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Cited by 41Bone health and osteoporosis researchArtificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.

Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures

Open access
Schilcher, Jörg, Nilsson, Alva, Andlid, Oliver, Eklund, Anders·2024·Computers in Biology and Medicine
DOI 10.1016/j.compbiomed.2023.107704

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Abstract

Atypical femur fractures (AFF) represent a very rare type of fracture that can be difficult to discriminate radiologically from normal femur fractures (NFF). AFFs are associated with drugs that are administered to prevent osteoporosis-related fragility fractures, which are highly prevalent in the elderly population. Given that these fractures are rare and the radiologic changes are subtle currently only 7% of AFFs are correctly identified, which hinders adequate treatment for most patients with AFF. Deep learning models could be trained to classify automatically a fracture as AFF or NFF, thereby assisting radiologists in detecting these rare fractures. Historically, for this classification task, only imaging data have been used, using convolutional neural networks (CNN) or vision transformers applied to radiographs. However, to mimic situations in which all available data are used to arrive at a diagnosis, we adopted an approach of deep learning that is based on the integration of image data and tabular data (from electronic health records) for 159 patients with AFF and 914 patients with NFF. We hypothesized that the combinatorial data, compiled from all the radiology departments of 72 hospitals in Sweden and the Swedish National Patient Register, would improve classification accuracy, as compared to using only one modality. At the patient level, the area under the ROC curve (AUC) increased from 0.966 to 0.987 when using the integrated set of imaging data and seven pre-selected variables, as compared to only using imaging data. More importantly, the sensitivity increased from 0.796 to 0.903. We found a greater impact of data fusion when only a randomly selected subset of available images was used to make the image and tabular data more balanced for each patient. The AUC then increased from 0.949 to 0.984, and the sensitivity increased from 0.727 to 0.849. These AUC improvements are not large, mainly because of the already excellent performance of the CNN (AUC of 0.966) when only images are used. However, the improvement is clinically highly relevant considering the importance of accuracy in medical diagnostics. We expect an even greater effect when imaging data from a clinical workflow, comprising a more diverse set of diagnostic images, are used.

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  • Schilcher et al. (2024) included a multicentre dataset of 72 Swedish radiology departments to develop a multimodal model based on X-ray imaging and clinical information for detecting atypical femur fractures.

    “Schilcher et al. (2024) included a multicentre dataset of 72 Swedish radiology departments to develop a multimodal model based on X-ray imaging and clinical information for detecting atypical femur fractures.”

    From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Schilcher et al., 2024

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Research works printed but not linked: 4

  1. Bradski, 2000, The OpenCV library, Dr. Dobb's J., 25, 120
  2. Neer, 1970, Displaced proximal humeral fractures. I. Classification and evaluation. The Journal of bone and joint surgery, Am. Vol., 52, 1077
  3. Chung, 2018, Automated detection and classification of the proximal humerus fracture by using deep learning algorithm, Acta Orthop., 89, 468, 10.1080/17453674.2018.1453714 · DOI
  4. Tiulpin, 2019, Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data, Sci. Rep., 9, 10.1038/s41598-019-56527-3 · DOI · Paper page

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Metadata

Authors
Schilcher, J., Nilsson, A., Andlid, O., & Eklund, A.
Affiliations
Linköping University (SE)
Corresponding author
Eklund, Anders
ORCID
Venue
Computers in Biology and Medicine
Publisher
Elsevier BV
Published
2024-01
Volume
168
Pages
107704
ISSN
0010-4825 (ISSN-L)
DOI
10.1016/j.compbiomed.2023.107704
OpenAlex
W4388635393
Cited by
41
References
51
Citations by year
2026: 17 · 2025: 17 · 2024: 7
FWCI
12.14
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers in Biology and Medicine (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DiVA (Linkoping University) (submittedVersion, other-oa)
Funders
  • Knut och Alice Wallenbergs Stiftelse
  • ITEA
  • Vetenskapsrådet (2023-01942)
  • Region Östergötland
  • VINNOVA (2021-01954)
Topics
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Bone health and osteoporosis research
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
atypical femoral fractures, fracture classification, multimodal fusion, electronic health records, radiographic images, osteoporosis drugs, fragility fractures, convolutional neural networks, vision transformer, fracture detection
MeSH terms
Deep Learning, Aged, Femoral Fractures, Femur, Humans, Radiography, Electronic Health Records, Osteoporotic Fractures

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