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Review
. 2023 Dec 12;6(1):tzad005.
doi: 10.1093/bjro/tzad005. eCollection 2024 Jan.

Commercially available artificial intelligence tools for fracture detection: the evidence

Cato Pauling  1 , Baris Kanber  2   3 , Owen J Arthurs  1   4   5 , Susan C Shelmerdine  1   4   5
Affiliations
Review

VSports - Commercially available artificial intelligence tools for fracture detection: the evidence

Cato Pauling et al. BJR Open. .

Erratum in

"V体育ios版" Abstract

Missed fractures are a costly healthcare issue, not only negatively impacting patient lives, leading to potential long-term disability and time off work, but also responsible for high medicolegal disbursements that could otherwise be used to improve other healthcare services VSports手机版. When fractures are overlooked in children, they are particularly concerning as opportunities for safeguarding may be missed. Assistance from artificial intelligence (AI) in interpreting medical images may offer a possible solution for improving patient care, and several commercial AI tools are now available for radiology workflow implementation. However, information regarding their development, evidence for performance and validation as well as the intended target population is not always clear, but vital when evaluating a potential AI solution for implementation. In this article, we review the range of available products utilizing AI for fracture detection (in both adults and children) and summarize the evidence, or lack thereof, behind their performance. This will allow others to make better informed decisions when deciding which product to procure for their specific clinical requirements. .

Keywords: artificial intelligence; commercial; fracture; imaging; machine learning; radiology. V体育安卓版.

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Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
This image demonstrates the results produced when using an artificial intelligence (AI) fracture detection tool by Gleamer, called BoneView. With this product, a summary image is sent to PACS, depicted in figure (A) which shows the number and type of pathologies detected on the radiograph. (B) A second image is also sent to PACS with bounding boxes and their associated labels (FRACT = fracture, DIS = dislocation) displayed as an “overlay” across the original radiographic image in question. In this example, the AI has flagged a fracture of the distal radius, with a scapholunate dislocation in a child. Image provided by Daniel Jones, Gleamer.
Figure 2.
Figure 2.
This image demonstrates the results produced when using an artificial intelligence (AI) fracture detection tool by AZMed, called Rayvolve. In this example, an oblique left wrist view (A) and DP wrist view (C) have been submitted for AI interpretation. The AI has flagged a fracture of the scaphoid and ulnar styloid (B, D) in a child by displaying bounding boxes as an “overlay” across the respective radiographic images. These are also sent to PACS for radiology reporter and clinician review. Image provided by Liza Alem, AZmed.
Figure 3.
Figure 3.
This image demonstrates an example of results produced when using an artificial intelligence (AI) fracture detection tool by Milvue, called Smarturgences. In this example, frog leg view of the pelvis in a child has been submitted for AI interpretation (A). The AI has correctly identified a fracture of the left anterior inferior iliac spine and placed a bounding box around the abnormality, as well as stating the pathology below the image (B).
See this image and copyright information in PMC

References

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    1. Shelmerdine SC, White RD, Liu H, Arthurs OJ, Sebire NJ.. Artificial intelligence for radiological paediatric fracture assessment: a systematic review. Insights Imaging. 2022;13(1):94. 10.1186/s13244-022-01234-3 - DOI - PMC - PubMed

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