Machine learning-based quality control of canine thoracic radiographs

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In animal hospitals and veterinary clinics, radiographs are taken by veterinary technicians and are often sent for a teleradiology consult by radiologists who are not present on-site. Turn around times for these studies range from 1 hour (STAT cases) to 2-3 days (non-emergency cases). Low-quality or poorly positioned radiographs may cause erroneous diagnosis and, in severe cases, may be classified as nondiagnostic by the radiologist. Therefore, quality control of the radiographs plays an important role in providing a reliable interpretation and convenient service to the patient. The training of personnel in proper positioning as well as modern x-ray units with wide exposure latitude and dynamic range help to reduce technical errors and increase quality. However, radiologists still receive radiographs with inappropriately collimated anatomy and inadequate positioning. As a result, there are a considerable number of canine thorax radiographs that render nondiagnostic or are limited in their diagnostic value. This increases the cost to veterinary clinics and clients, as well as increases radiation exposure to patients and personnel. Therefore, the importance of good image quality in radiographic studies to avoid the misleading or erroneous diagnosis, to reduce overall costs and to reduce radiation exposure, cannot be overstated. We propose a machine learning algorithm that analyzes the canine lateral thorax radiograph and evaluates its appropriateness before it is sent to the radiologist for diagnosis. We also aim to develop a sensor system that helps position the patient and reduce the room for technical error. The proposed work will benefit Canadian veterinarians as well as pet owners by 1) reducing the costs associated with repeating sedation and radiograph acquisition; 2) reducing the financial burden on the client to return to the veterinary clinic; 3) reducing technical staff availability; 3) eliminating roughly $2.5 variable cost per radiograph retake; and 4) eliminating the $50-100 telemedicine consultation fee per radiographic study if resubmitted for evaluation.