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Deep learning-based algorithm for automatic detection of incidental pulmonary embolism on contrast-enhanced CT: a

Hana Farzaneh¹, Jacqueline Junn², Yasmina Chaibi³

¹Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, United States.

Radiology Advances

|October 8, 2025

Summary

An AI tool accurately detected incidental pulmonary embolism (iPE) on CT scans with 90% accuracy. This artificial intelligence solution provides rapid results, potentially improving detection speed and accuracy in clinical practice.

Keywords:

artificial intelligence contrast-enhanced CT deep learning diagnostic performance incidental pulmonary embolism

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence in Medicine

Background:

Incidental pulmonary embolism (iPE) is increasingly detected on contrast-enhanced computed tomography (CECT) scans performed for other indications.
AI offers potential to improve detection accuracy and timeliness of iPE.
Underreporting of iPE may occur due to the primary focus of diagnostic imaging studies.

Purpose of the Study:

To evaluate the diagnostic performance and triage effectiveness of a standalone AI solution for detecting iPE.
To assess the AI's ability to identify iPE in CECT exams not originally performed for PE evaluation.

Main Methods:

A deep learning-based software (CINA-iPE) was used to analyze CECT images for suspected iPE.
Retrospective CECTs from 5 clinical centers were collected, forming a balanced dataset.
A reference standard was established by three U.S. board-certified radiologists.

Main Results:

The AI achieved 87.8% sensitivity and 92.0% specificity for iPE detection, with an overall accuracy of 90.0%.
The system processed results within 1.5 minutes, enabling rapid notification.
Disagreements among radiologists occurred in 50% of false positive cases and 45.5% of missed PE cases, often involving complex findings.

Conclusions:

The CINA-iPE application demonstrated high accuracy in identifying incidental PE on non-PE CECT studies.
Rapid availability of AI-processed results can help prioritize interpretation and potentially enhance iPE detection.
The AI tool shows promise for improving the accuracy and speed of incidental pulmonary embolism detection.