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OBJECTIVES: To determine the feasibility of machine learning algorithms for the classification of appropriate collimation of the cranial and caudal borders in ventrodorsal and dorsoventral thoracic radiographs. SAMPLES: 900 ventrodorsal and dorsoventral canine and feline thoracic radiographs were retrospectively acquired from the Picture Archiving and Communication system (PACs) system of the Ontario Veterinary College. PROCEDURES: Radiographs acquired from April 2020 to May 2021 were labeled by 1 radiologist in Summer of 2022 as either appropriately or inappropriately collimated for the cranial and caudal borders. A machine learning model was trained to identify the appropriate inclusion of the entire lung field at both the cranial and caudal borders. Both individual models and a combined overall inclusion model were assessed based on the combined results of both the cranial and caudal border assessments. RESULTS: The combined overall inclusion model showed a precision of 91.21% (95% CI 91, 91.4), accuracy of 83.17% (95% CI 83, 83.4), and F1 score of 87% (95% CI 86.8, 87.2) for classification when compared with the radiologist's quality assessment. The model took on average 6 ± 1 second to run. CLINICAL RELEVANCE: Deep learning-based methods can classify small animal thoracic radiographs as appropriately or inappropriately collimated. These methods could be deployed in a clinical setting to improve the diagnostic quality of thoracic radiographs in small animal practice.
Tahghighi et al. (Tue,) studied this question.