Clinical Review: Accuracy of AI Software for the Detection of Confirmed Pleural Effusion in Dogs

by | Apr 22, 2022 | Peer Reviews

Accuracy of Artificial Intelligence Software for the Detection of Confirmed Pleural Effusion in Thoracic Radiographs in Dogs

STUDY AUTHORS

Thiago Rinaldi Müller, Mauricio Solano, Mirian Harumi Tsunemi

Abstract

The use of artificial intelligence (AI) algorithms in diagnostic radiology is a developing area in veterinary medicine and may provide substantial benefit in many clinical settings. These range from timely image interpretation in the emergency setting when no boarded radiologist is available to allowing boarded radiologists to focus on more challenging cases that require complex medical decision making. Testing the performance of artificial intelligence (AI) software in veterinary medicine is at its early stages, and only a scant number of reports of validation of AI software have been published. 

The purpose of this study was to investigate the performance of an AI algorithm (Vetology AI®) in the detection of pleural effusion in thoracic radiographs of dogs. 

  • 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.
  • 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%).

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.

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Clinical Review: AI vs. Veterinary Radiologist on Canine Cardiogenic Pulmonary Edema

by | Jan 19, 2022 | Peer Reviews

Comparison of Artificial Intelligence to the Veterinary Radiologist's Diagnosis of Canine Cardiogenic Pulmonary Edema

STUDY AUTHORS

Eunbee Kim, Anthony J. Fischetti, Pratheev Sreetharan, Joel G. Weltman, Philip R. Fox

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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