• Korean Circulation Journal
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• v.52 no.9
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• pp.699-711
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• 2022

Background and Objectives: We investigated whether extra-pulmonary vein (PV) ablation targeting a high maximal slope of the action potential duration restitution curve (Smax) improves the rhythm outcome of persistent atrial fibrillation (PeAF) ablation. Methods: In this open-label, multi-center, randomized, and controlled trial, 178 PeAF patients were randomized with 1:1 ratio to computational modeling-guided virtual Smax ablation (V-Smax) or empirical ablation (E-ABL) groups. Smax maps were generated by computational modeling based on atrial substrate maps acquired during clinical procedures in sinus rhythm. Smax maps were generated during the clinical PV isolation (PVI). The V-Smax group underwent an additional extra-PV ablation after PVI targeting the virtual high Smax sites. Results: After a mean follow-up period of 12.3±5.2 months, the clinical recurrence rates (25.6% vs. 23.9% in the V-Smax and the E-ABL group, p=0.880) or recurrence appearing as atrial tachycardia (11.1% vs. 5.7%, p=0.169) did not differ between the 2 groups. The post-ablation cardioversion rate was higher in the V-Smax group than E-ABL group (14.4% vs. 5.7%, p=0.027). Among antiarrhythmic drug-free patients (n=129), the AF freedom rate was 78.7% in the V-Smax group and 80.9% in the E-ABL group (p=0.776). The total procedure time was longer in the V-Smax group (p=0.008), but no significant difference was found in the major complication rates (p=0.497) between the groups. Conclusions: Unlike a dominant frequency ablation, the computational modeling-guided V-Smax ablation did not improve the rhythm outcome of the PeAF ablation and had a longer procedure time.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.10
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• pp.927-932
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• 2013

The intracranial aneurysm model is extracted based on the Computed Tomography (CT) scan images. Computational fluid dynamics simulations were conducted under both steady and realistic flow conditions in ANSYS-FLUENT. The minimum wall shear stress in the intracranial aneurysm tended to occur in the aneurysmal region. The magnitude of wall shear stress along inner wall of the curvature in the right M1 segment of middle cerebral artery is approximately 20 times higher than that along both the proximal and distal walls. However, the magnitudes of the wall shear stress at the aneurysm region were considerably low. The blood flow has the complex distribution in the aneurysmal region during the systolic period. Complex helical flow patterns are observed inside the aneurysm. Through an analysis of the hemodynamic characteristics, one may predict the rupture of the cerebral aneurysms.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.364-374
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• 2022

Health information systems (HIS) are facing security challenges on data privacy and confidentiality. These challenges are based on centralized system architecture creating a target for malicious attacks. Blockchain technology has emerged as a trending technology with the potential to improve data security. Despite the effectiveness of this technology, still HIS are suffering from a lack of data privacy and confidentiality. This paper presents a blockchain-based data storage security architecture integrated with an e-Health care system to improve its security. The study employed a qualitative research method where data were collected using interviews and document analysis. Execute-order-validate Fabric's storage security architecture was implemented through private data collection, which is the combination of the actual private data stored in a private state, and a hash of that private data to guarantee data privacy. The key findings of this research show that data privacy and confidentiality are attained through a private data policy. Network peers are decentralized with blockchain only for hash storage to avoid storage challenges. Cost-effectiveness is achieved through data storage within a database of a Hyperledger Fabric. The overall performance of Fabric is higher than Ethereum. Ethereum's low performance is due to its execute-validate architecture which has high computation power with transaction inconsistencies. E-Health care system administrators should be trained and engaged with blockchain architectural designs for health data storage security. Health policymakers should be aware of blockchain technology and make use of the findings. The scientific contribution of this study is based on; cost-effectiveness of secured data storage, the use of hashes of network data stored in each node, and low energy consumption of Fabric leading to high performance.

• Clinical Endoscopy
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• v.57 no.2
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• pp.217-225
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• 2024

Background/Aims: This study aims to compare polyp detection performance of "Deep-GI," a newly developed artificial intelligence (AI) model, to a previously validated AI model computer-aided polyp detection (CADe) using various false positive (FP) thresholds and determining the best threshold for each model. Methods: Colonoscopy videos were collected prospectively and reviewed by three expert endoscopists (gold standard), trainees, CADe (CAD EYE; Fujifilm Corp.), and Deep-GI. Polyp detection sensitivity (PDS), polyp miss rates (PMR), and false-positive alarm rates (FPR) were compared among the three groups using different FP thresholds for the duration of bounding boxes appearing on the screen. Results: In total, 170 colonoscopy videos were used in this study. Deep-GI showed the highest PDS (99.4% vs. 85.4% vs. 66.7%, p<0.01) and the lowest PMR (0.6% vs. 14.6% vs. 33.3%, p<0.01) when compared to CADe and trainees, respectively. Compared to CADe, Deep-GI demonstrated lower FPR at FP thresholds of ≥0.5 (12.1 vs. 22.4) and ≥1 second (4.4 vs. 6.8) (both p<0.05). However, when the threshold was raised to ≥1.5 seconds, the FPR became comparable (2 vs. 2.4, p=0.3), while the PMR increased from 2% to 10%. Conclusions: Compared to CADe, Deep-GI demonstrated a higher PDS with significantly lower FPR at ≥0.5- and ≥1-second thresholds. At the ≥1.5-second threshold, both systems showed comparable FPR with increased PMR.

• The Korean Journal of Physiology and Pharmacology
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• v.24 no.1
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• pp.89-99
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• 2020

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.

• International journal of thyroidology
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• v.10 no.1
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• pp.14-23
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• 2017

Background and Objectives: To evaluate and compare the diagnostic performances of grayscale ultrasound (US) and quantitative parameters obtained from texture analysis of grayscale US and elastography images in evaluating patients with diffuse thyroid disease (DTD). Materials and Methods: From September to December 2012, 113 patients (mean age, $43.4{\pm}10.7years$) who had undergone preoperative staging US and elastography were included in this study. Assessment of the thyroid parenchyma for the diagnosis of DTD was made if US features suggestive of DTD were present. Nine histogram parameters were obtained from the grayscale US and elastography images, from which 'grayscale index' and 'elastography index' were calculated. Diagnostic performances of grayscale US, texture analysis using grayscale US and elastography were calculated and compared. Results: Of the 113 patients, 85 (75.2%) patients were negative for DTD and 28 (24.8%) were positive for DTD on pathology. The presence of US features suggestive of DTD showed significantly higher rates of DTD on pathology, 60.7% to 8.2% (p<0.001). Specificity, accuracy, and positive predictive value was highest in US features, 91.8%, 84.1%, and 87.6%, respectively (all ps<0.05). Grayscale index showed higher sensitivity and negative predictive value (NPV) than US features. All diagnostic performances were higher for grayscale index than the elastography index. Area under the curve of US features was the highest, 0.762, but without significant differences to grayscale index or mean of elastography (all ps>0.05). Conclusion: Diagnostic performances were the highest for grayscale US features in diagnosis of DTD. Grayscale index may be used as a complementary tool to US features for improving sensitivity and NPV.

• Industrial Engineering and Management Systems
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• v.13 no.2
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• pp.193-202
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• 2014

Patients' satisfaction while receiving medical service is affected by whether or not their preferences can be met, including time and physician preference. Due to scarcity of medical resource in China, efficient use of available resources is urgently required. To guarantee the utilization ratio, the scheduling decisions are made after all booking information is received. Two integer models with different objectives are formulated separately, maximizing the degree of satisfaction and revenue. The optimal value of the two models can be considered as the bound of corresponding objectives. However, it is improper to implement any of the extreme policies. Because revenue is a key element to keep the hospital running and satisfaction degree is related to the hospital's reputation, neither the revenue nor the satisfaction can be missed. Therefore, hospitals should make a balance. An integrated model is developed to find out the tradeoff between the two objectives. The whole degree of mismatching that is related to patient satisfaction and other separate mismatching degree are considered. Through a computational study, it is concluded that based on the proposed model hospitals can make their decisions according to service requirement.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.33 no.12
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• pp.19-28
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• 2017

The purpose of this study is to propose a design methodology to build temporary isolation rooms when infectious diseases suddenly occur in a certain region, such as the case of MERS-Cov in South Korea in 2015. Although most big hospitals usually have isolation rooms, they are expensive and dangerous to run such facilities on normal and typical days. To deal with these problems in this research, shipping containers are chosen as devices used to build the temporary isolation rooms near the original hospital. To do so, firstly, a prototype for the temporary isolation room was designed with the three part modules. The first part is for the medical team; the second part including the isolation rooms is for patients; the third part is for medical selection rooms to test the specimens. Secondly, the plan was compared with the MERS-Cov infection control guidelines. Finally this prototype is applied into the Yong-in Yon-sei severance hospital and then evaluated through a CFD simulation using STAR-CCM+(ver.9.06) for checking infectious bacterium movement in this prototype. The result showed that the prototype is effectively safe for patients tested as negative, patients waiting to be tested, and the medical team.

• Journal of Korean Neurosurgical Society
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• v.65 no.3
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• pp.422-429
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• 2022

Objective : Extracranial supra-aortic dissections (ESADs) with severe stenosis, occlusion and/or pseudoaneurysm presents potential risk of stroke. Endovascular stenting to reconstruct non acute phase ESADs (NAP-ESADs) is an alternative to anticoagulant or antiplatelet therapy. However, its feasibility, safety and efficacy of stenting in NAP-ESADs is unclear. This study aims to investigate the long-term outcomes of the feasibility, safety and efficacy of stenting in NAP-ESADs. Methods : Seventy-four patients with 91 NAP-ESAD vessels with severe stenosis, occlusion and/or pseudoaneurysm presents potential risk of stroke who underwent stent remodeling were enrolled into this respective study from December 2008 to March 2020. Technical success rate, complications, clinical and angiographic results were harvested and analyzed. Results : Success rate of stent deployment was 99% (90/91) with no procedural mortality or morbidity. Transient ischemic attack occurred in three patients during operation (4.1%, 3/74). Asymptomatic embolisms of distal intracranial vessels were found in two patients (2.7%, 2/74). One hundred and forty-two stents deployed at 85 carotid (135 stents) and six vertebral (seven stents) vessels. Six stent types (Wingspan, 28/135, 20.7%; Solitaire, 10/135, 7.4%; Neuroform, 8/135, 5.9%; LVIS, 2/135, 1.5%; Precise, 75/135, 55.6%; Acculink, 12/135, 8.9%) were deployed at carotid arterial dissection while two types (Wingspan, 5/7, 71.4%; Solitaire 2/7, 28.6%) at vertebral arterial dissection. Digital subtracted angiography (56%, 51/91), computational tomography angiography (41.8%, 38/91) and high resolution magnetic resonance imaging (2.2%, 2/91) were adopted for follow up, with a mean time of 17.2±15.4 months (5-77). All patient modified Rankin Scale scores showed no increase at discharge or follow-up. Angiographically, dissections in 86 vessels in 69 patients (94.5%, 86/91) were completely reconstructed with only minor remnant dissections in four vessels in four patients (4.4%, 4/91). Severe re-stenosis in the stented segment required re-stenting in one patient (1.1%, 1/91). Conclusion : Stent remodeling technique provides feasible, safe and efficacious treatment of ESADs patients with severe stenosis, occlusion and/or pseudoaneurysm.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.869-879
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• 2020

Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.