Quantitative Prediction of SYNTAX Score for Cardiovascular Artery Disease Patients via the Inverse Problem Algorithm Technique as Artificial Intelligence Assessment in Diagnostics

Abstract

The quantitative prediction of the SYNTAX score for cardiovascular artery disease patients using the inverse problem algorithm (IPA) technique in artificial intelligence was explored in this study. A 29-term semi-empirical formula was defined according to seven risk factors: (1) age, (2) mean arterial pressure, (3) body surface area, (4) pre-prandial blood glucose, (5) low-density-lipoprotein cholesterol, (6) Troponin I, and (7) C-reactive protein. Then, the formula was computed via the STATISTICA 7.0 program to obtain a compromised solution for a 405-patient dataset with a specific loss function [actual-predicted]² as low as 3.177, whereas 0.0 implies a 100% match between the prediction and observation via "the lower, the better" principle. The IPA technique first created a data matrix [405 × 29] from the included patients' data and then attempted to derive a compromised solution of the column matrix of 29-term coefficients [29 × 1]. The correlation coefficient, r², of the regression line for the actual versus predicted SYNTAX score was 0.8958, showing a high coincidence among the dataset. The follow-up verification based on another 105 patients' data from the same group also had a high correlation coefficient of r² = 0.8304. Nevertheless, the verified group's low derived average AT (agreement) (AT_avg = 0.308 ± 0.193) also revealed a slight deviation between the theoretical prediction from the STATISTICA 7.0 program and the grades assigned by clinical cardiologists or interventionists. The predicted SYNTAX scores were compared with earlier reported findings based on a single-factor statistical analysis or scanned images obtained by sonography or cardiac catheterization. Cardiologists can obtain the SYNTAX score from the semi-empirical formula for an instant referral before performing a cardiac examination.

Keywords: SYNTAX; artificial intelligence; cardiovascular artery disease; computational analysis; inverse problem algorithm.

Figure 1

Two scenarios showing how the SYNTAX score was graded in a cardiac examination. The SYNTAX score was graded as 47 (high) and 10 (low) for scenarios 1 and 2, respectively.

Figure 2

The flowchart of specific workloads illustrates how researchers apply the IPA technique in artificial intelligence.

Figure 3

A typical STATISTICA 7.0 program in function. The user must follow the suggested options and define the unique loss function to obtain the coefficient matrix according to the IPA technique.

Figure 4

The calculated outcomes from STATISTICA 7.0 program. The customized loss function equals 0.0 if there is a 100% match between theoretical prediction and practical observation, whereas the derived value is 3.177 in this study.

Figure 5

The actual and predicted SYNTAX scores for the original 405 patients and verified 105 patients, according to the STATISTICA 7.0-derived linear regression.

Figure 6

The distribution of 105 individual ATs in this study. As demonstrated, most ATs lie below 0.4, showing the convincing capability of the program to predict the SYNTAX score in reality.

Figure 7

The major three factors, age (30–90 yr), BSA (1.0–2.2 m²), and LDL-C (10–300 mg/dL), were preset as X-, Y-, and Z-axes, respectively, in this study. As clearly demonstrated, the SYNTAX score is high (>22) when LDL-C is higher than 100 mg/dL and becomes severe for high LDL-C (>250 mg/dL).