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Table 5 Multivariate analysis for the influencing factors associated with CNN’s classification on calcified plaques with motion artifacts

From: Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors

 

Inception v3

ResNet101

DenseNet201

Coefficient

p value

Coefficient

p value

Coefficient

p value

Density

0.033

< 0.001

0.024

< 0.001

0.319

< 0.001

CT vendor

0.012

0.147

− 0.025

0.091

− 0.038

0.102

Velocity

0.027

< 0.001

0.017

< 0.001

0.015

< 0.001

Dose

− 0.009

0.601

− 0.011

0.159

0.002

0.779

Reconstruction

0.009

0.126

0.010

0.112

0.012

0.099

  1. High, medium-1, medium-2, and low-density plaque were coded as 1–4, respectively, four CT systems (CT-A to CT-D) as 1–4; velocities from 0 to 60 mm/s coded as 0–6; dose level 40%, 80% and full dose coded as 1–3; recon method FBP, IR1 to IR3 coded as 1–4
  2. FBP filtered back projection, IR iterative reconstruction
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BMC Medical Imaging

ISSN: 1471-2342

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