Development of an artificial intelligence-based algorithm for the detection of left atrial enlargement from feline thoracic radiographs.

Abstract

A heart-convolutional neural network (heart-CNN) was developed and tested for the automatic detection of left atrial enlargement (LAE) from feline thoracic radiographs. A retrospective and multicenter study was performed. Right lateral and dorso-ventral and/or ventro-dorsal thoracic radiographs of cats with concomitant echocardiographic examination were selected from the internal databases of both academic and private referral institutions. Radiographic images were classified as no LAE, mild, moderate and severe LAE, based on echocardiographic reports. Heart-CNN performance was evaluated using confusion matrices and receiver operating characteristic curves for both radiographic projections considering a multiclass and a binary classification. Considering the multiclass classification, for the right lateral view, the area under the curve (AUC) was of 0.73, 0.68, 0.64 and 0.78 for the no LAE, mild, moderate and severe LAE groups, respectively. The AUCs for the dorso-ventral and/or ventro-dorsal images were 0.73, 0.64, 0.63 and 0.76 for the no LAE, mild, moderate and severe LAE groups, respectively. In the binary classification, AUCs were 0.83 and 0.81 for right lateral and dorso-ventral and/or ventro-dorsal projections, respectively. The developed AI-based tool seems to be a promising support for automatic identification of more advanced stages of LAE in cats.