We aimed to improve the diagnostic accuracy of automatic myocardial perfusion SPECT (MPS) interpretation analysis for prediction of coronary artery disease (CAD) by integrating several quantitative perfusion and functional variables for non-corrected (NC) data by support vector machines (SVM) a computer method for machine learning. and rest were derived by quantitative software. Rab21 The SVM was trained using a group of 125 pts (25 LLK 25 0 25 1 25 2 and 25 3-vessel CAD) using above quantitative variables and second order polynomial fitting. The remaining patients (N = 832) were categorized based on probability estimates with CAD defined as (probability estimate ≥ 0.50). The diagnostic accuracy of SVM was also compared to visual segmental scoring by two experienced readers. Results Sensitivity of SVM (84%) was significantly better than ISCH ME-143 (75% < 0.05) and EFC (31% < 0.05). Specificity of SVM (88%) was significantly better than that of TPD (78% < 0.05) and EFC (77% < 0.05). Diagnostic accuracy of SVM (86%) was significantly better than TPD (81%) ISCH (81%) or EFC (46%) (< 0.05 for all those). The Receiver-operator-characteristic area-under-the-curve (ROC-AUC) for SVM (0.92) was significantly better than TPD (0.90) ISCH (0.87) and EFC (0.60) (p < 0.001 for all those). Diagnostic accuracy of SVM was comparable to the overall accuracy of both visual readers (85% vs. 84% < 0.05). ROC-AUC for SVM (0.92) was significantly better than that of both visual readers (0.87 and 0.88 < 0.03). Conclusion Computational integration of quantitative perfusion and functional variables by SVM approach allows significant improvement of diagnostic accuracy of MPS and can significantly outperform visual assessment based on ROC analysis. ) are mapped into a n-dimensional feature space by the kernel functions. Kernel functions other than linear allow non-linear class boundaries. Mathematically any kernel function is usually defined by < 0.05). The sensitivity however was comparable between the SVM (84%) and TPD (85%) analysis. When ISCH was compared to the SVM analysis the sensitivity and accuracy of SVM was significantly higher than that ME-143 for TPD (< 0.05). The specificity however was comparable between the SVM and ISCH analysis. The sensitivity specificity and accuracy of SVM were higher than EFC (< 0.05). The ROC curves comparing TPD ISCH EFC and SVM probability estimates are shown in Physique 2. Table 2 also demonstrates the number of patients in whom the diagnosis was correctly changed based using SVM versus TPD alone. In the majority of cases one or both of the other factors (ISCH and EFC) established the correct diagnosis. The ROC-AUC for SVM probability estimates (0.92) was significantly better (< 0.001 for all those) versus TPD (0.90) ISCH (0.87) and EFC (0.60). Physique 1 Sensitivity specificity and accuracy of Support Vector Machines (SVM) versus Total Perfusion Deficit (TPD) Ischemic Change (ISCH) and Ejection Fraction Change (EFC) for detection of ≥70% coronary artery lesions. Red indicates significant difference ... Physique 2 The Receiver Operating Characteristic (ROC) curves comparing the Support Vector Machines (SVM) and Total Perfusion Deficit (TPD) Ischemic Change (ISCH) Motion and Thickening Change (MTC) and Ejection Fraction Change (EFC) for detection of ≥70% ... TABLE 2 Number of times the diagnosis was correctly changed when using SVM versus TPD The sensitivity specificity and diagnostic accuracy of SVM using linear kernel function (d=1) for detection of > 70% CAD on per-patient basis. The sensitivity was 89% the specificity was 77% and the overall diagnostic accuracy was 82%. When comparing polynomial SVM to linear SVM the diagnostic accuracy and specificity were significantly higher (< 0.05) while the sensitivity was significantly lower (= 0.046). We also assessed the sensitivity specificity and diagnostic accuracy of SVM by combining quantitative perfusion (TPD and ISCH) and ME-143 functional variables regional MTC and absolute stress EDV and ESV which are shown in Table 3. The sensitivity of quantitative perfusion with MTC and absolute volumes significantly decreased ME-143 while the specificity significantly improved when compared to the combined method using quantitative and changes in EF with accuracy remaining approximately the same. In addition the ROC-AUC was also not significantly different. TABLE 3 Comparison of SVM combining quantitative perfusion with different functional parameters SVM versus Visual Analysis Physique 3 compares the sensitivity specificity and accuracy of SVM versus to readers.