Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives

Shi, Zhengyi; Hu, Bing; Schoepf, U. Joseph; Savage, Rock H.; Dargis, Danielle M.; Pan, Cheng-Tang; Li, Xiuli; Niu, Qian; Lu, Guang Ming; Zhang, Long Jiang

doi:10.3174/ajnr.a6468

Cited by 80 publications

(47 citation statements)

References 55 publications

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“…MR angiography (MRA) or CTA-based CAD programs have been devised to automatically detect IAs. The conventional-style CAD systems were based on pre-supplied characteristics or imaging features, such as vessel curvature, thresholding, or a region-growing algorithm 16 , 17 . In addition, their performances in the real-world and their generalization have not been fully investigated.…”

Section: Introductionmentioning

confidence: 99%

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

et al. 2020

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Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients’ care in comparison to clinicians’ assessment.

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Section: Introductionmentioning

confidence: 99%

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

et al. 2020

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“…Including other reports [9,10,48], AI-based prediction for functional outcome has become established. Now AI works on the next stage: predicting rupture risk, automated calculation of hemodynamics, automated morphologic analysis to predict rupture, and automated aneurysm diagnosis [49].…”

Section: Recent Ai-based Prediction Models For Functional Outcome Of Sahmentioning

confidence: 99%

Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia

Katsuki¹,

Kawamura²,

Koh³

2021

Cureus

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IntroductionReliable prediction models of subarachnoid hemorrhage (SAH) outcomes and delayed cerebral ischemia (DCI) are needed to decide the treatment strategy. Automated artificial intelligence (AutoAI) is attractive, but there are few reports on AutoAI-based models for SAH functional outcomes and DCI. We herein made models using an AutoAI framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), and compared it to other previous statistical prediction scores. MethodsWe used an open dataset of 298 SAH patients, who were with non-severe neurological grade and treated by coiling. Modified Rankin Scale 0-3 at six months was defined as a favorable functional outcome and DCI occurrence as another outcome. We randomly divided them into a 248-patient training dataset and a 50patient test dataset. Prediction One made the model using training dataset with 5-fold cross-validation. We evaluated the model using the test dataset and compared the area under the curves (AUCs) of the created models. Those of the modified SAFIRE score and the Fisher computed tomography (CT) scale to predict the outcomes. ResultsThe AUCs of the AutoAI-based models for functional outcome in the training and test dataset were 0.994 and 0.801, and those for the DCI occurrence were 0.969 and 0.650. AUCs for functional outcome calculated using modified SAFIRE score were 0.844 and 0.892. Those for the DCI occurrence calculated using the Fisher CT scale were 0.577 and 0.544. ConclusionsWe easily and quickly made AutoAI-based prediction models. The models' AUCs were not inferior to the previous prediction models despite the easiness.

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“…In this context, computer-aided diagnostic (CAD) systems, which help improve the interpretation accuracy of radiologists by providing a second opinion, are considered to be one of the possible options. 9 Moreover, along with recent advances in the field of deep learning, several studies have evaluated deep learning algorithms in terms of their applicability as a CAD algorithm for intracranial aneurysm detection on TOF MRA. 10 11 12 13 14 These studies achieved high detection sensitivities and proved that deep learning algorithms were useful as a supportive and complementary tool for reducing the number of missed aneurysms.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model with High Standalone Performance for Diagnosis of Unruptured Intracranial Aneurysm

et al. 2021

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Purpose This study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to that of human radiologists for approval from the Korean Ministry of Food and Drug Safety as an artificial intelligence-applied software. Materials and Methods In this single-center, retrospective, confirmatory clinical trial, the diagnostic performance of the model was evaluated in a predetermined test set. After sample size estimation, the test set consisted of 135 aneurysm-containing examinations with 168 intracranial aneurysms and 197 aneurysm-free examinations. The target sensitivity and specificity were set as 87% and 92%, respectively. The patient-wise sensitivity and specificity of the model were analyzed. Moreover, the lesion-wise sensitivity and false-positive detection rate per case were also investigated. Results The sensitivity and specificity of the model were 91.11% [95% confidence interval (CI): 84.99, 95.32] and 93.91% (95% CI: 89.60, 96.81), respectively, which met the target performance values. The lesion-wise sensitivity was 92.26%. The overall false-positive detection rate per case was 0.123. Of the 168 aneurysms, 13 aneurysms from 12 examinations were missed by the model. Conclusion The present deep learning model for automated detection of unruptured intracranial aneurysms on TOF MRA achieved the target diagnostic performance comparable to that of human radiologists. With high standalone performance, this model may be useful for accurate and efficient diagnosis of intracranial aneurysm.

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Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives

Cited by 80 publications

References 55 publications

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia

A Deep Learning Model with High Standalone Performance for Diagnosis of Unruptured Intracranial Aneurysm

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