Deep Learning–Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study

Stroke ◽  
2021 ◽  
Author(s):  
Maximilian Nielsen ◽  
Moritz Waldmann ◽  
Andreas M. Frölich ◽  
Fabian Flottmann ◽  
Evelin Hristova ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cerebral Infarction ◽  
Digital Subtraction Angiography ◽  
Information Integration ◽  
Mechanical Thrombectomy ◽  
Model Updating ◽  
Digital Subtraction ◽  
Cohen’S Kappa ◽  
Expert Rating ◽  
Cohen's Kappa

Background and Purpose: Mechanical thrombectomy is an established procedure for treatment of acute ischemic stroke. Mechanical thrombectomy success is commonly assessed by the Thrombolysis in Cerebral Infarction (TICI) score, assigned by visual inspection of X-ray digital subtraction angiography data. However, expert-based TICI scoring is highly observer-dependent. This represents a major obstacle for mechanical thrombectomy outcome comparison in, for instance, multicentric clinical studies. Focusing on occlusions of the M1 segment of the middle cerebral artery, the present study aimed to develop a deep learning (DL) solution to automated and, therefore, objective TICI scoring, to evaluate the agreement of DL- and expert-based scoring, and to compare corresponding numbers to published scoring variability of clinical experts. Methods: The study comprises 2 independent datasets. For DL system training and initial evaluation, an in-house dataset of 491 digital subtraction angiography series and modified TICI scores of 236 patients with M1 occlusions was collected. To test the model generalization capability, an independent external dataset with 95 digital subtraction angiography series was analyzed. Characteristics of the DL system were modeling TICI scoring as ordinal regression, explicit consideration of the temporal image information, integration of physiological knowledge, and modeling of inherent TICI scoring uncertainties. Results: For the in-house dataset, the DL system yields Cohen’s kappa, overall accuracy, and specific agreement values of 0.61, 71%, and 63% to 84%, respectively, compared with the gold standard: the expert rating. Values slightly drop to 0.52/64%/43% to 87% when the model is, without changes, applied to the external dataset. After model updating, they increase to 0.65/74%/60% to 90%. Literature Cohen’s kappa values for expert-based TICI scoring agreement are in the order of 0.6. Conclusions: The agreement of DL- and expert-based modified TICI scores in the range of published interobserver variability of clinical experts highlights the potential of the proposed DL solution to automated TICI scoring.

scholarly journals Classification of Shoulder X-ray Images with Deep Learning Ensemble Models

2021 ◽  
Vol 11 (6) ◽  
pp. 2723
Author(s):  
Fatih Uysal ◽  
Fırat Hardalaç ◽  
Ozan Peker ◽  
Tolga Tolunay ◽  
Nil Tokgöz
Keyword(s):  
Deep Learning ◽  
Performance Test ◽  
The Body ◽  
Test Accuracy ◽  
Cohen’S Kappa ◽  
X Ray ◽  
Cohen's Kappa ◽  
Auc Value ◽  
Magnetic Resonance Imaging Mri ◽  
Fully Connected

Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from X-radiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture/non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pre-trained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pre-trained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pre-trained models with the best performance, test accuracy was 0.8455, 0.8472, Cohen’s kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862, 0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohen’s kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.

scholarly journals Arteriovenous shunting and early cortical venous filling in subacute cerebral infarction: An old angiographic finding revisited

2002 ◽  
Vol 6 (3) ◽  
pp. 32-33
Author(s):  
Ian C. Duncan
Keyword(s):  
Cerebral Infarction ◽  
Digital Subtraction Angiography ◽  
Digital Subtraction ◽  
Angiographic Finding ◽  
Arteriovenous Shunting ◽  
Venous Filling ◽  
Sectional Imaging

Demonstrated in this report is an example of arteriovenous shunting and early venous filling in an area of cerebral infarction recorded on digital subtraction angiography. This angiographic appearance is largely of historical interest given the current use of sectional imaging (CT and MR) and altered role of angiography in the imaging of stroke, but should nevertheless still be considered amongst the differential causes of cerebral arteriovenous shunting.

Automated prostate lesion detection and PI-RADS assessment with deep learning.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e16605-e16605
Author(s):  
Choongheon Yoon ◽  
Jasper Van ◽  
Michelle Bardis ◽  
Param Bhatter ◽  
Alexander Ushinsky ◽  
...  
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Lesion Detection ◽  
Dice Coefficient ◽  
Cohen’S Kappa ◽  
Transrectal Biopsy ◽  
Cohen's Kappa ◽  
Feature Pyramid

e16605 Background: Prostate Cancer is the most commonly diagnosed male cancer in the U.S. Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for both prostate cancer evaluation and biopsy guidance. The PI-RADS v2 scoring paradigm was developed to stratify prostate lesions on MRI and to predict lesion grade. Prostate organ and lesion segmentation is an essential step in pre-biopsy surgical planning. Deep learning convolutional neural networks (CNN) for image recognition are becoming a more common method of machine learning. In this study, we develop a comprehensive deep learning pipeline of 3D/2D CNN based on U-Net architecture for automatic localization and segmentation of prostates, detection of prostate lesions and PI-RADS v2 lesion scoring of mpMRIs. Methods: This IRB approved retrospective review included a total of 303 prostate nodules from 217 patients who had a prostate mpMRI between September 2014 and December 2016 and an MR-guided transrectal biopsy. For each T2 weighted image, a board-certified abdominal radiologist manually segmented the prostate and each prostate lesion. The T2 weighted and ADC series were co-registered and each lesion was assigned an overall PI-RADS score, T2 weighted PI-RADS score, and ADC PI-RADS score. After a U-Net neural network segmented the prostate organ, a mask regional convolutional neural network (R-CNN) was applied. The mask R-CNN is composed of three neural networks: feature pyramid network, region proposal network, and head network. The mask R-CNN detected the prostate lesion, segmented it, and estimated its PI-RADS score. Instead, the mask R-CNN was implemented to regress along dimensions of the PI-RADS criteria. The mask R-CNN performance was assessed with AUC, Sørensen–Dice coefficient, and Cohen’s Kappa for PI-RADS scoring agreement. Results: The AUC for prostate nodule detection was 0.79. By varying detection thresholds, sensitivity/PPV were 0.94/.54 and 0.60/0.87 at either ends of the spectrum. For detected nodules, the segmentation Sørensen–Dice coefficient was 0.76 (0.72 – 0.80). Weighted Cohen’s Kappa for PI-RADS scoring agreement was 0.63, 0.71, and 0.51 for composite, T2 weighted, and ADC respectively. Conclusions: These results demonstrate the feasibility of implementing a comprehensive 3D/2D CNN-based deep learning pipeline for evaluation of prostate mpMRI. This method is highly accurate for organ segmentation. The results for lesion detection and categorization are modest; however, the PI-RADS v2 score accuracy is comparable to previously published human interobserver agreement.

Deep learning-based digital subtraction angiography image generation

2019 ◽  
Vol 14 (10) ◽  
pp. 1775-1784 ◽  
Author(s):  
Yufeng Gao ◽  
Yu Song ◽  
Xiangrui Yin ◽  
Weiwen Wu ◽  
Lu Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Digital Subtraction Angiography ◽  
Image Generation ◽  
Digital Subtraction

Clinical Application of Medical Imaging in Neurointerventional Treatment of Brain Diseases

2021 ◽  
Vol 11 (7) ◽  
pp. 1869-1876
Author(s):  
Qidong Wu ◽  
Zongliang Wu ◽  
Lei Zhang ◽  
Haiyang Wang
Keyword(s):  
Cerebral Infarction ◽  
Digital Subtraction Angiography ◽  
Clinical Effect ◽  
Spiral Ct ◽  
Brain Diseases ◽  
External Carotid ◽  
Digital Subtraction ◽  
Flat Detector ◽  
Risk Of Death ◽  
Atherosclerotic Stenosis

The incidence rate of cerebral infarction is high, and the risk of death is also grown significantly with age. Atherosclerotic stenosis is a part of the main causes of cerebral infarction. The effect of drug conservative therapy is not ideal. Interventional therapy is tantamount to send the guidewire, catheter and so on to the lesion site using imaging means, and operates the local area to achieve the purpose of a precise treatment. Therefore, it is important to explore the characteristics and high-risk factors of complications for clinical prevention and guidance of treatment righteousness. This study was to investigate the clinical effect of digital subtraction angiography (DSA) in the treatment of ischemic cerebrovascular disease. Also, this paper discusses the clinical effect of digital subtraction angiography (DSA) in the treatment of cerebral infarction. It has been proved that the application of flat detector CT in the interventional room can not only obtain high-quality 3D angiography (3D rotational angiography), but also display the vessels and high-density structures (skeleton, vascular clamp, coil, stent, and ingenious plaque). Fd-ct has also been proved to be able to perform 3D reconstruction on the stent placed in the patient’s heart and the stent of the external carotid artery. Compared with multi-slice spiral CT, the stent is much clearer and can be used to evaluate the soil and stent placement in the treatment of aneurysms.

scholarly journals Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning

SLEEP ◽  
2020 ◽  
Vol 43 (11) ◽  
Author(s):  
Maurice Abou Jaoude ◽  
Haoqi Sun ◽  
Kyle R Pellerin ◽  
Milena Pavlova ◽  
Rani A Sarkis ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Cohen’S Kappa ◽  
Scalp Eeg ◽  
Cohen's Kappa ◽  
Eeg Recordings

Abstract Study Objectives Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings. Methods Using a clinical dataset of polysomnograms from 6,431 patients (MGH–PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network for feature extraction, followed by a recurrent neural network that extracts temporal dependencies of sleep stages. The algorithm’s inputs are four scalp EEG bipolar channels (F3-C3, C3-O1, F4-C4, and C4-O2), which can be derived from any standard PSG or scalp EEG recording. We initially trained the algorithm on the MGH–PSG dataset and used transfer learning to fine-tune it on a dataset of long-term (24–72 h) scalp EEG recordings from 112 patients (scalpEEG dataset). Results The algorithm achieved a Cohen’s kappa of 0.74 on the MGH–PSG holdout testing set and cross-validated Cohen’s kappa of 0.78 after optimization on the scalpEEG dataset. The algorithm also performed well on two publicly available PSG datasets, demonstrating high generalizability. Performance on all datasets was comparable to the inter-rater agreement of human sleep staging experts (Cohen’s kappa ~ 0.75 ± 0.11). The algorithm’s performance on long-term scalp EEGs was robust over a wide age range and across common EEG background abnormalities. Conclusion We developed a deep learning algorithm that achieves human expert level sleep staging performance on long-term scalp EEG recordings. This algorithm, which we have made publicly available, greatly facilitates the use of large long-term EEG clinical datasets for sleep-related research.

Abstract P494: Automatically Predicting Modified Treatment in Cerebral Ischemia Scores From Patient Digital Subtraction Angiography Using Deep Learning

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Ayush Lall ◽  
Fabien Scalzo ◽  
Henrik Ullman ◽  
David S Liebeskind ◽  
Aichi Chien
Keyword(s):  
Neural Networks ◽  
Ischemic Stroke ◽  
Deep Learning ◽  
Cerebral Ischemia ◽  
Acute Ischemic Stroke ◽  
Digital Subtraction Angiography ◽  
Endovascular Therapy ◽  
Imaging Biomarker ◽  
Digital Subtraction ◽  
Neural Network Models

Introduction: We propose a new method for quantifying the effect of endovascular therapy for acute ischemic stroke. Currently, an mTICI (modified treatment in cerebral ischemia) score is assigned manually to document the success of endovascular revascularization therapy. The mTICI score based on Digital Subtraction Angiography (DSA), due to visual assignment, has limitations in settings where standardization is pertinent. Methods: We hypothesize that mTICI scores can be classified successfully by deep learning and thus be used as a standardized imaging biomarker. We aim to develop a regression framework using classification models that can assign continuous score to patients depending on the success of therapy, resulting in a score that is more granular than the mTICI. We use deep learning and 3D Convolutional Neural Networks (CNN) to classify frontal post-intervention DSA 2D time series into the mTICI score categories of 0, 1, 2a, 2b, and 3. An mTICI score of 0 represents no perfusion and a score of 3 represents full perfusion. The DSA series serve as features where the time dimension is the third dimension for the CNN. For our preliminary research we have condensed our groupings into binary {0,1} (0 refers to mTICI of 0, 1, 2a while 1 refers to mTICI of 2b, 3) of frontal DSA to see if Deep Learning models can categorize between the different mTICI classes. Results: We reduced our original data size of 181 patients to 93 patients in binary group 0 and 88 patients in group 1. Using a train/test split of 0.2, we have achieved a test classification accuracy of 73%, and F1-Score of 72.2% on the binary dataset. This is a good statistical indication that neural networks are able to classify between DSA. Conclusion: Neural network models show promise as a method of distinguishing between DSA to be used as an automatic standardized scoring method for acute ischemic stroke procedures. We aim to expand this research to frontal and lateral DSA images to get more vascular information to improve model accuracies. We propose using the softmax score of the classifier as a new score which will be a standardized measurement for endovascular therapy success.

Digital Subtraction Angiography in Cerebral Infarction

1995 ◽  
Vol 32 (1) ◽  
pp. 15
Author(s):  
Sin Young Cho ◽  
Eun Young Kwack ◽  
Hyo Heon Kim ◽  
Ik Won Kang ◽  
Kil Woo Lee ◽  
...  
Keyword(s):  
Cerebral Infarction ◽  
Digital Subtraction Angiography ◽  
Digital Subtraction

scholarly journals A calibrated deep learning ensemble for abnormality detection in musculoskeletal radiographs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Minliang He ◽  
Xuming Wang ◽  
Yijun Zhao
Keyword(s):  
Deep Learning ◽  
Kappa Statistic ◽  
X Rays ◽  
Cohen’S Kappa ◽  
Musculoskeletal Radiology ◽  
Locomotor System ◽  
The Public ◽  
Cohen's Kappa ◽  
Proposed Model ◽  
Overall Performance

AbstractMusculoskeletal disorders affect the locomotor system and are the leading contributor to disability worldwide. Patients suffer chronic pain and limitations in mobility, dexterity, and functional ability. Musculoskeletal (bone) X-ray is an essential tool in diagnosing the abnormalities. In recent years, deep learning algorithms have increasingly been applied in musculoskeletal radiology and have produced remarkable results. In our study, we introduce a new calibrated ensemble of deep learners for the task of identifying abnormal musculoskeletal radiographs. Our model leverages the strengths of three baseline deep neural networks (ConvNet, ResNet, and DenseNet), which are typically employed either directly or as the backbone architecture in the existing deep learning-based approaches in this domain. Experimental results based on the public MURA dataset demonstrate that our proposed model outperforms three individual models and a traditional ensemble learner, achieving an overall performance of (AUC: 0.93, Accuracy: 0.87, Precision: 0.93, Recall: 0.81, Cohen’s kappa: 0.74). The model also outperforms expert radiologists in three out of the seven upper extremity anatomical regions with a leading performance of (AUC: 0.97, Accuracy: 0.93, Precision: 0.90, Recall:0.97, Cohen’s kappa: 0.85) in the humerus region. We further apply the class activation map technique to highlight the areas essential to our model’s decision-making process. Given that the best radiologist performance is between 0.73 and 0.78 in Cohen’s kappa statistic, our study provides convincing results supporting the utility of a calibrated ensemble approach for assessing abnormalities in musculoskeletal X-rays.

scholarly journals Deep learning based detection of intracranial aneurysms on digital subtraction angiography: A feasibility study

2020 ◽  
Vol 33 (4) ◽  
pp. 311-317
Author(s):  
Nicolin Hainc ◽  
Manoj Mannil ◽  
Vaia Anagnostakou ◽  
Hatem Alkadhi ◽  
Christian Blüthgen ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Deep Learning ◽  
Digital Subtraction Angiography ◽  
Gold Standard ◽  
Intracranial Aneurysms ◽  
Cross Validation ◽  
Digital Subtraction ◽  
Commercial Grade ◽  
Learning Software ◽  
Monte Carlo Cross Validation

Background Digital subtraction angiography is the gold standard for detecting and characterising aneurysms. Here, we assess the feasibility of commercial-grade deep learning software for the detection of intracranial aneurysms on whole-brain anteroposterior and lateral 2D digital subtraction angiography images. Material and methods Seven hundred and six digital subtraction angiography images were included from a cohort of 240 patients (157 female, mean age 59 years, range 20–92; 83 male, mean age 55 years, range 19–83). Three hundred and thirty-five (47%) single frame anteroposterior and lateral images of a digital subtraction angiography series of 187 aneurysms (41 ruptured, 146 unruptured; average size 7±5.3 mm, range 1–5 mm; total 372 depicted aneurysms) and 371 (53%) aneurysm-negative study images were retrospectively analysed regarding the presence of intracranial aneurysms. The 2D data was split into testing and training sets in a ratio of 4:1 with 3D rotational digital subtraction angiography as gold standard. Supervised deep learning was performed using commercial-grade machine learning software (Cognex, ViDi Suite 2.0). Monte Carlo cross validation was performed. Results Intracranial aneurysms were detected with a sensitivity of 79%, a specificity of 79%, a precision of 0.75, a F1 score of 0.77, and a mean area-under-the-curve of 0.76 (range 0.68–0.86) after Monte Carlo cross-validation, run 45 times. Conclusion The commercial-grade deep learning software allows for detection of intracranial aneurysms on whole-brain, 2D anteroposterior and lateral digital subtraction angiography images, with results being comparable to more specifically engineered deep learning techniques.

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