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

Din, Munaib; Agarwal, Siddharth; Grzeda, Mariusz; Wood, David A.; Modat, Marc; Booth, Thomas C.

doi:10.1136/jnis-2022-019456

Cited by 27 publications

(21 citation statements)

References 78 publications

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“…This step successfully overcame a significant obstacle often faced in brain age model development (i.e. identifying radiological normal scans in a large hospital dataset), resulting in a diverse and realistic set of training data that accurately represents clinical populations (Agarwal et al, 2023; Booth et al, 2023; Din et al, 2023). The diversity of our data, encompassing a range of scanner vendors, acquisition protocols, patient ethnicities, and a wide age span (18–96 years), added robustness to our models.…”

Section: Discussionmentioning

confidence: 99%

“…However, realising this goal will involve overcoming several challenges. One challenge is the lack of representativeness of research datasets (Agarwal et al, 2023; Agarwal & Wood et al, 2023; Din et al, 2023), particularly public datasets commonly used for training brain age models. This applies not only to the demographics of the study participants, but also to the nature of the MRI data (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Optimising brain age estimation through transfer learning: A suite of pre‐trained foundation models for improved performance and generalisability in a clinical setting

Wood,

Townend,

Guilhem

et al. 2024

Human Brain Mapping

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Estimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18–96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R2 ≥ .86) across five different MRI sequences (T2‐weighted, T2‐FLAIR, T1‐weighted, diffusion‐weighted, and gradient‐recalled echo T2*‐weighted). Our trained models offer dual functionality. First, they have the potential to be directly employed on clinical data. Second, they can be used as foundation models for further refinement to accommodate a range of other MRI sequences (and therefore a range of clinical scenarios which employ such sequences). This adaptation process, enabled by transfer learning, proved effective in our study across a range of MRI sequences and scan orientations, including those which differed considerably from the original training datasets. Crucially, our findings suggest that this approach remains viable even with limited data availability (as low as N = 25 for fine‐tuning), thus broadening the application of brain age estimation to more diverse clinical contexts and patient populations. By making these models publicly available, we aim to provide the scientific community with a versatile toolkit, promoting further research in brain age prediction and related areas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimising brain age estimation through transfer learning: A suite of pre‐trained foundation models for improved performance and generalisability in a clinical setting

Wood,

Townend,

Guilhem

et al. 2024

Human Brain Mapping

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“…25 In a meta-analysis of AI articles on intracranial aneurysm detection, the authors concluded that most studies had a high risk of bias with poor generalizability, with only one-quarter of studies using an appropriate reference standard and only 6/43 studies using an external or hold-out test set. 26 They found low-level evidence for using these AI algorithms and that none of the studies specifically tested for the possibility of bias in algorithm development. 26 In a study that used AI models to detect both intracranial hemorrhage and large-vessel occlusion, the algorithm showed similar excellent performance in diverse populations regardless of scanning parameters and geographic distribution, suggesting that it is unbiased.…”

Section: Primum No Nocerementioning

confidence: 99%

“…26 They found low-level evidence for using these AI algorithms and that none of the studies specifically tested for the possibility of bias in algorithm development. 26 In a study that used AI models to detect both intracranial hemorrhage and large-vessel occlusion, the algorithm showed similar excellent performance in diverse populations regardless of scanning parameters and geographic distribution, suggesting that it is unbiased. 27 This study did not use independent data sets to test that assertion formally.…”

Section: Primum No Nocerementioning

confidence: 99%

Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology

Filippi,

Stein,

Wang

et al. 2023

AJNR Am J Neuroradiol

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In this review, concepts of algorithmic bias and fairness are defined qualitatively and mathematically. Illustrative examples are given of what can go wrong when unintended bias or unfairness in algorithmic development occurs. The importance of explainability, accountability, and transparency with respect to artificial intelligence algorithm development and clinical deployment is discussed. These are grounded in the concept of "primum no nocere" (first, do no harm). Steps to mitigate unfairness and bias in task definition, data collection, model definition, training, testing, deployment, and feedback are provided. Discussions on the implementation of fairness criteria that maximize benefit and minimize unfairness and harm to neuroradiology patients will be provided, including suggestions for neuroradiologists to consider as artificial intelligence algorithms gain acceptance into neuroradiology practice and become incorporated into routine clinical workflow. ABBREVIATION: AI ¼ artificial intelligenceA rtificial intelligence (AI) is beginning to transform the practice of radiology, from order entry through image acquisition and reconstruction, workflow management, diagnosis, and treatment decisions. AI will certainly change neuroradiology practice across routine workflow, education, and research. Neuroradiologists are understandably concerned about how AI will affect their subspecialty and how they can shape its development. Multiple published consensus statements advocate the need for radiologists to play a primary role in ensuring that AI software used for clinical care is fair to and unbiased against specific groups of patients. 1 In this review, we focus on the need for developing and implementing fairness criteria and how to balance competing interests that minimize harm and maximize patient benefits when implementing AI solutions in neuroradiology. The responsibility for promoting health care equity rests with the entire neuroradiology community, from academic leaders to private practitioners. We all have a stake in establishing best practices as AI enters routine clinical practice. Definitions"Ethics," in a strict dictionary definition, is a theory or system of values that governs the conduct of individuals and groups. 2 Ethical physicians should endeavor to promote fairness and avoid bias in their personal treatment of patients and with respect to the health care system at large. A biased object yields 1 outcome more frequently than statistically expected, eg, a 2-headed coin. Similarly, a biased algorithm systematically produces outcomes that are not statistically expected. One proposed definition for algorithmic bias in health care systems is "when the application of an algorithm compounds existing inequities in socioeconomic status, race, ethnic background, religion, sex, disability, or sexual orientation to amplify them and adversely impact inequities in health systems." 3 This definition, while not ideal, is a request for developers and end users of AI algorithms in health care to be aware of the poten...

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“…The IAs are detected via visual assessment of the scans, which is a cumbersome task subject to human error. Even skilled radiologists achieve a rather low sensitivity for small IAs, for instance, from 64 to 74.1% for CTAs (IA diameter ≤ 3 mm) [5] and from 70 to 92.8% (IA diameter ≤ 5 mm) [6], which seems could be achieved or even improved using computerassisted deep learning and artificial intelligence-based approaches [7]. For instance, in a recent study, Yang et al [8] used such a computer-aided aneurysm detection tool and found that 8 out of 649 aneurysms (1.2%) had been overlooked in the initial radiologic reports.…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 27 publications

References 78 publications

Optimising brain age estimation through transfer learning: A suite of pre‐trained foundation models for improved performance and generalisability in a clinical setting

Optimising brain age estimation through transfer learning: A suite of pre‐trained foundation models for improved performance and generalisability in a clinical setting

Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology

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

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