UniXGen: A Unified Vision-Language Model for Multi-View Chest X-ray Generation and Report Generation

Generated synthetic data in medical research can substitute privacy and security-sensitive data with a large-scale curated dataset, reducing data collection and annotation costs. As part of this effort, we propose UniXGen, a unified chest X-ray and report generation model, with the following contributions. First, we design a unified model for bidirectional chest X-ray and report generation by adopting a vector quantization method to discretize chest X-rays into discrete visual tokens and formulating both tasks as sequence generation tasks. Second, we introduce several special tokens to generate chest X-rays with specific views that can be useful when the desired views are unavailable. Furthermore, UniXGen can flexibly take various inputs from single to multiple views to take advantage of the additional findings available in other X-ray views. We adopt an efficient transformer for computational and memory efficiency to handle the long-range input sequence of multi-view chest X-rays with high resolution and long paragraph reports. In extensive experiments, we show that our unified model has a synergistic effect on both generation tasks, as opposed to training only the task-specific models. We also find that view-specific special tokens can distinguish between different views and properly generate specific views even if they do not exist in the dataset, and utilizing multi-view chest X-rays can faithfully capture the abnormal findings in the additional X-rays. The source code is publicly available at: https://github.com/ttumyche/UniXGen

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

BackgroundThere 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.AimsThis study aimed to identify the optimal BP for the primary prevention of HS in a healthy population aged between 20 and 65 years.MethodsWe 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.ResultsOut 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)].ConclusionTo prevent primary HS, including ICH and SAH, BP management is recommended for adults under the age of 65 years with stage 1 HTN

Spider U-Net: Incorporating Inter-Slice Connectivity Using LSTM for 3D Blood Vessel Segmentation

Blood vessel segmentation (BVS) of 3D medical imaging such as computed tomography and magnetic resonance angiography (MRA) is an essential task in the clinical field. Automation of 3D BVS using deep supervised learning is being researched, and U-Net-based approaches, which are considered as standard for medical image segmentation, are proposed a lot. However, the inherent characteristics of blood vessels, e.g., they are complex and narrow, as well as the resolution and sensitivity of the imaging modalities increases the difficulty of 3D BVS. We propose a novel U-Net-based model named Spider U-Net for 3D BVS that considers the connectivity of the blood vessels between the axial slices. To achieve this, long short-term memory (LSTM), which can capture the context of the consecutive data, is inserted into the baseline model. We also propose a data feeding strategy that augments data and makes Spider U-Net stable. Spider U-Net outperformed 2D U-Net, 3D U-Net, and the fully convolutional network-recurrent neural network (FCN-RNN) in dice coefficient score (DSC) by 0.048, 0.077, and 0.041, respectively, for our in-house brain MRA dataset and also achieved the highest DSC for two public datasets. The results imply that considering inter-slice connectivity with LSTM improves model performance in the 3D BVS task

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

Abstract 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

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

© AANS 2022, except where prohibited by US copyright lawOBJECTIVE 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.N

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

Abstract 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

Machine learning for detecting moyamoya disease in plain skull radiography using a convolutional neural networkResearch in context

Background: Recently, innovative attempts have been made to identify moyamoya disease (MMD) by focusing on the morphological differences in the head of MMD patients. Following the recent revolution in the development of deep learning (DL) algorithms, we designed this study to determine whether DL can distinguish MMD in plain skull radiograph images. Methods: Three hundred forty-five skull images were collected as an MMD-labeled dataset from patients aged 18 to 50 years with definite MMD. As a control-labeled data set, 408 skull images of trauma patients were selected by age and sex matching. Skull images were partitioned into training and test datasets at a 7:3 ratio using permutation. A total of six convolution layers were designed and trained. The accuracy and area under the receiver operating characteristic (AUROC) curve were evaluated as classifier performance. To identify areas of attention, gradient-weighted class activation mapping was applied. External validation was performed with a new dataset from another hospital. Findings: For the institutional test set, the classifier predicted the true label with 84·1% accuracy. Sensitivity and specificity were both 0·84. AUROC was 0·91. MMD was predicted by attention to the lower face in most cases. Overall accuracy for external validation data set was 75·9%. Interpretation: DL can distinguish MMD cases within specific ages from controls in plain skull radiograph images with considerable accuracy and AUROC. The viscerocranium may play a role in MMD-related skull features. Fund: This work was supported by grant no. 18-2018-029 from the Seoul National University Bundang Hospital Research Fund. Keywords: Convolutional neural network, Deep learning, Moyamoya, Skul