reduce noise using high- and low-quality images. Purpose: To develop a cycle-consistent generative adversarial network (cycleGAN)-based deep learning model to generate synthetic TOF (synTOF) using PETRA. Study type: Retrospective. Population: A total of 377 patients (mean age: 60 ± 11; 293 females) with treated intracranial aneurysms who underwent both PETRA and TOF from October 2017 to January 2021. Data were randomly divided into training (49.9%, 188/377) and validation (50.1%, 189/377) groups. Field strength/sequence: Ultra-short echo time and TOF-MRA on a 3-T MR system. Assessment: For the cycleGAN model, the peak SNR (PSNR) and structural similarity (SSIM) were evaluated. Image quality was compared qualitatively (5-point Likert scale) and quantitatively (SNR). A multireader diagnostic optimality evaluation was performed with 17 radiologists (experience of 1-18 years). Statistical tests: Generalized estimating equation analysis, Friedman's test, McNemar test, and Spearman's rank correlation. P < 0.05 indicated statistical significance. Results: The PSNR and SSIM between synTOF and TOF were 17.51 [16.76; 18.31] dB and 0.71 ± 0.02. The median values of overall image quality, noise, sharpness, and vascular conspicuity were significantly higher for synTOF than for PETRA (4.00 [4.00; 5.00] vs. 4.00 [3.00; 4.00]; 5.00 [4.00; 5.00] vs. 3.00 [2.00; 4.00]; 4.00 [4.00; 4.00] vs. 4.00 [3.00; 4.00]; 3.00 [3.00; 4.00] vs. 3.00 [2.00; 3.00]). The SNRs of the middle cerebral arteries were the highest for synTOF (synTOF vs. TOF vs. PETRA; 63.67 [43.25; 105.00] vs. 52.42 [32.88; 74.67] vs. 21.05 [12.34; 37.88]). In the multireader evaluation, there was no significant difference in diagnostic optimality or preference between synTOF and TOF (19.00 [18.00; 19.00] vs. 20.00 [18.00; 20.00], P = 0.510; 8.00 [6.00; 11.00] vs. 11.00 [9.00, 14.00], P = 1.000). Data conclusion: The cycleGAN-based deep learning model provided synTOF free from background artifact. The synTOF could be a versatile alternative to TOF in patients who have undergone PETRA for evaluating treated aneurysms. Evidence level: 4 TECHNICAL EFFICACY: Stage 1.
MeSH term(s)	Aged ; Angiography, Digital Subtraction/methods ; Female ; Humans ; Intracranial Aneurysm/diagnostic imaging ; Magnetic Resonance Angiography/methods ; Middle Aged ; Retrospective Studies ; Signal-To-Noise Ratio
Language	English
Publishing date	2022-02-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	1146614-5
ISSN	1522-2586 ; 1053-1807
ISSN (online)	1522-2586
ISSN	1053-1807
DOI	10.1002/jmri.28114
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Zs.A 3648: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MO 236: Show issues

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Article: Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI.

Lee, Kyu-Chong / Cho, Yongwon / Ahn, Kyung-Sik / Park, Hyun-Joon / Kang, Young-Shin / Lee, Sungshin / Kim, Dongmin / Kang, Chang Ho

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 20

Abstract: This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) were utilized. Three ... ...

Abstract	This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) were utilized. Three musculoskeletal radiologists labeled the rotator cuff tear. The YOLO v8 rotator cuff tear detection model was then trained; training was performed with all imaging planes simultaneously and with axial, coronal, and sagittal images separately. The performances of the models were evaluated and compared using receiver operating curves and the area under the curve (AUC). The AUC was the highest when using all imaging planes (0.94;
Language	English
Publishing date	2023-10-19
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13203254
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