Automated detection of steno-occlusive lesion on time-of-flight magnetic resonance angiography: an observer performance study.

BACKGROUND AND PURPOSE: Intracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an artificial intelligence model for detecting steno-occlusive lesions in the intracranial arteries. MATERIALS AND METHODS: Overall, 138 TOF-MRA images were collected from two institutions, which served as internal (n = 62) and external (n = 76) test sets, respectively. Each study was reviewed by five radiologists (two neuroradiologists and three radiology residents) to compare the usage and non-usage of our proposed artificial intelligence model for TOF-MRA interpretation. They identified the steno-occlusive lesions and recorded their reading time. Observer performance was assessed using the area under the Jackknife free-response receiver operating characteristic curve and reading time for comparison. RESULTS: The average area under the Jackknife free-response receiver operating characteristic curve for the five radiologists demonstrated an improvement from 0.70 without artificial intelligence to 0.76 with artificial intelligence (P = .027). Notably, this improvement was most pronounced among the three radiology residents, whose performance metrics increased from 0.68 to 0.76 (P = .002). Despite an increased reading time upon using artificial intelligence, there was no significant change among the readings by radiology residents. Moreover, the use of artificial intelligence resulted in improved inter-observer agreement among the reviewers (the intraclass correlation coefficient increased from 0.734 to 0.752). CONCLUSIONS: Our proposed artificial intelligence model offers a supportive tool for radiologists, potentially enhancing the accuracy of detecting intracranial steno-occlusion lesions on TOF-MRA. Less-experienced readers may benefit the most from this model.ABBREVIATIONS: AI = Artificial intelligence; AUC = Area under the receiver operating characteristic curve; AUFROC = Area under the Jackknife free-response receiver operating characteristic curve; DL = Deep learning; ICC = Intraclass correlation coefficient; IRB = Institutional Review Boards; JAFROC = Jackknife free-response receiver operating characteristic.

Angiographic and clinical outcomes of various techniques of intracranial-to-intracranial bypasses for complex cases with a review of pertinent literature and illustrated cases.

OBJECTIVE: Determine the utility of intracranial-to-intracranial bypass (IIB) surgery for complex cases and bypass options. METHODS: Eighteen IIB cases were included. Each case was classified as IIB with interposition grafts and non-interposition grafts. Clinical and angiographical status were evaluated pre- and postoperatively and at the last follow-up. Angiographic images were analyzed and schematically drawn. Postoperative angiography was used to measure the bypass patency and the presence of postoperative cerebral infarction. Recipient artery occlusion time of each bypass was measured. RESULTS: 14 cases were complex intracranial aneurysms (IAs), 1 case was vertebrobasilar dolichoectasia, and 3 cases were intracranial arterial steno-occlusive disease (ICAS). 10 patients had incidental discovered IAs, and 7 patients presented with neurological deficits due to ischemia or aneurysmal mass effects. 10 cases were IIB with interposition grafts, including 4 cases of superficial temporal artery (STA) and 6 cases of radial artery graft (RAG) bypass. Eight cases were IIB with non-interposition grafts, including 3 cases of in situ bypass, 1 case of reanastomosis, and 4 cases of reimplantation. The pre- and postoperative mRS were not changed or improved, and all the bypasses were patent. There was no mortality during the mean follow-up period of 50.0 months. Mean occlusion time of recipient artery was 59.5 min. Total 8 patients had postoperative cerebral infarction but almost recovered at the discharge period. CONCLUSIONS: With the proper selection of the IIB type, IIB surgery can be a suitable treatment option for some patients with complex IAs and ICAS when extracranial-to-intracranial bypass is not feasible.

Optimal target blood pressure for the primary prevention of hemorrhagic stroke: a nationwide observational study.

Background: There are few reports on the preventative value of intensive blood pressure (BP) management for stroke, especially hemorrhagic stroke (HS), after new criteria for hypertension (HTN) were announced by the American College of Cardiology/American Heart Association in 2017. Aims: This study aimed to identify the optimal BP for the primary prevention of HS in a healthy population aged between 20 and 65 years. Methods: We conducted a 10-year observational study on the risk of HS, subclassified as intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH) according to BP categories (e.g., low normal BP, high normal BP, elevated BP, stage 1 HTN, and stage 2 HTN) using the National Health Insurance Service Database. Results: Out of 8,327,751 participants who underwent a health checkup in 2008, 949,550 were included in this study and observed from 2009 to 2018. The risk of ICH was significantly increased in men with stage 2 HTN {adjusted hazard ratio [aHR] 2.002 [95% confidence interval (CI) 1.203-3.332]} and in women with stage 1 HTN [aHR 2.021 (95% CI, 1.251-3.263)]. The risk of SAH was significantly increased in both men [aHR 1.637 (95% CI, 1.066-2.514)] and women [aHR 4.217 (95% CI, 2.648-6.715)] with stage 1 HTN. Additionally, the risk of HS was significantly increased in men with stage 2 HTN [aHR 3.034 (95% CI, 2.161-4.260)] and in women with stage 1 HTN [aHR 2.976 (95% CI, 2.222-3.986)]. Conclusion: To prevent primary HS, including ICH and SAH, BP management is recommended for adults under the age of 65 years with stage 1 HTN.

Validation of prediction algorithm for risk estimation of intracranial aneurysm development using real-world data.

Intracranial aneurysm (IA) is difficult to detect, and most patients remain undiagnosed, as screening tests have potential risks and high costs. Thus, it is important to develop risk assessment system for efficient and safe screening strategy. Through previously published research, we have developed a prediction model for the incidence risk of IA using cohort observational data. This study was designed to verify whether such a prediction model also demonstrates sufficient clinical performance in predicting the prevalence risk at the point of health screening, using cross-sectional data. The study population comprised individuals who visited the Chonnam National University Hwasun Hospital Health Promotion Center in Korea for voluntary medical checkups between 2007 and 2019. All participants had no history of cerebrovascular disease and underwent brain CTA for screening purpose. Presence of IA was evaluated by two specialized radiologists. The risk score was calculated using the previously developed AI model, and 0 point represents the lowest risk and 100 point represents the highest risk. To compare the prevalence according to the risk, age-sex standardization using national database was performed. A study collected data from 5942 health examinations, including brain CTA data, with participants ranging from 20 to 87 years old and a mean age of 52 years. The age-sex standardized prevalence of IA was 3.20%. The prevalence in each risk group was 0.18% (lowest risk, 0-19), 2.12% (lower risk, 20-39), 2.37% (mid-risk, 40-59), 4.00% (higher risk, 60-79), and 6.44% (highest risk, 80-100). The odds ratio between the lowest and highest risk groups was 38.50. The adjusted proportions of IA patients in the higher and highest risk groups were 26.7% and 44.5%, respectively. The median risk scores among IA patients and normal participants were 74 and 54, respectively. The optimal cut-off risk score was 60.5 with an area under the curve of 0.70. We have confirmed that the incidence risk prediction model built through machine learning also shows viable clinical performance in predicting prevalence risk. By utilizing this prediction system, we can effectively predict not only the incidence risk but also the prevalence risk, which is the probability of already having the disease, using health screening data. This may enable us to consider strategies for the early detection of intracranial aneurysms.

Clinical Outcomes of Coil Embolization for Unruptured Intracranial Aneurysms Categorized by Region and Hospital Size : A Nationwide Cohort Study in Korea.

OBJECTIVE: To analyze the outcomes of coil embolization (CE) for unruptured intracranial aneurysm (UIA) according to region and hospital size based on National Health Insurance Service data in South Korea. METHODS: The incidence of complications, including intracranial hemorrhage (ICRH) and cerebral infarction (CI), occurring within 3 months and the 1-year mortality rates in UIA patients who underwent CE in 2018 were analyzed. Hospitals were classified as tertiary referral general hospitals (TRGHs), general hospitals (GHs) or semigeneral hospitals (sGHs) according to their size, and the administrative districts of South Korea were divided into 15 regions. RESULTS: In 2018, 8425 (TRGHs, 4438; GHs, 3617; sGHs, 370) CEs were performed for UIAs. Complications occurred in 5.69% of patients seen at TRGHs, 13.48% at GHs, and 20.45% at sGHs. The complication rate in TRGHs was significantly lower than that in GHs (p=0.039) or sGHs (p=0.005), and that in GHs was significantly lower than that in sGHs (p=0.030). The mortality rates in TRGHs, GHs, and sGHs were 0.81%, 2.16%, and 3.92%, respectively, with no significant difference. Despite no significant difference in the mortality rates, the complication rate significantly increased as the number of CE procedures per hospital decreased (p=0.001; rho=-0.635). Among the hospitals where more than 30 CEs were performed for UIAs, the incidence of CIs (p=0.096, rho=-0.205) and the mortality rates (3 months, p=0.048, rho=-0.243; 1 year, p=0.009, rho=-0.315) significantly decreased as the number of CEs that were performed increased and no significant difference in the incidence of post-CE ICRH was observed. CONCLUSION: The complication rate in patients who underwent CE for UIA increased as the hospital size and physicians' experience in conducting CEs decreased. We recommend nationwide quality control policies CEs for UIAs.

Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA.

Accurate and reliable detection of intracranial aneurysms is vital for subsequent treatment to prevent bleeding. However, the detection of intracranial aneurysms can be time-consuming and even challenging, and there is great variability among experts, especially in the case of small aneurysms. This study aimed to detect intracranial aneurysms accurately using a convolutional neural network (CNN) with 3D time-of-flight magnetic resonance angiography (TOF-MRA). A total of 154 3D TOF-MRA datasets with intracranial aneurysms were acquired, and the gold standards were manually drawn by neuroradiologists. We also obtained 113 subjects from a public dataset for external validation. These angiograms were pre-processed by using skull-stripping, signal intensity normalization, and N4 bias correction. The 3D patches along the vessel skeleton from MRA were extracted. Values of the ratio between the aneurysmal and the normal patches ranged from 1:1 to 1:5. The semantic segmentation on intracranial aneurysms was trained using a 3D U-Net with an auxiliary classifier to overcome the imbalance in patches. The proposed method achieved an accuracy of 0.910 in internal validation and external validation accuracy of 0.883 with a 2:1 ratio of normal to aneurysmal patches. This multi-task learning method showed that the aneurysm segmentation performance was sufficient to be helpful in an actual clinical setting.

Intracranial steno-occlusive lesion detection on time-of-flight MR angiography using multi-task learning.

Steno-occlusive lesions in intracranial arteries refer to segments of narrowed or occluded blood vessels that increase the risk of ischemic strokes. Steno-occlusive lesion detection is crucial in clinical settings; however, automatic detection methods have hardly been studied. Therefore, we propose a novel automatic method to detect steno-occlusive lesions in sequential transverse slices on time-of-flight magnetic resonance angiography. Our method simultaneously detects lesions while segmenting blood vessels based on end-to-end multi-task learning, reflecting that the lesions are closely related to the connectivity of blood vessels. We design classification and localization modules that can be attached to arbitrary segmentation network. As blood vessels are segmented, both modules simultaneously predict the presence and location of lesions for each transverse slice. By combining outputs from the two modules, we devise a simple operation that boosts the performance of lesion localization. Experimental results show that lesion prediction and localization performance is improved by incorporating blood vessel extraction. Our ablation study demonstrates that the proposed operation enhances lesion localization accuracy. We also verify the effectiveness of multi-task learning by comparing our approach with those that individually detect lesions with extracted blood vessels.

Long-term outcomes and quantitative radiologic analysis of extracranial-intracranial bypass for hemodynamically compromised chronic large artery occlusive disease.

This study aimed to demonstrate the effectiveness of nonemergent extracranial-to-intracranial bypass (EIB) in symptomatic chronic large artery atherosclerotic stenosis or occlusive disease (LAA) through quantitative analysis of computed tomography perfusion (CTP) parameters using RAPID software. We retrospectively analyzed 86 patients who underwent nonemergent EIB due to symptomatic chronic LAA. CTP data obtained preoperatively, immediately postoperatively (PostOp0), and 6 months postoperatively (PostOp6M) after EIB were quantitatively analyzed through RAPID software, and their association with intraoperative bypass flow (BF) was assessed. The clinical outcomes, including neurologic state, incidence of recurrent infarction and complications, were also analyzed. The time-to-maximum (Tmax) > 8 s, > 6 s and > 4 s volumes decreased significantly at PostOp0 and up through PostOp6M (preoperative, 5, 51, and 223 ml (median), respectively; PostOp0, 0, 20.25, and 143 ml, respectively; PostOp6M, 0, 7.5, and 148.5 ml, respectively; p < 0.001, p < 0.001, and p < 0.001, respectively). The postoperative improvement in the Tmax > 6 s and > 4 s volumes was significantly correlated with the BF at PostOp0 and PostOp6M (PostOp0, r = 0.367 (p = 0.001) and r = 0.275 (p = 0.015), respectively; PostOp6M r = 0.511 (p < 0.001) and r = 0.391 (p = 0.001), respectively). The incidence of recurrent cerebral infarction was 4.7%, and there were no major complications that produced permanent neurological impairment. Nonemergent EIB under strict operation indications can be a feasible treatment for symptomatic, hemodynamically compromised LAA patients.

Automated deep neural network analysis of lateral cephalogram data can aid in detecting obstructive sleep apnea.

STUDY OBJECTIVES: Information on obstructive sleep apnea (OSA) is often latently detected in diagnostic tests conducted for other purposes, providing opportunities for maximizing value. This study aimed to develop a convolutional neural network (CNN) to identify the risk of OSA using lateral cephalograms. METHODS: The lateral cephalograms of 5,648 individuals (mean age, 49.0 ± 15.8 years; men, 62.3%) with or without OSA were collected and divided into training, validation, and internal test datasets in a 5:2:3 ratio. A separate external test dataset (n = 378) was used. A densely connected CNN was trained to diagnose OSA using a cephalogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Gradient-weighted class activation mapping (Grad-CAM) was used to evaluate the region of focus, and the relationships between the model outputs, anthropometric characteristics, and OSA severity were evaluated. RESULTS: The AUROC of the model for the presence of OSA was 0.82 (95% confidence interval, 0.80-0.84) and 0.73 (95% confidence interval, 0.65-0.81) in the internal and external test datasets, respectively. Grad-CAM demonstrated that the model focused on the area of the tongue base and oropharynx in the cephalogram. Sigmoid output values were positively correlated with OSA severity, body mass index, and neck and waist circumference. CONCLUSIONS: Deep learning may help develop a model that classifies OSA using a cephalogram, which may be clinically useful in the appropriate context. The definition of ground truth was the main limitation of this study. CITATION: Jeong H-G, Kim T, Hong JE, et al. Automated deep neural network analysis of lateral cephalogram data can aid in detecting obstructive sleep apnea. J Clin Sleep Med. 2023;19(2):327-337.

Prediction of hemorrhagic cerebral hyperperfusion syndrome after direct bypass surgery in adult nonhemorrhagic moyamoya disease: combining quantitative parameters on RAPID perfusion CT with clinically related factors.

OBJECTIVE: The aim of this study was to identify predictive factors for hemorrhagic cerebral hyperperfusion syndrome (hCHS) after direct bypass surgery in adult nonhemorrhagic moyamoya disease (non-hMMD) using quantitative parameters on rapid processing of perfusion and diffusion (RAPID) perfusion CT software. METHODS: A total of 277 hemispheres in 223 patients with non-hMMD who underwent combined bypass were retrospectively reviewed. Preoperative volumes of time to maximum (Tmax) > 4 seconds and > 6 seconds were obtained from RAPID analysis of perfusion CT. These quantitative parameters, along with other clinical and angiographic factors, were statistically analyzed to determine the significant predictors for hCHS after bypass surgery. RESULTS: Intra- or postoperative hCHS occurred in 13 hemispheres (4.7%). In 7 hemispheres, subarachnoid hemorrhage occurred intraoperatively, and in 6 hemispheres, intracerebral hemorrhage was detected postoperatively. All hCHS occurred within the 4 days after bypass. Advanced age (OR 1.096, 95% CI 1.039-1.163, p = 0.001) and a large volume of Tmax > 6 seconds (OR 1.011, 95% CI 1.004-1.018, p = 0.002) were statistically significant factors in predicting the risk of hCHS after surgery. The cutoff values of patient age and volume of Tmax > 6 seconds were 43.5 years old (area under the curve [AUC] 0.761) and 80.5 ml (AUC 0.762), respectively. CONCLUSIONS: In adult patients with non-hMMD older than 43.5 years or with a large volume of Tmax > 6 seconds over 80.5 ml, more prudence is required in the decision to undergo bypass surgery and in postoperative management.

Implantation of Acellular Dermal Matrix to Prevent Frontotemporal Depression Following Minipterional Craniotomy for the Surgical Clipping of Unruptured Intracranial Aneurysms.

OBJECTIVE: The objective of the study was to assess the esthetic efficacy of acellular dermal matrix (ADM) implantation to prevent frontotemporal depression (FTD) following minipterional craniotomy (MPT) to clip unruptured intracranial aneurysms. METHODS: We retrospectively compared the incidence of FTD in 100 patients treated without ADM from March to July 2019 and 100 patients treated with ADM from August to December 2019. ADM was implanted in the interfascial layer to cover the temporalis muscle. The specific location and degree of FTD were analyzed by measuring the thickness and area of multiple points (P1-P12) and regions (S1-S3) through brain computed tomography preoperatively and 1 year postoperatively. RESULTS: In the non-ADM group, the thickness at P1, P2, P5, P6, and P9 was reduced and the area of S1 and S2 was smaller after surgery than before surgery (P < 0.05), similar to the incision and suture site of the temporalis muscle. However, in the ADM group, the preoperative and postoperative measurements were not different. FTD recognition was significantly lower in the ADM group (6.0%) than that in the non-ADM group (17.0%) (P = 0.015) and occurred in the retroorbital region through P1, P2, P5, and P6, with the area under the receiver operating characteristic curves of 0.840, 0.766, 0.811, and 0.751, respectively. ADM implantation was the only significant predictive factor for FTD recognition in multivariate logistic regression analysis (odds ratio = 0.30; 95% confidence interval: 0.11-0.79; P = 0.015). CONCLUSIONS: Even MPT cannot completely prevent FTD in the retroorbital region. ADM implantation in MPT can help to improve esthetic satisfaction.

Quantitative radiological analysis and clinical outcomes of urgent EC-IC bypass for hemodynamic compromised patients with acute ischemic stroke.

This study aimed to demonstrate the effectiveness of urgent extracranial-to-intracranial bypass (EIB) in acute ischemic stroke (AIS) through quantitative analysis of computed tomography perfusion (CTP) results using RAPID software. We retrospectively analyzed 41 patients who underwent urgent EIB for AIS under strict operation criteria. The quantitative data from CTP images were reconstructed to analyze changes in pre- and postoperative perfusion status in terms of objective numerical values using RAPID software. Short- and long-term clinical outcomes, including complications and neurological status, were also analyzed. Postoperatively, the volume of time-to-max (Tmax) > 6 s decreased significantly; it continued to improve significantly until 6 months postoperatively (preoperative, 78 ml (median); immediate postoperative, 23 ml; postoperative 6 months, 7 ml; p = 0.000). Ischemic core-penumbra mismatch volumes were also significantly improved until 6 months postoperatively (preoperative, 72 ml (median); immediate postoperative, 23 ml; postoperative 6 months, 5 ml; p = 0.000). In addition, the patients' neurological condition improved significantly (p < 0.001). Only one patient (2.3%) showed progression of infarction. Urgent EIB using strict indications can be a feasible treatment for IAT-ineligible patients with AIS due to large vessel occlusion or stenosis.

Diagnostic triage in patients with central lumbar spinal stenosis using a deep learning system of radiographs.

OBJECTIVE: Magnetic resonance imaging (MRI) is the gold-standard tool for diagnosing lumbar spinal stenosis (LSS), but it is difficult to promptly examine all suspected cases with MRI considering the modality's high cost and limited accessibility. Although radiography is an efficient screening technique owing to its low cost, rapid operability, and wide availability, its diagnostic accuracy is relatively poor. In this study, the authors aimed to develop a deep learning model with a convolutional neural network (CNN) for diagnosing severe central LSS using radiography and to evaluate radiological diagnostic features using gradient-weighted class activation mapping (Grad-CAM). METHODS: Patients who had undergone both spinal MRI and radiography in the period from May 1, 2005, to December 31, 2017, were screened. According to the formal MRI report, participants were consecutively included in the severe central LSS or healthy control group, and radiographs for both groups were collected. A CNN-based transfer learning algorithm was developed to classify radiographic findings as LSS or normal (binary classification). The proposed models were evaluated using six performance metrics: area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and positive and negative predictive values. RESULTS: The VGG19 model achieved the highest accuracy with an AUROC of 90.0% (95% CI 89.8%-90.3%) by training 12,442 images. Accuracy was 82.8% (95% CI 82.5%-83.1%) by averaging 5-fold models. Feature points on Grad-CAM were reasonable, and the features could be categorized into reduced disc height, narrow foramina, short pedicle, and hyperdense facet joint. The AUROC in the extra validation was 89.3% (95% CI 88.7%-90.0%). Accuracy was 81.8% (95% CI 80.6%-83.0%) by averaging 5-fold models. Multivariate logistic regression analysis showed that a combination of demographic factors (age and sex) did not improve the model performance. CONCLUSIONS: The algorithm trained by a CNN to identify central LSS on radiographs showed high diagnostic accuracy and is expected to be useful as a triage tool. The algorithm could accurately localize the stenotic lesion to assist physicians in the identification of LSS.

Size-Related Differences in Computed Tomography Markers of Hematoma Expansion in Acute Intracerebral Hemorrhage.

BACKGROUND: Noncontrast computed tomography (NCCT) markers for hematoma expansion (HE) in intracerebral hemorrhage (ICH) are difficult to be found in small ICHs, of which can also expand. We aimed to investigate whether there were size-related differences in the prevalence of NCCT markers and their association with HE. METHODS: This retrospective analysis of prospectively collected stroke registry included 267 consecutive patients with ICH who underwent baseline NCCT within 12 h of onset. Qualitative NCCT markers, including heterogeneous density and irregular shape, were assessed. Hematoma density, defined as mean Hounsfield unit of hematoma, and hematoma volume were measured by semiautomated planimetry. Hematoma volume was categorized as small (≤ 10 ml) and large (> 10 ml). Associations of NCCT markers with HE were analyzed using multivariable logistic regression analyses. The model performances of NCCT markers and hematoma density were compared using receiver operating characteristic curves. RESULTS: Hematoma expansion occurred in 29.9% of small ICHs and 35.5% of large ICHs. Qualitative NCCT markers were less frequently observed in small ICHs. Heterogeneous density, irregular shape, and hematoma density were associated with HE in small ICH (adjusted odds ratios [95% confidence interval] 3.94 [1.50-10.81], 4.23 [1.73-10.81], and 0.72 [0.60-0.84], respectively), and hematoma density was also related to HE in large ICH (0.84 [0.73-0.97). The model performance was significantly improved in small ICHs when hematoma density was added to the baseline model (DeLong's test, p = 0.02). CONCLUSIONS: The prevalence of NCCT markers and their association with HE differed according to hematoma volume. Quantitative hematoma density was associated with HE, regardless of hematoma size.

The inhibition rate estimated using VerifyNow can help to predict the thromboembolic risk of coil embolization for unruptured intracranial aneurysms.

BACKGROUND: The role of the inhibition rate of VerifyNow in assessing the thromboembolic risk of coil embolization for unruptured intracranial aneurysms is unclear. OBJECTIVE: To carry out a retrospective study to determine whether the inhibition rate could provide additional help in predicting thromboembolic events when it was used for patients with a P2Y12 reaction unit (PRU) level of 220 or lower. METHODS: Patients who underwent coil embolization for unruptured aneurysms with an appropriate PRU level (PRU 220 or lower) between January 1, 2015 and December 31, 2018 were analyzed. A total of 954 patients with 1020 aneurysms were included in this study. The primary outcome was the thromboembolic events occurring within 30 days after coil embolization. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were obtained to determine the quantitative predictive ability of the inhibition rate. The optimal cut-off value was derived using the Youden index. RESULTS: Thromboembolic events developed in 11 patients (1.08% of 1020 procedures). The AUC of the ROC curve was 0.83. The optimal cut-off value of the inhibition rate derived using the maximum Youden index was 22.0%. A sensitivity test using a multiple logistic regression analysis demonstrated that the inhibition rate was a significant variable for predicting thromboembolic events. CONCLUSIONS: The inhibition rate can be used to determine high thromboembolic risks for patients with PRU levels of 220 or lower. The optimal cut-off value of the inhibition rate was 22.0% when the PRU level was 220 or less.

Effect of dental screening on cardiovascular risk: A nationwide cohort study.

AIM: To evaluate the effect of dental screening on the risk of cardiovascular disease (CVD) using data from a nationwide population-based cohort. MATERIALS AND METHODS: This retrospective cohort study extracted data of 478,245 individuals aged 40-79 years who participated in a health screening programme during 2002-2003 from the National Health Insurance Service-National Health Screening Cohort. Based on screening experience, participants were classified into the non-screening, general screening only, and dental screening groups. Using Cox proportional hazard models, hazard ratios (HRs) were determined for major adverse cardiovascular events (MACE) during each group's 11-year follow-up period. RESULTS: The risk of MACE in the dental screening group was 10% lower than that in the non-screening group (adjusted HR, 0.90; 95% confidence interval [CI], 0.87-0.93; p < .001) and 9% lower than that in the general screening only group (adjusted HR, 0.91; 95% CI, 0.89-0.94; p < .001). CONCLUSIONS: Dental screening was associated with a lower MACE risk; however, decreases in CVD-related healthcare utilization and costs were not clinically significant. The association could be attributed to healthy habits of participants in the dental screening group; nevertheless, it is conceivable that the improvement of oral health through dental screening influenced CVD prevention.

Simultaneous Craniotomies for Multiple Intracranial Aneurysm Clippings-One-Stage Surgery with Multiple Craniotomies.

OBJECTIVE: The treatment of multiple intracranial aneurysms (MIAs) involves various modalities and sometimes requires staged operations. This study aimed to prove the efficacy and safety of one-stage multiple craniotomies (OSMC) for multiple cerebral aneurysms. METHODS: We retrospectively reviewed the medical records of the patients who underwent treatment for intracranial aneurysms between May 2003 and April 2020. The surgical results, complications, and lengths of hospital stay were compared between the patients who underwent OSMC and those who underwent multistage multiple craniotomies. RESULTS: The demographic characteristics of the OSMC and multistage multiple craniotomies groups (n = 82 and 43, respectively) were similar. There were no statistically significant differences between the 2 groups when the amount of blood transfused, complications, and surgical results were compared (P = n.s. for all); however, the operation time and hospitalization period (353.9 minutes vs. 490.3 minutes and 12.3 days vs. 21.8 days, respectively; P = 0.001 for both) were shorter in the OSMC group. The treatment cost (17,000 USD vs. 22,000 USD, P = 0.001) was lower in the OSMC group. CONCLUSIONS: OSMC for aneurysm clipping in patients with MIAs is a relatively safe and economical method. Furthermore, it has good clinical outcomes. This new surgical method is worthwhile in that it can be applied to patients who are afraid to undergo multiple surgeries, and we suggest that it is an efficient, low-cost option for the treatment of MIAs.

Heavily T2-Weighted Magnetic Resonance Myelography as a Safe Cerebrospinal Fluid Leakage Detection Modality for Nontraumatic Subdural Hematoma.

OBJECTIVE: Nontraumatic subdural hematoma (SDH) is a common disease, and spinal cerebrospinal fluid (CSF) leakage is a possible etiology of unknown significance, which is commonly investigated by several invasive studies. This study demonstrates that heavily T2-weighted magnetic resonance myelography (HT2W-MRM) is a safe and clinically effective imaging modality for detecting CSF leakage in patients with nontraumatic SDH. METHODS: All patients who underwent HT2W-MRM for nontraumatic SDH workup at our institution were searched and enrolled in this study. Several parameters were measured and analyzed, including patient demographic data, initial modified Rankin Scale (mRS) score upon presentation, SDH bilaterality, hematoma thickness upon presentation, CSF leakage sites, treatment modalities, follow-up hematoma thickness, and follow-up mRS score. RESULTS: Forty patients were identified, of which 22 (55.0%) had CSF leakage at various spinal locations. Five patients (12.5%) showed no change in mRS score, whereas the remaining (87.5%) showed decreases in follow-up mRS scores. In terms of the overall hematoma thickness, four patients (10.0%) showed increased thickness, two (5.0%) showed no change, 32 (80.0%) showed decreased thickness, and two (5.0%) did not undergo follow-up imaging for hematoma thickness measurement. CONCLUSION: HT2W-MRM is not only safe but also clinically effective as a primary diagnostic imaging modality to investigate CSF leakage in patients with nontraumatic SDH. Moreover, this study suggests that CSF leakage is a common etiology for nontraumatic SDH, which warrants changes in the diagnosis and treatment strategies.

Deep learning for anatomical interpretation of video bronchoscopy images.

Anesthesiologists commonly use video bronchoscopy to facilitate intubation or confirm the location of the endotracheal tube; however, depth and orientation in the bronchial tree can often be confused because anesthesiologists cannot trace the airway from the oropharynx when it is performed using an endotracheal tube. Moreover, the decubitus position is often used in certain surgeries. Although it occurs rarely, the misinterpretation of tube location can cause accidental extubation or endobronchial intubation, which can lead to hyperinflation. Thus, video bronchoscopy with a decision supporting system using artificial intelligence would be useful in the anesthesiologic process. In this study, we aimed to develop an artificial intelligence model robust to rotation and covering using video bronchoscopy images. We collected video bronchoscopic images from an institutional database. Collected images were automatically labeled by an optical character recognition engine as the carina and left/right main bronchus. Except 180 images for the evaluation dataset, 80% were randomly allocated to the training dataset. The remaining images were assigned to the validation and test datasets in a 7:3 ratio. Random image rotation and circular cropping were applied. Ten kinds of pretrained models with < 25 million parameters were trained on the training and validation datasets. The model showing the best prediction accuracy for the test dataset was selected as the final model. Six human experts reviewed the evaluation dataset for the inference of anatomical locations to compare its performance with that of the final model. In the experiments, 8688 images were prepared and assigned to the evaluation (180), training (6806), validation (1191), and test (511) datasets. The EfficientNetB1 model showed the highest accuracy (0.86) and was selected as the final model. For the evaluation dataset, the final model showed better performance (accuracy, 0.84) than almost all human experts (0.38, 0.44, 0.51, 0.68, and 0.63), and only the most-experienced pulmonologist showed performance comparable (0.82) with that of the final model. The performance of human experts was generally proportional to their experiences. The performance difference between anesthesiologists and pulmonologists was marked in discrimination of the right main bronchus. Using bronchoscopic images, our model could distinguish anatomical locations among the carina and both main bronchi under random rotation and covering. The performance was comparable with that of the most-experienced human expert. This model can be a basis for designing a clinical decision support system with video bronchoscopy.

Mediation effects of mean Hounsfield unit on relationship between hemoglobin and expansion of intracerebral hemorrhage.

Low hemoglobin levels are known to be associated with hematoma expansion (HE) and poor functional outcome in patients with intracerebral hemorrhage (ICH). However, it is not yet known whether low hemoglobin itself causes HE directly or is merely a confounder. Thus, we investigated the mediation effect of the mean Hounsfield unit (HU) of hematoma on the relationship between low hemoglobin and expansion of ICH. Overall, 232 consecutive patients with ICH who underwent non-contrast computed tomography (NCCT) within 12 h since onset were included. The mean HU and hematoma volume on NCCT were investigated using semi-automated planimetry. HE was defined as an increase in hematoma volume > 33% or 6 mL. The respective associations among the hemoglobin level, mean HU, and HE were analyzed using multivariable regression analysis, adjusting for age, sex, and known HE predictors. Mediation analysis was performed to examine the potential causal association among the three. HE occurred in 34.5% of patients; hemoglobin levels were inversely associated with HE occurrence (adjusted odds ratio, 0.90; p = 0.03). The mean HU of the hematoma was lower in patients with HE than in patients without HE (58.5 ± 3.3 vs. 56.8 ± 3.0; p < 0.01). Hemoglobin levels on admission were linearly related to the mean HU (adjusted ß, 0.33; p < 0.01) after adjusting for known HE predictors (time from onset to CT, antithrombotic use, hematoma volume). Causal mediation analysis showed a significant mediation effect of the mean HU on the association between hemoglobin levels and HE (p = 0.04). The proportion of indirect effect through the mean HU among the total effect was 19% (p = 0.05). The mediation effect became nonsignificant in the when the multivariable model was adjusted with additional covariates (baseline systolic blood pressure and hematoma location). The mean HU of the hematoma mediated the association between hemoglobin levels and HE occurrence. Therefore, the mean HU of the hematoma may be a potential marker of impaired hemostasis in patients with ICH.