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- 저자 : Sunwoo Leonard (검색결과 2 건)
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Choi Hyunsu,Sunwoo Leonard,Cho Se Jin,Baik Sung Hyun,Bae Yun Jung,Choi Byung Se,Jung Cheolkyu,Kim Jae Hyoung 대한영상의학회 2023 Korean Journal of Radiology Vol.24 No.5
Objective: We aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. Materials and Methods: In April 2022, a 30-item online survey was conducted by neuroradiologists from the Korean Society of Neuroradiology (KSNR) to assess current user experiences, perceptions, attitudes, and future expectations regarding AI for neuro-applications. Respondents with experience in AI software were further investigated in terms of the number and type of software used, period of use, clinical usefulness, and future scope. Results were compared between respondents with and without experience with AI software through multivariable logistic regression and mediation analyses. Results: The survey was completed by 73 respondents, accounting for 21.9% (73/334) of the KSNR members; 72.6% (53/73) were familiar with AI and 58.9% (43/73) had used AI software, with approximately 86% (37/43) using 1–3 AI software programs and 51.2% (22/43) having up to one year of experience with AI software. Among AI software types, brain volumetry software was the most common (62.8% [27/43]). Although 52.1% (38/73) assumed that AI is currently useful in practice, 86.3% (63/73) expected it to be useful for clinical practice within 10 years. The main expected benefits were reducing the time spent on repetitive tasks (91.8% [67/73]) and improving reading accuracy and reducing errors (72.6% [53/73]). Those who experienced AI software were more familiar with AI (adjusted odds ratio, 7.1 [95% confidence interval, 1.81–27.81]; P = 0.005). More than half of the respondents with AI software experience (55.8% [24/43]) agreed that AI should be included in training curriculums, while almost all (95.3% [41/43]) believed that radiologists should coordinate to improve its performance. Conclusion: A majority of respondents experienced AI software and showed a proactive attitude toward adopting AI in clinical practice, suggesting that AI should be incorporated into training and active participation in AI development should be encouraged.
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Woo Ho Geol,Jung Cheolkyu,Sunwoo Leonard,Bae Yun Jung,최병세,Kim Jae Hyoung,Kim Beom Joon,Han Moon-Ku,Bae Hee-Joon,Jung Seunguk,차상훈 대한신경중재치료의학회 2019 Neurointervention Vol.14 No.2
Purpose: Although endovascular treatment is currently thought to only be suitable for patients who have pial arterial filling scores >3 as determined by multiphase computed tomography angiography (mpCTA), a cut-off score of 3 was determined by a study, including patients within 12 hours after symptom onset. We aimed to investigate whether a cut-off score of 3 for endovascular treatment within 6 hours of symptom onset is an appropriate predictor of good functional outcome at 3 months. Materials and Methods: From April 2015 to January 2016, acute ischemic stroke patients treated with mechanical thrombectomy within 6 hours of symptom onset were enrolled into this study. Pial arterial filling scores were semi-quantitatively assessed using mpCTA, and clinical and radiological parameters were compared between patients with favorable and unfavorable outcomes. Multivariate logistic regression analysis was then performed to investigate the independent association between clinical outcome and pial collateral score, with the predictive power of the latter assessed using C-statistics. Results: Of the 38 patients enrolled, 20 (52.6%) had a favorable outcome and 18 had an unfavorable outcome, with the latter group showing a lower mean pial arterial filling score (3.6±0.8 vs. 2.4±1.2, P=0.002). After adjusting for variables with a P-value of <0.1 in univariate analysis (i.e., age and National Institutes of Health Stroke Scale score at admission), pial arterial filling scores higher than a cut-off of 2 were found to be independently associated with favorable clinical outcomes (P=0.012). C-statistic analysis confirmed that our model had the highest prediction power when pial arterial filling scores were dichotomized at >2 vs. ≤2. Conclusion: A pial arterial filling cut-off score of 2 as determined by mpCTA appears to be more suitable for predicting clinical outcomes following endovascular treatment within 6 hours of symptom onset than the cut-off of 3 that had been previously suggested.
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