OP1 A novel coronary computed tomography angiography deep-learning methodology for coronary atheroma assessment trained using near-infrared spectroscopy-intravascular ultrasound

<h3>Objective</h3> We developed and validated a novel deep-learning (DL)-methodology for coronary CT angiography (CCTA) plaque quantification and characterisation trained using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). <h3>Methods</h3> Seventy patients with stable angina were prospectively recruited, and underwent 3^rd generation, dual-source CCTA and 3-vessel NIRS-IVUS imaging (NCT03556644). The lumen and vessel wall contours on CCTA and NIRS-IVUS were manually drawn and matched using in-house developed software. A novel DL convolutional neural network was trained using the NIRS-IVUS annotations as the ground truth in 138 vessels and applied to automatically segment a test set of 48 vessels and its estimations were compared with the estimations of NIRS-IVUS and the experts. <h3>Results</h3> The DL-methodology was superior to experts for estimating lumen volume (ICC 0.980 vs 0.938), vessel volume (ICC 0.991 vs 0.829), total atheroma volume (ICC 0.969 vs 0.520), percentage atheroma volume (ICC 0.786 vs 0.463) and lipid core burden index (LCBI; ICC 0.794 vs 0.492, all p&lt;0.001). The DL-methodology estimations of calcific burden index (CaBI) were also superior compared to experts without statistical significance (ICC 0.931 vs 0.745, p=0.140). In addition, the DL-methodology was superior for morphological assessment of lumen area (dice similarity coefficient (DSC) 0.86±0.04 vs 0.82±0.03, hausdorff distance (HD) 0.41±0.10 vs 0.45±0.11 and mean distance 0.20±0.06 vs 0.24±0.07, all p&lt;0.001) and vessel area (DSC 0.86±0.04 vs 0.73±0.06, HD 0.47±0.10 vs 0.80±0.17 and mean distance 0.23±0.05 vs 0.48±0.12, all p&lt;0.001). <h3>Conclusion</h3> Our novel CCTA DL-methodology trained on NIRS-IVUS enables accurate and rapid coronary plaque quantification and characterisation.