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Esophageal discoid foreign body detection and classification using artificial intelligence
Bradley S Rostad1,2, Edward J Richer3,4, Erica L Riedesel3,4
1Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1405 Clifton Rd. NE, Atlanta, GA, 30322, USA. brostad@emory.edu.
Background:
Early and accurate radiographic diagnosis is required for the management of children with radio-opaque esophageal foreign bodies. Button batteries are some of the most dangerous esophageal foreign bodies and coins are among the most common. We hypothesized that artificial intelligence could be used to triage radiographs with esophageal button batteries and coins.
Objective:
Our primary objective was to train an object detector to detect esophageal foreign bodies, whether button battery or coin. Our secondary objective was to train an image classifier to classify the detected foreign body as either a button battery or a coin.
Materials And Methods:
We trained an object detector to detect button batteries and coins. The training data set for the object detector was 57 radiographs, consisting of 3 groups of 19 images each with either an esophageal button battery, esophageal coin or no foreign body. The foreign bodies were endoscopically confirmed, and the groups were age and gender matched. We then trained an image classifier to classify the detected foreign body as either a button battery or a coin. The training data set for the image classifier consisted of 19 radiographs of button batteries and 19 of coins, cropped from the object detector training data set. The object detector and image classifier were then tested on 103 radiographs with an esophageal foreign body, and 103 radiographs without a foreign body.
Results:
The object detector was 100% sensitive and specific for detecting an esophageal foreign body. The image classifier accurately classified all 6/6 (100%) button batteries in the testing data set and 93/95 (97.9%) of the coins. The remaining two coins were incorrectly classified as button batteries. In addition to these images with a single button battery or coin, there were two unique cases in the testing data set: a stacked button battery and coin, and two stacked coins, both of which were classified as coins.
Conclusion:
Artificial intelligence models show promise in detecting and classifying esophageal discoid foreign bodies and could potentially be used to triage radiographs for radiologist interpretation.
