Development of Semi-automatic Algorithms for the Echocardiographic Diagnosis of Posterior Systolic Curling.

Objective: Posterior systolic curling (PSC) is a morphological and functional abnormality of the posterior mitral valve annulus, known in the literature as a significant risk factor for the onset of malignant ventricular arrhythmias and sudden cardiac death. This study proposes a semi-automatic algorithm to acquire the Mitral valve annulus to Infero-basal wall Rotation Angle (MIRA) and an innovative semi-automatic echocardiographic parameter, called Mitral Annulus to Infero-Basal wall Angle (MAIBA) for diagnosing PSC.

Methods: Both algorithms leverage cardiac tissue tracking and biomedical image-processing techniques. They were tested on 100 patients (44 female, median age 58 years) and classified into PSC-positive and PSC-negative groups based on an initial eyeball estimation. All subjects underwent echocardiographic exams using parasternal long-axis view transthoracic echocardiography.

Results: The MIRA and MAIBA algorithms achieved Pearson correlation coefficients of 0.70 and 0.80, respectively, with respective percentage accuracies of 89% and 91%, respectively, supporting a strong correlation with manual measurements. Bland-Altman analyses confirmed agreement between semi-automatic and manual methods, and inter-observer reliability was excellent, with intra-class correlation coefficient values of 0.98 for MIRA and 0.97 for MAIBA. The optimal cutoff value for the MAIBA angle was set equal to 69° through receiver operator characteristic curve analysis and Youden's index. The diagnostic accuracy of PSC identification was 76% for MIRA and 85% for MAIBA, demonstrating robust potential for clinical application.

Conclusion: In conclusion, statistical analyses underscore MIRA and MAIBA angles as promising diagnostic tools, potentially enhancing clinicians' ability to identify PSC with high reliability and accuracy, paving the way for improved diagnostic support in cases of arrhythmogenic risk.