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Deep Learning Approach for Automatic Wrist Fracture Detection Using Ultrasound Bone Probability Maps

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Abstract

Wrist fractures are currently examined using radiograms, resulting in undesirable radiation exposure in children. Ultrasound is fast, safe, and highly sensitive to fractures, making it ideally suited for wrist examination in emergency departments (ED). However, ultrasound images are difficult to interpret, resulting in high variability in assessment depending on the reader’s expertise. We developed a new machine learning (ML) technique to detect fractures from 3D ultrasound (3DUS). We generate bone probability maps using local phase (LP) information in each ultrasound frame, combine these into a feature sequence, and analyze the same to predict the probability of fracture using three variants of recurrent neural networks (RNN): vanilla RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) models. This approach was validated on 30 3DUS volumes, each of which was assessed by a radiologist for the presence of a fracture. RNN, LSTM and GRU gave 83%, 90%, and 87% accuracy when compared to clinical assessment by expert musculoskeletal radiologist, with GRU giving the most balanced sensitivity and specificity. The automatic assessment technique is reliable in detecting wrist fractures from 3D ultrasound and can be used as a valuable ED triage tool for fracture detection.

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Data Availability

The ultrasound data used in this study is not available for public access as institutional policy.

Code Availability

The source code is not available for public access in line with our IP policy.

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Acknowledgements

Dr. J.J is supported by the AHS Chair in Diagnostic Imaging and Canada CIFAR AI Chair, and his academic time is made available by the Medical Imaging Consultants (MIC), Edmonton, Canada. We acknowledge the support of the TD Ready Health and Alberta Machine Intelligence Institute (AMII) for funding this project, Alberta Emergency Strategic Clinical Network and Alberta Innovates for clinical scanning, and Compute Canada in providing us with computational resources including high-power graphical processing units (GPU) that were used for training and testing our deep learning models.

Funding

This research was funded by the TD Ready Health Grant and Alberta Machine Intelligence Institute (AMII).

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Authors and Affiliations

  1. Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, T6G 2B7, Canada

    Abhilash Rakkunedeth Hareendranathan, Jack Zhang, Naveenjyote Boora & Jacob Jaremko

  2. Indian Institute of Technology, Palakkad, India

    Arpan Tripathi & Mahesh Raveendranatha Panicker

Authors
  1. Abhilash Rakkunedeth Hareendranathan
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  2. Arpan Tripathi
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  3. Mahesh Raveendranatha Panicker
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  4. Jack Zhang
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  5. Naveenjyote Boora
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  6. Jacob Jaremko
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Contributions

ARH: conceptualization, literature review, methodology, software development, data analysis, writing — original draft preparation. AT: software development, visualization, writing — review and editing. MRP: conceptualization, methodology, software development, writing — review and editing. JZ: data collection, conceptualization, writing — review and editing. NB: data collection, conceptualization, writing — review and editing JJ: project supervision, conceptualization, funding acquisition, writing — review and editing.

Corresponding author

Correspondence to Abhilash Rakkunedeth Hareendranathan.

Ethics declarations

Ethics Approval

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of University of Alberta Hospital.

Consent to Participate

Informed consent was obtained from all subjects involved in the study.

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All authors have agreed to publish this work in its current form.

Competing Interests

The authors of this manuscript declare relationships with the following companies: J. J was a co-founder of MEDO.ai Inc. — a company that develops AI-based solutions in medical ultrasound, which has since been acquired. Other authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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Hareendranathan, A.R., Tripathi, A., Panicker, M.R. et al. Deep Learning Approach for Automatic Wrist Fracture Detection Using Ultrasound Bone Probability Maps. SN Compr. Clin. Med. 5, 276 (2023). https://doi.org/10.1007/s42399-023-01608-8

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  • DOI: https://doi.org/10.1007/s42399-023-01608-8

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