Computer-aided detection of cerebral aneurysms with magnetic resonance angiography: usefulness of volume rendering to display lesion candidates

Miki, Soichiro; Nakao, Takahiro; Nomura, Yukihiro; Okimoto, Naomasa; Nyunoya, Keisuke; Nakamura, Yukihiko; Kurokawa, Ryo; Amemiya, Shonan; Yoshikawa, Tsuneo; Hanaoka, Shouhei; Hayashi, Naoto; Abe, Osamu

doi:10.1007/s11604-021-01099-4

Cited by 4 publications

(1 citation statement)

References 23 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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Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN’s final layer for distinguishing between aneurysm and infundibular dilatation

et al. 2022

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Purpose We evaluated the diagnostic performance of a clinically available deep learning-based computer-assisted diagnosis software for detecting unruptured aneurysms (UANs) using magnetic resonance angiography and assessed the functionality of the convolutional neural network (CNN) final layer score for distinguishing between UAN and infundibular dilatation (ID). Materials and methods EIRL brain aneurysm (EIRL_BA) was used in this study. The subjects were 117 UAN and/or ID cases including 100 UAN lesions (average sizes of 2.56 ± 1.45 mm) and 40 ID lesions (average sizes of 1.75 ± 0.41 mm) in any of internal carotid artery, middle cerebral artery, and anterior communicating artery, and 123 normal controls. The sensitivity, specificity, and accuracy of EIRL_BA were determined for UAN and ID or UAN only. Furthermore, the relationship between the lesion category and score was examined using a linear regression analysis model, and the receiver operating characteristic (ROC) analysis was used to assess whether the scores represent UAN-like characteristics. Results EIRL_BA showed a total of 203 candidates (an average of 1.73/case) in UAN and/or ID cases and 98 candidates (an average of 0.80/case) in normal controls. For diagnosing either UAN/ID, EIRL_BA showed an overall sensitivity of 80%, specificity of 84.2%, and accuracy of 83.7%, resulting in the positive likelihood ratio of 5.0. For diagnosing UAN only, the overall sensitivity of 89.0, specificity of 82.6%, and accuracy of 83.2% resulting in the positive likelihood ratio of 5.1. In a linear regression analysis, the scores significantly increased in the candidates’ first and second ranks in UAN (p < 0.05) but not in ID. An ROC analysis using the score for diagnosing UAN showed an area under the curve of 0.836. Conclusion EIRL_BA is applicable for detecting small UAN, and the CNN’s final layer scores may be an effective index for discriminating UAN and ID and representing the likelihood of UAN.

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Fairness of artificial intelligence in healthcare: review and recommendations

Ueda¹,

Kakinuma²,

Fujita

et al. 2023

Jpn J Radiol

122

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In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

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Computer-aided detection of cerebral aneurysms with magnetic resonance angiography: usefulness of volume rendering to display lesion candidates

Cited by 4 publications

References 23 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

Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN’s final layer for distinguishing between aneurysm and infundibular dilatation

Fairness of artificial intelligence in healthcare: review and recommendations

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