Deep Learning-based Automated Detection of Pulmonary Embolism
The results showed that the chest pain package of the Canon Automation Platform accurately provides rapid automatic PE detection in chest CTPAs by leveraging deep learning algorithms to facilitate the clinical workflow. This study demonstrates that AI can provide physicians with robust diagnostic support for acute PE, particularly in hospitals without 24/7 access to radiology specialists.
Authors
- Züleyha Bingöl (ORCID: https://orcid.org/0000-0002-1414-617X)
- Ravi Shrestha (ORCID: https://orcid.org/0000-0003-2324-7658)
- Gülfer Okumuş (ORCID: https://orcid.org/0000-0001-8309-1216)
- Görkem Durak (ORCID: https://orcid.org/0000-0002-1608-1955)
- Şükrü Mehmet Ertürk (ORCID: https://orcid.org/0000-0003-4086-675X)
- Emre Uysal (ORCID: https://orcid.org/0000-0002-4737-4304)
- Alpay Medetalibeyoğlu (ORCID: https://orcid.org/0000-0002-3024-4036)
- Ülkü Durak
- Ahmet Yasin Karkaş (ORCID: https://orcid.org/0009-0005-2389-9113)
- Önder Babacan
Institutions
- Canon (Japan) (JP)
- Istanbul Eye Hospital (TR)
- Istanbul University (TR)
Publication Details
- Journal
- Journal of Computer Assisted Tomography
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1097/rct.0000000000001803
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00