RAPID Aneurysm: Artificial intelligence for unruptured cerebral aneurysm detection on CT angiography

Heit, Jeremy J; Honce, Justin M.; Yedavalli, Vivek; Baccin, Carlos Eduardo; Tatit, Rafael Trindade; Copeland, Karen A. F.; Timpone, Vincent M.

doi:10.1016/j.jstrokecerebrovasdis.2022.106690

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…3389/fnhum.2023.1254417 Frontiers in Human Neuroscience 10 frontiersin.org in their approved range of 4 mm or larger with a mean processing time of 114.7 s (Colasurdo et al, 2023). RAPID Aneurysm was one of the most promising accurate programs, demonstrating a sensitivity of 95% and accuracy of 99% across 51 patients (Heit et al, 2022). However, there remains a dearth of available data pertaining to CTA with ML in aneurysm diagnosis and management.…”

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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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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“…Initially, 608 studies that met the search criteria were identified, and from these, 163 full-text articles were assessed for potential eligibility. Eventually, 15 studies published between June 2019 and April 2023 were included in the analysis [8,9,[19][20][21][22][23][24][25][26][27][28][29][30][31]. The collective training and testing data encompassed 11,210 subjects, which featured a total of 13,086 IAs.…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…Of these, 9012 cases were utilized for training, while 4074 cases were allocated for validation purposes, both internally (3039 cases) and externally (765 cases). For comprehensive insights, Tables 1 and 2 present detailed characteristics of the studies, divided into two groups: (i) automatic methods (13/15, 87%) [8,9,[19][20][21][22][23][24][25][26][27][28]31], and (ii) semi-automatic methods (2/15, 13%) [29,30]. All studies were retrospective in nature.…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…Please note that these values were pooled across IAs of all sizes included in each study's test dataset and may be over-optimistic regarding the detection of small IAs. [8,9,[19][20][21][22][23][24][25][26][27][28][29][30]. The 95% confidence intervals (CIs) are given in square brackets.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…However, the diagnostic accuracy of these studies is somewhat limited due to a high risk of bias and concerns regarding the data used. For instance, approximately half of the studies included in this review did not report lesion-level sensitivity with respect to the IA size, and those that did exhibit important limitations such as limited count of small IAs in the test dataset [19,20,29], a small test dataset [19,20,28,30], internal instead of external dataset (all except [19]), poor sensitivity to small IAs [19,20,23,29], or a high number of false positives per image [8,24,27].…”

Section: Summary Of Findingsmentioning

confidence: 99%

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A Systematic Review of Deep-Learning Methods for Intracranial Aneurysm Detection in CT Angiography

Bizjak,

Špiclin

2023

Biomedicines

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Background: Subarachnoid hemorrhage resulting from cerebral aneurysm rupture is a significant cause of morbidity and mortality. Early identification of aneurysms on Computed Tomography Angiography (CTA), a frequently used modality for this purpose, is crucial, and artificial intelligence (AI)-based algorithms can improve the detection rate and minimize the intra- and inter-rater variability. Thus, a systematic review and meta-analysis were conducted to assess the diagnostic accuracy of deep-learning-based AI algorithms in detecting cerebral aneurysms using CTA. Methods: PubMed (MEDLINE), Embase, and the Cochrane Library were searched from January 2015 to July 2023. Eligibility criteria involved studies using fully automated and semi-automatic deep-learning algorithms for detecting cerebral aneurysms on the CTA modality. Eligible studies were assessed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A diagnostic accuracy meta-analysis was conducted to estimate pooled lesion-level sensitivity, size-dependent lesion-level sensitivity, patient-level specificity, and the number of false positives per image. An enhanced FROC curve was utilized to facilitate comparisons between the studies. Results: Fifteen eligible studies were assessed. The findings indicated that the methods exhibited high pooled sensitivity (0.87, 95% confidence interval: 0.835 to 0.91) in detecting intracranial aneurysms at the lesion level. Patient-level sensitivity was not reported due to the lack of a unified patient-level sensitivity definition. Only five studies involved a control group (healthy subjects), whereas two provided information on detection specificity. Moreover, the analysis of size-dependent sensitivity reported in eight studies revealed that the average sensitivity for small aneurysms (<3 mm) was rather low (0.56). Conclusions: The studies included in the analysis exhibited a high level of accuracy in detecting intracranial aneurysms larger than 3 mm in size. Nonetheless, there is a notable gap that necessitates increased attention and research focus on the detection of smaller aneurysms, the use of a common test dataset, and an evaluation of a consistent set of performance metrics.

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Revolutionizing Aneurysm detection: The role of artificial intelligence in reducing rupture rates

Akhtar,

Farooqi,

Nabi

2024

Neurosurg Rev

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RAPID Aneurysm: Artificial intelligence for unruptured cerebral aneurysm detection on CT angiography

Cited by 12 publications

References 9 publications

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

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

A Systematic Review of Deep-Learning Methods for Intracranial Aneurysm Detection in CT Angiography

Revolutionizing Aneurysm detection: The role of artificial intelligence in reducing rupture rates

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