Abstract 1097 Histopathology-Based Deep-Learning Predicts Atherosclerotic Lesions in Intravascular Imaging

Clin Res Cardiol (2023). https://doi.org/10.1007/s00392-023-02180-w
Histopathology-Based Deep-Learning Predicts Atherosclerotic Lesions in Intravascular Imaging
P. Nicol¹, T. Lenz¹, O. Holmber², V. Koch², A. Alyagoob¹, L. Utsch¹, A. Rank¹, E. Sabci¹, M. Seguchi¹, E. Xhepa¹, S. Kufner¹, S. Cassese³, A. Kastrati¹, C. Marr², M. Joner¹
¹Klinik für Herz- und Kreislauferkrankungen, Deutsches Herzzentrum München, München; ²Institute of Computational Biology, Helmholtz Zentrum München, München; ³Deutsches Herzzentrum München, München;
Background: Optical coherence tomography is a powerful modality to assess atherosclerotic lesions, but detecting lesions in high-resolution OCT is challenging and requires expert knowledge. Deep-learning algorithms can be used to automatically identify atherosclerotic lesions, facilitating identification of patients at risk. We trained a deep-learning algorithm (DeepAD) with co-registered, annotated histopathology to predict atherosclerotic lesions in optical coherence tomography (OCT). Methods: Two datasets were used for training DeepAD: (i) a histopathology data set from 7 autopsy cases with 62 OCT frames and co-registered histopathology for high quality manual annotation and (ii) a clinical data set from 51 patients with 222 OCT frames in which manual annotations were based on clinical expertise only. A U-net based deep convolutional neural network (CNN) ensemble was employed as an atherosclerotic lesion prediction algorithm. Results were analyzed using intersection over union (IOU) for segmentation. Results: DeepAD showed good performance regarding the prediction of atherosclerotic lesions, with a median IOU of 0.68 ± 0.18 for segmentation of atherosclerotic lesions. Detection of calcified lesions yielded an IOU = 0.34. When training the algorithm without histopathology-based annotations, a performance drop of >0.25 IOU was observed. The practical application of DeepAD was evaluated retrospectively in a clinical cohort (n = 11 cases), showing high sensitivity as well as specificity and similar performance when compared to manual expert analysis. Conclusion: Automated detection of atherosclerotic lesions in OCT is improved using a histopathology-based deep-learning algorithm, allowing accurate detection in the clinical setting. An automated decision-support tool based on DeepAD could help in risk prediction and guide interventional treatment decisions.
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