Automatic assessment of calcified plaque and nodule by optical coherence tomography adopting deep learning model

Optical coherence tomography (OCT) has become the best imaging tool to assess calcified plaque and nodule. However, every OCT pullback has numerous images, and artificial analysis requires too much time and energy. Thus, it is unsuitable for clinical application. This study aimed to develop and validate an automatic assessment of calcified plaque and nodule by OCT using deep-learning model. The OCT images of calcified plaque and nodule were labeled by two expert readers based on the consensus. A deep-learning model with a MultiScale and MultiTask u-net network (MS-MT u-net) was developed. Then, with the ground truth labeled by expert readers as reference, the diagnostic accuracy and agreement of the model was validated. For the pixelwise evaluation of calcified plaque, the model had a high performance with precision (93.95%), recall (88.95%), and F1 score (91.38%). For the lesion-level evaluation of calcified plaque, the quantitative metrics by the model excellently correlated with the ground truth (calcium score, r = 0.90, p < 0.01; calcified volume, r = 0.99, p < 0.01). For calcified nodules, the model showed excellent diagnostic performance including sensitivity (91.7%), specificity (89.3%), and accuracy (91.0%). We developed a novel deep-learning model to identify the attributes of calcified plaque and nodule. This model provided excellent diagnostic accuracy and agreement with the ground truth, thereby reducing the subjectivity of manual measurements and substantially saving time. These findings can help practitioners efficiently adopt appropriate therapeutic strategies to treat calcified lesions.