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    Can AI distinguish a bone radiograph from photos of flowers or cars? Evaluation of bone age deep learning model on inappropriate data inputs

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    Author
    Yi, Paul H
    Arun, Anirudh
    Hafezi-Nejad, Nima
    Choy, Garry
    Sair, Haris I
    Hui, Ferdinand K
    Fritz, Jan
    Date
    2021-08-05
    Journal
    Skeletal Radiology
    Publisher
    Springer Nature
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1007/s00256-021-03880-y
    http://www.ncbi.nlm.nih.gov/pmc/articles/pmc8339162/
    Abstract
    Objective: To evaluate the behavior of a publicly available deep convolutional neural network (DCNN) bone age algorithm when presented with inappropriate data inputs in both radiological and non-radiological domains. Methods: We evaluated a publicly available DCNN-based bone age application. The DCNN was trained on 12,612 pediatric hand radiographs and won the 2017 RSNA Pediatric Bone Age Challenge (concordance of 0.991 with radiologist ground-truth). We used the application to analyze 50 left-hand radiographs (appropriate data inputs) and seven classes of inappropriate data inputs in radiological (i.e., chest radiographs) and non-radiological (i.e., image of street numbers) domains. For each image, we noted if (1) the application distinguished between appropriate and inappropriate data inputs and (2) inference time per image. Mean inference times were compared using ANOVA. Results: The 16Bit Bone Age application calculated bone age for all pediatric hand radiographs with mean inference time of 1.1 s. The application did not distinguish between pediatric hand radiographs and inappropriate image types, including radiological and non-radiological domains. The application inappropriately calculated bone age for all inappropriate image types, with mean inference time of 1.1 s for all categories (p = 1). Conclusion: A publicly available DCNN-based bone age application failed to distinguish between appropriate and inappropriate data inputs and calculated bone age for inappropriate images. The awareness of inappropriate outputs based on inappropriate DCNN input is important if tasks such as bone age determination are automated, emphasizing the need for appropriate oversight at the data input and verification stage to avoid unrecognized erroneous results.
    Rights/Terms
    © 2021. ISS.
    Keyword
    Artificial intelligence
    Bone age
    Deep learning
    Quality
    Safety
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/16350
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00256-021-03880-y
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