Assessment of New Coronary Features on Quantitative Coronary Angiographic Images With Innovative Unsupervised Artificial Adaptive Systems: A Proof-of-Concept Study

Mauro Amato; Massimo Buscema; Giulia Massini; Guido Maurelli; Enzo Grossi; Beatrice Frigerio; Alessio L Ravani; Daniela Sansaro; Daniela Coggi; Cristina Ferrari; Antonio L Bartorelli; Fabrizio Veglia; Elena Tremoli; Damiano Baldassarre

doi:10.3389/fcvm.2021.730626

Assessment of New Coronary Features on Quantitative Coronary Angiographic Images With Innovative Unsupervised Artificial Adaptive Systems: A Proof-of-Concept Study

Front Cardiovasc Med. 2021 Oct 14:8:730626. doi: 10.3389/fcvm.2021.730626. eCollection 2021.

Authors

Mauro Amato¹, Massimo Buscema^{2

3}, Giulia Massini², Guido Maurelli², Enzo Grossi², Beatrice Frigerio¹, Alessio L Ravani¹, Daniela Sansaro¹, Daniela Coggi¹, Cristina Ferrari¹, Antonio L Bartorelli^{1

4}, Fabrizio Veglia¹, Elena Tremoli⁵, Damiano Baldassarre^{1

6}

Affiliations

¹ Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy.
² Semeion, Research Centre of Sciences of Communication, Rome, Italy.
³ Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, United States.
⁴ Department of Biomedical and Clinical Sciences "Luigi Sacco", University of Milan, Milan, Italy.
⁵ Maria Cecilia Hospital, Cotignola, Italy.
⁶ Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy.

Abstract

Background and Purpose: The Active Connection Matrixes (ACMs) are unsupervised artificial adaptive systems able to extract from digital images features of interest (edges, tissue differentiation, etc.) unnoticeable with conventional systems. In this proof-of-concept study, we assessed the potentiality of ACMs to increase measurement precision of morphological structures (e.g., stenosis and lumen diameter) and to grasp morphological features (arterial walls) from quantitative coronary angiography (QCA), unnoticeable on the original images. Methods: Archive images of QCA and intravascular ultrasound (IVUS) of 10 patients (8 men, age 69.1 ± 9.7 years) who underwent both procedures for clinical reasons were retrospectively analyzed. Arterial features derived from "IVUS images," "conventional QCA images," and "ACM-reprocessed QCA images" were measured in 21 coronary segments. Portions of 1-mm length (263 for lumen and 526 for arterial walls) were head-to-head compared to assess quali-quantitative between-methods agreement. Results: When stenosis was calculated on "ACM-reprocessed QCA images," the bias vs. IVUS (gold standard) did not improve, but the correlation coefficient of the QCA-IVUS relationship increased from 0.47 to 0.83. When IVUS-derived lumen diameters were compared with diameters obtained on ACM-reprocessed QCA images, the bias (-0.25 mm) was significantly smaller (p < 0.01) than that observed with original QCA images (0.58 mm). ACMs were also able to extract arterial wall features from QCA. The bias between the measures of arterial walls obtained with IVUS and ACMs, although significant (p < 0.01), was small [0.09 mm, 95% CI (0.03, 0.14)] and the correlation was fairly good (r = 0.63; p < 0.0001). Conclusions: This study provides proof of concept that ACMs increase the measurement precision of coronary lumen diameter and allow extracting from QCA images hidden features that mirror well the arterial walls derived by IVUS.

Keywords: artificial intelligence; atherosclerosis; computer-assisted; coronary angiography; diagnostic; image processing; ultrasonography.