Coronary artery decision algorithm trained by two-step machine learning algorithm

Figures

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  Fig. 1. Schematic of the two-step algorithm process.

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  Fig. 2. (Left) Examples of OCT, CT, and fusion images: (A) original CT image of patient (the ranges of CT image and OCT image measured are shown); (B) 3D model from CT with low resolution but with curvature; (C) OCT image with high resolution but without curvature; and (D) OCT-CR fusion image with both high resolution and curvature. (Right) Accuracy of FFR calculated by LBM method with each image types.

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  Fig. 3. Schematic of the two-step algorithm training process.

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  Fig. 4. Error percentage by data index aligned by FFREXP. The average error percentage was 27.46% when FFREXP < 0.75, 3.80% when 0.75 ≤ FFREXP < 0.85, and 12.70% when FFREXP ≥ 0.85.

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  Fig. 5. Relative weight factors for each feature.

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  Fig. 6. Correlation between each flow features and FFREXP. The position between −1 and 0 is the proximal lumen, that between 0 and 1 is the center lumen, and that between 1 and 2.5 is the distal lumen.

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  Fig. 7. Pressure, vorticity, and WSS of sample cases from each category.

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  Fig. 8. FFREXP, vorticity, and WSS of 20 cases separated into four categories.

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  Fig. 9. Correlation of each biometric feature.

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  Fig. 10. Biometric features of 20 cases separated into four categories.