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- Vetology AI Accurately Detects Pleural Effusion in Canine Thoracic Radiographs
Vetology AI Accurately Detects Pleural Effusion in Canine Thoracic Radiographs
VRU 63(5): 573-579
Study: The study investigates the performance of an AI algorithm (Vetology AI®) in detecting pleural effusion in thoracic radiographs of dogs.
Method: In this retrospective, diagnostic case–controlled study, 62 canine patients were recruited. A control group of 21 dogs with normal thoracic radiographs and a sample group of 41 dogs with confirmed pleural effusion were selected from the electronic medical records at the Cummings School of Veterinary Medicine. The images were cropped to include only the area of interest (i.e., thorax). The software then classified images into those with pleural effusion and those without.
Results: The AI algorithm was able to determine the presence of pleural effusion with 88.7% accuracy (P < 0.05). The sensitivity and specificity were 90.2% and 81.8%, respectively (positive predictive value, 92.5%; negative predictive value, 81.8%).
Conclusions: The application of this technology in the diagnostic interpretation of thoracic radiographs in veterinary medicine appears to be of value and warrants further investigation and testing.
Examples of radiographs incorrectly classified by the AI model as having pleural effusion. A, Left lateral (kVp 80, mAs 6.5) and B, ventrodorsal (kVp 90, mAs 6.5) radiographic projections of a dog without pleural effusion. Note the fat opacity ventral to the heart (arrow). The excessive fat accumulation in the mediastinum is mistakenly assigned a yes-effusion value, likely as the radiographic density of fat allowing visualization of the apex of the heart was not considered by the algorithm
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