The emergency radiology of pelvic trauma

Shenton, Ayeshea; Choudhary, Surabhi

doi:10.1177/1460408614545955

Cited by 2 publications

(1 citation statement)

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“…Triage plays a crucial role in differentiating patients into appropriate dispositions, and according to advanced trauma life support, it is the first step before the primary survey in managing severely wounded patients. The PXR is a low-cost tool that is essential for detecting possible fractures in trauma patients [ 44 ]. With the support of innovative information technology, we have developed AI algorithms capable of accurately classifying trauma patients within a constrained timeframe.…”

Section: Discussionmentioning

confidence: 99%

The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture Detection

et al. 2023

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(1) Background: Design thinking is a problem-solving approach that has been applied in various sectors, including healthcare and medical education. While deep learning (DL) algorithms can assist in clinical practice, integrating them into clinical scenarios can be challenging. This study aimed to use design thinking steps to develop a DL algorithm that accelerates deployment in clinical practice and improves its performance to meet clinical requirements. (2) Methods: We applied the design thinking process to interview clinical doctors and gain insights to develop and modify the DL algorithm to meet clinical scenarios. We also compared the DL performance of the algorithm before and after the integration of design thinking. (3) Results: After empathizing with clinical doctors and defining their needs, we identified the unmet need of five trauma surgeons as “how to reduce the misdiagnosis of femoral fracture by pelvic plain film (PXR) at initial emergency visiting”. We collected 4235 PXRs from our hospital, of which 2146 had a hip fracture (51%) from 2008 to 2016. We developed hip fracture DL detection models based on the Xception convolutional neural network by using these images. By incorporating design thinking, we improved the diagnostic accuracy from 0.91 (0.84–0.96) to 0.95 (0.93–0.97), the sensitivity from 0.97 (0.89–1.00) to 0.97 (0.94–0.99), and the specificity from 0.84 (0.71–0.93) to 0.93(0.990–0.97). (4) Conclusions: In summary, this study demonstrates that design thinking can ensure that DL solutions developed for trauma care are user-centered and meet the needs of patients and healthcare providers.

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Section: Discussionmentioning

confidence: 99%