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Pediatric Radiology
Published in: Pediatric Radiology 5/2021

01-05-2021 | Rickets | Original Article

Artificial intelligence research within reach: an object detection model to identify rickets on pediatric wrist radiographs

Authors: Karthik C. Meda, Sarah S. Milla, Bradley S. Rostad

Published in: Pediatric Radiology | Issue 5/2021

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Abstract

Background

Artificial intelligence models have been successful in analyzing ordinary photographic images. One type of artificial intelligence model is object detection, where a labeled bounding box is drawn around an area of interest. Object detection can be applied to medical imaging tasks.

Objective

To demonstrate object detection in identifying rickets and normal wrists on pediatric wrist radiographs using a small dataset, simple software and modest computer hardware.

Materials and methods

The institutional review board at Children’s Healthcare of Atlanta approved this study. The radiology information system was searched for radiographic examinations of the wrist for the evaluation of rickets from 2007 to 2018 in children younger than 7 years of age. Inclusion criteria were an exam type of “Rickets Survey” or “Joint Survey 1 View” with reports containing the words “rickets” or “rachitic.” Exclusion criteria were reports containing the words “renal,” “kidney” or “transplant.” Two pediatric radiologists reviewed the images and categorized them as either rickets or normal. Images were annotated by drawing a labeled bounding box around the distal radial and ulnar metaphases. The training dataset was created from images acquired from Jan. 1, 2007, to Dec. 31, 2017. This included 104 wrists with rickets and 264 normal wrists. This training dataset was used to create the object detection model. The testing dataset consisted of images acquired during the 2018 calendar year. This included 20 wrists with rickets and 37 normal wrists. Model sensitivity, specificity and accuracy were measured.

Results

Of the 20 wrists with rickets in the testing set, 16 were correctly identified as rickets, 2 were incorrectly identified as normal and 2 had no prediction. Of the 37 normal wrists, 33 were correctly identified as normal, 2 were incorrectly identified as rickets and 2 had no prediction. This yielded a sensitivity and specificity of 80% and 95% for wrists with rickets and 89% and 90% for normal wrists. Overall model accuracy was 86%.

Conclusion

Object detection can identify rickets on pediatric wrist radiographs. Object detection models can be developed with a small dataset, simple software tools and modest computing power.
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Metadata
Title
Artificial intelligence research within reach: an object detection model to identify rickets on pediatric wrist radiographs
Authors
Karthik C. Meda
Sarah S. Milla
Bradley S. Rostad
Publication date
01-05-2021
Publisher
Springer Berlin Heidelberg
Published in
Pediatric Radiology / Issue 5/2021
Print ISSN: 0301-0449
Electronic ISSN: 1432-1998
DOI
https://doi.org/10.1007/s00247-020-04895-8

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