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Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis

  • Computed Tomography
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Background

Artificial intelligence (AI)-driven software has been developed and become commercially available within the past few years for the detection of intracranial hemorrhage (ICH) and chronic cerebral microbleeds (CMBs). However, there is currently no systematic review that summarizes all of these tools or provides pooled estimates of their performance.

Methods

In this PROSPERO-registered, PRISMA compliant systematic review, we sought to compile and review all MEDLINE and EMBASE published studies that have developed and/or tested AI algorithms for ICH detection on non-contrast CT scans (NCCTs) or MRI scans and CMBs detection on MRI scans.

Results

In total, 40 studies described AI algorithms for ICH detection in NCCTs/MRIs and 19 for CMBs detection in MRIs. The overall sensitivity, specificity, and accuracy were 92.06%, 93.54%, and 93.46%, respectively, for ICH detection and 91.6%, 93.9%, and 92.7% for CMBs detection. Some of the challenges encountered in the development of these algorithms include the laborious work of creating large, labeled and balanced datasets, the volumetric nature of the imaging examinations, the fine tuning of the algorithms, and the reduction in false positives.

Conclusions

Numerous AI-driven software tools have been developed over the last decade. On average, they are characterized by high performance and expert-level accuracy for the diagnosis of ICH and CMBs. As a result, implementing these tools in clinical practice may improve workflow and act as a failsafe for the detection of such lesions.

Registration-URL

https://www.crd.york.ac.uk/prospero/ Unique Identifier: CRD42021246848.

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Availability of data and material

Submitted as supplemental material; Less lengthier tables are included in the manuscript.

Abbreviations

AI:

Artificial intelligence

ICH:

Intracranial hemorrhage

NCCT:

Non-contrast CT scan

IPH:

Intraparenchymal hemorrhage

IVH:

Intraventricular hemorrhage

SAH:

Subarachnoid hemorrhage

EDH:

Epidural hematoma

SDH:

Subdural hematoma

CMBs:

Chronic microbleeds

SN:

Sensitivity

SP:

Specificity

PPV:

Positive predictive value

NVP:

Negative predicted value

AUC:

Area under the curve

CNN:

Convolutional neural network

RNN:

Recurrent neural networks

ANN:

Artificial neural networks

2D:

2-Dimensional

3D:

3-Dimensional

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Acknowledgements

Work was conceptualized by S. Matsoukas and CK. S. Matsoukas and JC performed screening. S. Matsoukas conducted the analysis and generated the tables and graphs. S. Matsoukas drafted and developed Introduction, Methods, Results, and Discussion regarding ICH. CMB part of the discussion was drafted and developed by JS. S. Matsoukas and CK significantly edited the manuscript. Bias assessment was conducted by S Matsoukas and BRS. All authors approved the final version of the article.

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There were no funding sources for this study.

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Authors and Affiliations

  1. Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA

    Stavros Matsoukas, Jacopo Scaggiante, Braxton R. Schuldt, Colton J. Smith, Susmita Chennareddy, Roshini Kalagara, Shahram Majidi, Joshua B. Bederson, Johanna T. Fifi, J. Mocco & Christopher P. Kellner

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  1. Stavros Matsoukas
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  2. Jacopo Scaggiante
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Corresponding author

Correspondence to Stavros Matsoukas.

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Conflicts of interest

J Mocco is the PI on research trials funded by: Stryker Neurovascular, Microvention, Penumbra, and Genentech and he is an investor in: Cerebrotech, Imperative Care, Endostream, Viseon, BlinkTBI, Myra Medical, Serenity, Vastrax, NTI, RIST, Viz.ai, Synchron, Radical, and Truvic. He serves, or has recently served, as a consultant for: Imperative Care, Cerebrotech, Viseon, Endostream, Vastrax, RIST, Synchron, Viz.ai, Perflow, and CVAid. Cristopher Kellner is the PI on research trials supported by Penumbra, Integra Life Sciences, and Cerenovus; he has received research grants from Viz.AI, Penumbra, Integra LifeSciences, ICE Neurosystems, Minnetronix, Irras, Longeviti Neuro Solutions, Cerebrotech Medical Systems, and Siemens; he has an ownership stake in Borealis, Precision Recovery, and Metis Innovative. Metis Innovative is a venture capital group with investments in Synchron, Fluid Biomed, and Proprio.

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BRS has been added as an extra author (3rd), since he contributed significantly during the revision.

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Other Information: Registration and protocol: https://www.crd.york.ac.uk/prospero/ Unique Identifier: CRD42021246848.

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Matsoukas, S., Scaggiante, J., Schuldt, B.R. et al. Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis. Radiol med 127, 1106–1123 (2022). https://doi.org/10.1007/s11547-022-01530-4

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