Peer-reviewed veterinary case report
How AI compares to vets diagnosing dog heart lung fluid on X-rays
By Eunbee Kim et al.·Published in Veterinary Radiology & Ultrasound·2022·View original on Semantic Scholar →
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Original publication title: Comparison of artificial intelligence to the veterinary radiologist's diagnosis of canine cardiogenic pulmonary edema.
- Species:
- dog
Plain-English summary
A group of dogs with breathing problems was evaluated for cardiogenic pulmonary edema (a condition where fluid builds up in the lungs due to heart issues) using both artificial intelligence (AI) software and a veterinary radiologist's assessment of their X-rays. Out of 481 dogs, 46 were diagnosed with this condition by the radiologist, and the AI correctly identified 42 of those cases. The AI showed a high accuracy rate of over 92%, making it a helpful tool for vets when a radiologist isn't available. This suggests that AI can assist in diagnosing serious conditions quickly and effectively.
People also search for: dog breathing problems · cardiogenic pulmonary edema in dogs · AI for dog X-ray diagnosis
Abstract
Application of artificial intelligence (AI) to improve clinical diagnosis is a burgeoning field in human and veterinary medicine. The objective of this prospective, diagnostic accuracy study was to determine the accuracy, sensitivity, and specificity of an AI-based software for diagnosing canine cardiogenic pulmonary edema from thoracic radiographs, using an American College of Veterinary Radiology-certified veterinary radiologist's interpretation as the reference standard. Five hundred consecutive canine thoracic radiographs made after-hours by a veterinary Emergency Department were retrieved. A total of 481 of 500 cases were technically analyzable. Based on the radiologist's assessment, 46 (10.4%) of these 481 dogs were diagnosed with cardiogenic pulmonary edema (CPE+). Of these cases, the AI software designated 42 of 46 as CPE+ and four of 46 as cardiogenic pulmonary edema negative (CPE-). Accuracy, sensitivity, and specificity of the AI-based software compared to radiologist diagnosis were 92.3%, 91.3%, and 92.4%, respectively (positive predictive value, 56%; negative predictive value, 99%). Findings supported using AI software screening for thoracic radiographs of dogs with suspected cardiogenic pulmonary edema to assist with short-term decision-making when a radiologist is unavailable.
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Search related cases →Original publication on Semantic Scholar: https://www.semanticscholar.org/paper/35048445