Deep Learning–Based Software Improves Clinicians’ Detection Sensitivity of Aneurysms on Brain TOF-MRA

Sohn, Beomseok; Park, K.-Y.; Choi, Jongeun; Koo, Ja-Bin; Han, Kuo Sen; Joo, Bio; Won, So Yeon; Cha, Jihoon; Choi, Hyun Seok; Lee, Seung Koo

doi:10.3174/ajnr.a7242

Cited by 11 publications

(10 citation statements)

References 17 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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“…However, there remains a dearth of available data pertaining to CTA with ML in aneurysm diagnosis and management. With respect to MRA, when using ML techniques with CAD, the sensitivities for aneurysm detection ranged from 70 to 100%, depending on technique and size (Nakao et al, 2018;Sichtermann et al, 2019;Ueda et al, 2019;Faron et al, 2020;Joo et al, 2020;Shimada et al, 2020;Sohn et al, 2021;Terasaki et al, 2021). Most of these models utilized validated ML algorithms UNet, DeepMedic, and ResNet with retrospective data.…”

Section: The Role Of Ai In Aneurysm Detectionmentioning

confidence: 99%

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

Gilotra,

Swarna,

Mani

et al. 2023

Front. Hum. Neurosci.

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IntroductionCerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms.MethodsWe review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice.ResultsThe incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers.ConclusionThe implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

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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.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…27 6 articles compared the sensitivity and/or specificity of ML model to the human readers. 25,27,28,31,32,36 Sensitivity of ML and human reader based on location and size of aneurysms was reported in7 22,30,[33][34][35][36]38 and5 22,26,33,34,36 articles, respectively.…”

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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Deep Learning–Based Software Improves Clinicians’ Detection Sensitivity of Aneurysms on Brain TOF-MRA

Cited by 11 publications

References 17 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

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

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

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