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.

scholarly journals Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Wong ◽  
Z. Q. Lin ◽  
L. Wang ◽  
A. G. Chung ◽  
B. Shen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Lung Disease ◽  
Disease Severity ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
X Rays ◽  
Computer Aided ◽  
Monte Carlo Cross Validation ◽  
Validation Experiments

AbstractA critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R$$^2$$ 2 of $$0.664 \pm 0.032$$ 0.664 ± 0.032 and $$0.635 \pm 0.044$$ 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R$$^2$$ 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

Intraoperative digital subtraction angiography and the surgical treatment of intracranial aneurysms and vascular malformations

1990 ◽  
Vol 73 (4) ◽  
pp. 526-533 ◽  
Author(s):  
Neil A. Martin ◽  
John Bentson ◽  
Fernando Viñuela ◽  
Grant Hieshima ◽  
Murray Reicher ◽  
...  
Keyword(s):  
Digital Subtraction Angiography ◽  
Intracranial Aneurysms ◽  
Vascular Malformations ◽  
Operating Table ◽  
Digital Subtraction ◽  
Intraoperative Angiography ◽  
Content Type ◽  
Head Holder ◽  
Cerebral Embolization ◽  
Leg Ischemia

✓ Intraoperative digital subtraction angiography using commercially available equipment was employed to confirm the precision of the surgical result in 105 procedures for intracranial aneurysms or arteriovenous malformations (AVM's). Transfemoral selective arterial catheterization was performed in most of these cases. A radiolucent operating table was used in all cases, and a radiolucent head-holder in most. In five of the 57 aneurysm procedures, clip repositioning was required after intraoperative angiography demonstrated an inadequate result. In five of the 48 AVM procedures, intraoperative angiography demonstrated residual AVM nidus which was then located and resected. In two cases intraoperative angiography failed to identify residual filling of an aneurysm which was seen later on postoperative angiography, and in one case the intraoperative study failed to demonstrate a tiny residual fragment of AVM which was seen on conventional postoperative angiography. Two complications resulted from intraoperative angiography: one patient developed aphasia from cerebral embolization and one patient developed leg ischemia from femoral artery thrombosis. This technique appears to be of particular value in the treatment of complex intracranial aneurysms and vascular malformations.

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.

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