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Classification of left ventricular thrombi by their history of systemic embolization using pattern recognition of
Insights
Predicting peripheral embolization from left ventricular (LV) thrombi is crucial. Texture features from two-dimensional echocardiography (2DE) combined with clinical data can accurately identify patients at high risk for emboli.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Diagnosing left ventricular (LV) thrombi is possible with two-dimensional echocardiography (2DE).
- Factors predicting peripheral embolization from LV thrombi require further delineation.
- Understanding these factors is critical for preventing embolic events.
Purpose of the Study:
- To identify clinical and echocardiographic variables associated with peripheral embolization in patients with LV thrombi.
- To develop a predictive model for embolization risk using texture features from 2DE and clinical data.
Main Methods:
- Retrospective analysis of 38 patients with LV thrombi diagnosed by 2DE.
- Inclusion of clinical variables and 2DE texture features (e.g., run length, gray level statistics).
- Utilized an expert system (Expert Ease) for classification into emboli vs. no emboli groups.
Main Results:
- Eight patients experienced clinically apparent emboli (leg or brain) within a week of 2DE.
- Key predictive variables included run length, long runs emphasis, gray level difference statistics, gray level second-order statistics, and warfarin status.
- The developed model demonstrated a predicted accuracy of at least 96%.
Conclusions:
- Specific 2DE texture features and clinical data can accurately predict peripheral embolization in LV thrombi patients.
- This predictive model offers a valuable tool for risk stratification and management.
- Early identification of high-risk patients can guide preventative strategies.
Abstract:
Although one can diagnose left ventricular (LV) thrombi by two-dimensional echocardiography (2DE), the factors associated with peripheral embolization, given a 2DE with LV thrombi, have not been well delineated. Therefore we looked at 2DE and clinical variables that included texture features in the 2DE of 38 patients whose 2DE had LV thrombi and questioned these patients to see if clinical embolization had occurred in the 8.9 +/- 6.1 month (+/- SD) average follow-up period. Eight patients, four with acute myocardial infarction (AMI) and four with dilated LV and decreased LV systolic wall motion, had clinically apparent leg or brain emboli, whereas the remaining patients did not. Emboli occurred within a week of obtaining the 2DE in question. The variables considered were the age of the patient, the type of heart disease present, warfarin administration, exercise tolerance, standard M-mode measurements, LV dyssynergy by 2DE, clot size and mobility, and gray scale statistics which include run length, Sobel edge points followed by 50% gradient thresholding, gray level second-order statistics, offset 1 and gray level difference statistics, offset 1. The values of the variables were then entered into an expert system (Expert Ease) in order to achieve classification of patients into emboli versus no emboli groups, while using a minimal number of variables. The only variables that were needed included run length, long runs emphasis, gray level difference statistics (entropy, contrast, mean, and angular second moment), gray level second-order statistics (contrast), and warfarin status. When probability statistics were applied to this schema, its accuracy was predicted to be at least 96%.(ABSTRACT TRUNCATED AT 250 WORDS)