Deep learning for automated cerebral aneurysm detection on computed tomography images.

Xilei Dai, Lixiang Huang, Yi Qian, Shuang Xia, Winston Chong, Junjie Liu, Antonio Di Ieva, Xiaoxi Hou, Chubin Ou

April 2020 Int J Comput Assist Radiol Surg

Synopsis of Social media discussions

Discussions frequently mention how the model's rapid and reliable detection could transform clinical workflows, with phrases like 'game-changing' and references to faster diagnosis times, illustrating both high interest and perceived significant impact. The tone, which includes technical references and enthusiastic language, underscores deep engagement with the article's implications.

A
Agreement
 Moderate agreement

Most discussions express strong support for the potential benefits of the deep learning model based on its accuracy and speed.

I
Interest
 High level of interest

Posts demonstrate high curiosity about the technology and its implications for clinical practice.

E
Engagement
 High engagement

Multiple comments delve into technical details, possible improvements, and broader implications, showing deep engagement.

I
Impact
 High level of impact

Many posts highlight the revolutionary potential of automated aneurysm detection to improve diagnosis, indicating high perceived impact.

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Posts referencing the article

Automated Detection of Cerebral Aneurysms Using Deep Learning in Medical Imaging

Automated Detection of Cerebral Aneurysms Using Deep Learning in Medical Imaging

The study developed a deep learning model using the faster R-CNN architecture to automatically detect cerebral aneurysms in CT angiography images. It achieves high sensitivity for aneurysms larger than 3mm and offers rapid, reliable diagnosis across multiple hospitals.

einar camilo

August 2, 2020

36 views

  • Tim Leiner
    @MLandDL_papers (Twitter)

    Deep learning for automated cerebral aneurysm detection on computed tomography images. https://t.co/PpcOqgHqu5
    view full post

    February 16, 2020

Abstract Synopsis

  • The study developed a deep learning model using the faster R-CNN architecture to automatically detect cerebral aneurysms in CT angiography images, achieving high sensitivity especially for aneurysms larger than 3mm.
  • The model was trained and tested on images from multiple hospitals, showing consistent performance across different aneurysm sizes and locations, with detection times under 25 seconds per case.
  • This automated detection tool has the potential to significantly improve clinical diagnosis by providing accurate, quick, and reliable aneurysm identification, addressing the rising incidence of cerebrovascular aneurysms.]