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Abstract

An automated classifier receives input data indicative of a prospective surgical patient's mediastinal volume, body surface area and gender, and provides a classifier output signal indicative of quality of fit of a totally implantable artificial heart in the chest cavity of the patient. The present invention assists the surgical team in making their preoperative decision regarding whether or not the totally implantable artificial heart will fit in the chest cavity of the candidate patient. The classifier may also receive as input parameters: the distance between the patient's spine at the sternum at the level of the pulmonary bifurcation; the distance between the spine and the sternum in a caudal heart slice; the maximum left to right distance dimension of the heart; the distance from the rightmost end of the heart to the left chest wall; and the maximum left to right dimension of the chest cavity. Each of the input signals to the classifier is multiplied by an associated regression coefficient, and the resultant products are summed to provide a dependent variable whose value is indicative of quality of fit. To ensure that the classifier provides an accurate measure of quality of fit, the classifier coefficients are developed using a training process, and preferably verified in a testing process.

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First Claim