Abstract

JACC

Lots of interesting abstracts and cases were submitted for TCTAP 2022. Below are the accepted ones after a thorough review by our official reviewers. Don’t miss the opportunity to expand your knowledge and interact with authors as well as virtual participants by sharing your opinion in the comment section!

TCTAP A-049

Automatic Detection of Calcified Lesion in Coronary Optical Coherence Tomography With Patch-Wise Classification Deep Learning Model

By Wei Chieh Huang, Yu-Chieh Cheng, Hsin-I Teng, Tse-Min Lu, Chung-Ming Chen

Presenter

Wei Chieh Huang

Authors

Wei Chieh Huang1, Yu-Chieh Cheng2, Hsin-I Teng3, Tse-Min Lu1, Chung-Ming Chen2

Affiliation

Taipei Veterans General Hospital, Taiwan1, National Taiwan University, Taiwan2, Taichung Veterans General Hospital, Taiwan3
View Study Report
TCTAP A-049
Imaging: Intravascular

Automatic Detection of Calcified Lesion in Coronary Optical Coherence Tomography With Patch-Wise Classification Deep Learning Model

Wei Chieh Huang1, Yu-Chieh Cheng2, Hsin-I Teng3, Tse-Min Lu1, Chung-Ming Chen2

Taipei Veterans General Hospital, Taiwan1, National Taiwan University, Taiwan2, Taichung Veterans General Hospital, Taiwan3

Background

BackgroundThe presence of extensive calcification is a primary concern when planning and implementing a vascular percutaneous intervention such as stenting. If the balloon does not expand, the interventionalist must blindly apply high balloon pressure, use an atherectomy device, or abort the procedure. Intravascular optical coherence tomography can evaluate extensive calcification and aid intervention planning, there we aim to develop a method for automatic detection of calcium in coronary IVOCT images.

Methods

patch-wise classification deep learning model was developed and trained. IVOCT pullbacks from 2 medical centers were analyzed in a core lab, annotating basic calcified components and other structures. We divided our dataset into training, validation, and test dataset. We used polar transform to split images with different angle to patches (30, 45, 60 and 90 degrees).Figure 1 showed we used polar transform to split our images and  we demonstrated a splited image with 45 degrees in figure 2.

Results

Annotated IVOCT images from 111 pullbacks (111 patients) were divided into 1871 frames of normal images and 3951 frames of calcified lesions (total frames: 5822 frames).We used polar transform to split images with different angle to patches. We found the images with 90 degrees splitting have better performance. The sensitivity was 0.901, the specificity was 0.8847 and the accuracy was 0.892.           

Conclusion

A novel artificial intelligence framework for automatic calcified lesion detection in IVOCT was developed, providing excellent diagnostic accuracy. This model might reduce subjectivity in image interpretation with potential applications in research and IVOCT-guided percutaneous coronary intervention with calcified lesions.                                                                                                                                                       

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