Deep neural network-based detection and segmentation of intracranial aneurysms on 3D rotational DSA

Liu, X.; Feng, Junqiang; Wu, Zhenzhou; Neo, Zhonghao; Zhu, Chengcheng; Zhang, Peifang; Wang, Yan; Jiang, Yuhua; Mitsouras, Dimitrios; Li, Youxiang

doi:10.1177/15910199211000956

Cited by 13 publications

(8 citation statements)

References 34 publications

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“…Figure 1 shows that overall, 1736 studies met the search criteria and 99 potentially eligible full-text articles were assessed. Forty-three studies ranging from October 2004 to August 2021 were included 21–63. The total number of patient cases used for both training and testing was 18 143, and of these 10 625 patients had aneurysms, with a combined total of 12 990 aneurysms (datasets that were used across different studies were only included once).…”

Section: Resultsmentioning

confidence: 99%

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis

Din

Agarwal

Grzeda

et al. 2022

J NeuroIntervent Surg

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BackgroundSubarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of morbidity and mortality. Early aneurysm identification, aided by automated systems, may improve patient outcomes. Therefore, a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence (AI) algorithms in detecting cerebral aneurysms using CT, MRI or DSA was performed.MethodsMEDLINE, Embase, Cochrane Library and Web of Science were searched until August 2021. Eligibility criteria included studies using fully automated algorithms to detect cerebral aneurysms using MRI, CT or DSA. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy (PRISMA-DTA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis included a bivariate random-effect model to determine pooled sensitivity, specificity, and area under the receiver operator characteristic curve (ROC-AUC). PROSPERO: CRD42021278454.Results43 studies were included, and 41/43 (95%) were retrospective. 34/43 (79%) used AI as a standalone tool, while 9/43 (21%) used AI assisting a reader. 23/43 (53%) used deep learning. Most studies had high bias risk and applicability concerns, limiting conclusions. Six studies in the standalone AI meta-analysis gave (pooled) 91.2% (95% CI 82.2% to 95.8%) sensitivity; 16.5% (95% CI 9.4% to 27.1%) false-positive rate (1-specificity); 0.936 ROC-AUC. Five reader-assistive AI studies gave (pooled) 90.3% (95% CI 88.0% – 92.2%) sensitivity; 7.9% (95% CI 3.5% to 16.8%) false-positive rate; 0.910 ROC-AUC.ConclusionAI has the potential to support clinicians in detecting cerebral aneurysms. Interpretation is limited due to high risk of bias and poor generalizability. Multicenter, prospective studies are required to assess AI in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis

Din

Agarwal

Grzeda

et al. 2022

J NeuroIntervent Surg

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“…7 studies used computed tomography angiography (CTA), [25][26][27][28][29][30][31] 9 used time of flight magnetic resonance angiography (TOF-MRA), 22,[32][33][34][35][36][37][38][39] and 4 used Digital Subtraction Angiography (DSA). [40][41][42][43] 18 out of 20 articles had retrospective study design, and the two others used randomized crossover 25 and crossover design. 27 6 articles compared the sensitivity and/or specificity of ML model to the human readers.…”

Section: Study Characteristicsmentioning

confidence: 99%

Application of convolutional network models in detection of intracranial aneurysms: A systematic review and meta-analysis

et al. 2022

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Introduction Intracranial aneurysms have a high prevalence in human population. It also has a heavy burden of disease and high mortality rate in the case of rupture. Convolutional neural network(CNN) is a type of deep learning architecture which has been proven powerful to detect intracranial aneurysms. Methods Four databases were searched using artificial intelligence, intracranial aneurysms, and synonyms to find eligible studies. Articles which had applied CNN for detection of intracranial aneurisms were included in this review. Sensitivity and specificity of the models and human readers regarding modality, size, and location of aneurysms were sought to be extracted. Random model was the preferred model for analyses using CMA 2 to determine pooled sensitivity and specificity. Results Overall, 20 studies were used in this review. Deep learning models could detect intracranial aneurysms with a sensitivity of 90/6% (CI: 87/2–93/2%) and specificity of 94/6% (CI: 0/914–0/966). CTA was the most sensitive modality (92.0%(CI:85/2–95/8%)). Overall sensitivity of the models for aneurysms more than 3 mm was above 98% (98%-100%) and 74.6 for aneurysms less than 3 mm. With the aid of AI, the clinicians’ sensitivity increased to 12/8% and interrater agreement to 0/193. Conclusion CNN models had an acceptable sensitivity for detection of intracranial aneurysms, surpassing human readers in some fields. The logical approach for application of deep learning models would be its use as a highly capable assistant. In essence, deep learning models are a groundbreaking technology that can assist clinicians and allow them to diagnose intracranial aneurysms more accurately.

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“…5 Deep neural network, such as convolutional neural network, has been applied in the detection of aneurysms from MRA images, [6][7][8][9] CTA images, [10][11][12][13][14] and DSA images. [15][16][17][18] For rupture risk prediction, most studies employ statistical learning algorithms such as logistic regression, support vector machine, and random forest, with features such as morphology, 19 hemodynamics, 20 radiomics, 21,22 and demographics 23 as input variables. A few studies applied deep learning on images for rupture prediction.…”

Section: Introductionmentioning

confidence: 99%

“…provided a detailed review of artificial intelligence applied for the detection, risk management, and treatment planning of IAs 5 . Deep neural network, such as convolutional neural network, has been applied in the detection of aneurysms from MRA images, 6–9 CTA images, 10–14 and DSA images 15–18 . For rupture risk prediction, most studies employ statistical learning algorithms such as logistic regression, support vector machine, and random forest, with features such as morphology, 19 hemodynamics, 20 radiomics, 21,22 and demographics 23 as input variables.…”

Section: Introductionmentioning

confidence: 99%

A deep learning–based automatic system for intracranial aneurysms diagnosis on three‐dimensional digital subtraction angiographic images

Qian

Chong

et al. 2022

Medical Physics

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Background Intracranial aneurysms (IAs) are a life‐threatening disease. Their rupture can lead to hemorrhagic stroke. Most studies applying deep learning for the detection of aneurysms are based on angiographic images. However, critical diagnostic information such as morphology and aneurysm location are not captured by deep learning algorithms and still require manual assessments. Purpose Digital subtraction angiography (DSA) is the gold standard for aneurysm diagnosis. To facilitate the fully automatic diagnosis of aneurysms, we proposed a comprehensive system for the detection, morphology measurement, and location classification of aneurysms on three‐dimensional DSA images, allowing automatic diagnosis without further human input. Methods The system comprised three neural networks: a network for aneurysm detection, a network for morphology measurement, and a network for aneurysm location identification. A cross‐scale dual‐path transformer module was proposed to effectively fuse local and global information to capture aneurysms of varying sizes. A multitask learning approach was also proposed to allow an accurate localization of aneurysm neck for morphology measurement. Results The cross‐scale dual‐path transformer module was shown to outperform other state‐of‐the‐art network architectures, improving segmentation, and classification accuracy. The detection network in our system achieved an F2 score of 0.946 (recall 93%, precision 100%), better than the winning team in the Cerebral Aneurysm Detection and Analysis challenge. The measurement network achieved a relative error of less than 10% for morphology measurement, at the same level as human operators. Perfect accuracy (100%) was achieved on aneurysm location classification. Conclusions We have demonstrated that a comprehensive system can automatically detect, measure morphology and report the aneurysm location of aneurysms without human intervention. This can be a potential tool for the diagnosis of IAs, improving radiologists’ performance and reducing their workload.

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Deep neural network-based detection and segmentation of intracranial aneurysms on 3D rotational DSA

Cited by 13 publications

References 34 publications

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis

Application of convolutional network models in detection of intracranial aneurysms: A systematic review and meta-analysis

A deep learning–based automatic system for intracranial aneurysms diagnosis on three‐dimensional digital subtraction angiographic images

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