ArticlePediatric radiology2022
Esophageal discoid foreign body detection and classification using artificial intelligence.
Article in Pediatric radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- A Case Report of a Coin Stack Masquerading as a Button Battery.Journal of education & teaching in emergency medicine · 2026Article
- A Review of Button Battery Ingestions in Children-Diagnosis and Management.Children (Basel, Switzerland) · 2025Review
- Thoracoscopic retrieval of a rare migrated fish bone in the thorax: a case report with sequential CT evidence and literature review.Journal of cardiothoracic surgery · 2025Review
- Artificial intelligence in the detection of non-biological materials.Emergency radiology · 2024Review
- Two Stacked Coins Mimicking an Esophageal Button Battery: A Case Presentation and Review of the Literature.Cureus · 2023Article
- Improving detection of impacted animal bones on lateral neck radiograph using a deep learning artificial intelligence algorithm.Insights into imaging · 2023Article
- Secondary esophageal perforation rupture of ascending aorta 16 day accidently-swallowing button battery in a young child: A case report of esophagial foreign body and mini review.International journal of surgery case reports · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundEarly 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.
objectiveOur 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
methodsWe 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.
resultsThe 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.
conclusionArtificial intelligence models show promise in detecting and classifying esophageal discoid foreign bodies and could potentially be used to triage radiographs for radiologist interpretation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.