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Abstract
Background The morphological assessment of cerebral aneurysms based on cerebral angiography is an essential step when planning strategy and device selection in endovascular treatment, but manual evaluation by human raters only has moderate interrater/intrarater reliability.
Methods We collected data for 889 cerebral angiograms from consecutive patients with suspected cerebral aneurysms at our institution from January 2017 to October 2021. The automatic morphological analysis model was developed on the derivation cohort dataset consisting of 388 scans with 437 aneurysms, and the performance of the model was tested on the validation cohort dataset consisting of 96 scans with 124 aneurysms. Five clinically important parameters were automatically calculated by the model: aneurysm volume, maximum aneurysm size, neck size, aneurysm height, and aspect ratio.
Results On the validation cohort dataset the average aneurysm size was 7.9±4.6 mm. The proposed model displayed high segmentation accuracy with a mean Dice similarity index of 0.87 (median 0.93). All the morphological parameters were significantly correlated with the reference standard (all P<0.0001; Pearson correlation analysis). The difference in the maximum aneurysm size between the model prediction and reference standard was 0.5±0.7 mm (mean±SD). The difference in neck size between the model prediction and reference standard was 0.8±1.7 mm (mean±SD).
Conclusion The automatic aneurysm analysis model based on angiography data exhibited high accuracy for evaluating the morphological characteristics of cerebral aneurysms.
- aneurysm
- angiography
- intervention
Data availability statement
Data are available upon reasonable request. The data that support the findings of this study are available, upon reasonable request, from the corresponding author.
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Data availability statement
Data are available upon reasonable request. The data that support the findings of this study are available, upon reasonable request, from the corresponding author.
Footnotes
Twitter @NMCancelliere, @gcharbonnier, @VitorMendesPer1
Contributors HN analyzed the data, performed labeling, performed the machine learning and statistical analysis, drafted the manuscript for intellectual content, and accept full responsibility for data integrity and accuracy of the data analysis. NMC designed and conceptualized the study, had a major role in the acquisition of data, and revised the manuscript for intellectual content. AR and GC analyzed the data, performed the labeling, and revised the manuscript for intellectual content. VC collected the data, analyzed the data, and revised the manuscript for intellectual content. JS and TM had a major role in the acquisition of data and revised the manuscript for intellectual content. VMP designed and conceptualized the study, had a major role in the acquisition of data, and revised the manuscript for intellectual content.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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