Application of a deep learning algorithm in the detection of hip fractures

Gao, Yan; Soh, Nicholas Yock Teck; Li, Nan; Lim, Gilbert; Ting, Daniel Shu Wei; Cheng, Lionel Tim-Ee; Wong, Kang Min; Liew, Charlene; Oh, Hong Choon; Tan, Jerry; Venkataraman, Narayan; Goh, Siang Hiong; Yan, Yet Yen

doi:10.1016/j.isci.2023.107350

Cited by 8 publications

(12 citation statements)

References 47 publications

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“…Convolutional neural networks attained over 90% sensitivity and specificity for detecting solid organ abdominal trauma like spleen, liver, and kidney lesions on CT scans. 9 Additional models achieved up to 97% accuracy in diagnosing distal radius fractures on radiographs, 11 98% sensitivity for hip fractures on pelvic X-rays, 14 and AUC exceeding 0.80 for intracranial hemorrhage detection on head CT scans. 19 Deep learning also shows precision in localizing traumatic findings, with activation mapping techniques precisely pinpointing 95.9% of hip fracture lesions 14 and models consistently highlighting displaced ribs on chest CTs.…”

Section: Resultsmentioning

confidence: 98%

“… The model, which uses 176 potential injuries for its predictions, showed excellent calibration for predicting facility discharge but requires further study to evaluate its effectiveness at scale. Diagnostics [ 14 ] Gao, Soh, Liu, Lim, Ting, Cheng, Wong, Liew, Oh, Tan, Venkataraman, Goh, Yan Application of a deep learning algorithm in the detection of hip fractures. Observational Diagnostic Accuracy/Retrospective Cohort Singapore A deep convolutional neural network (DCNN) demonstrated high accuracy (91%) and sensitivity (98%) in detecting hip fractures on plain frontal pelvic radiographs (PXRs), with a low false-negative rate (2%) and an area under the receiver operating characteristic curve (AUC) of 0.98.…”

Section: Resultsmentioning

confidence: 99%

“…Most of the investigated studies demonstrated that CNNs can achieve exceptional diagnostic accuracy, with sensitivity and specificity exceeding 90% for diverse traumatic injuries identifiable on CT, X-ray, and MRI. 9 , 11 , 14 , 19 , 24 However, reliance on single-center, retrospective data risks optimism bias and overfitting, requiring further multi-institutional prospective validation encompassing diverse patient populations and scanners before responsible clinical integration. 9 , 11 , 14 , 19 , 21 , 24 Additional gaps emerge in standardized injury quantification and localization.…”

Section: Discussionmentioning

confidence: 99%

“… 9 , 11 , 14 , 19 , 24 However, reliance on single-center, retrospective data risks optimism bias and overfitting, requiring further multi-institutional prospective validation encompassing diverse patient populations and scanners before responsible clinical integration. 9 , 11 , 14 , 19 , 21 , 24 Additional gaps emerge in standardized injury quantification and localization. Injury severity scoring models demonstrate comparable performance to validated classifications like ISS but currently suffer from mortality overestimation, requiring refinements to improve calibration.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review

Ventura,

Denton,

David

2024

MDER

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This study aimed to analyze the use of generative artificial intelligence in the emergency trauma care setting through a brief scoping review of literature published between 2014 and 2024. An exploration of the NCBI repository was performed using a search string of selected keywords that returned N=87 results; articles that met the inclusion criteria (n=28) were reviewed and analyzed. Heterogeneity sources were explored and identified by a significance threshold of P < 0.10 or an I 2 value exceeding 50%. If applicable, articles were categorized within three primary domains: triage, diagnostics, or treatment. Findings suggest that CNNs demonstrate strong diagnostic performance for diverse traumatic injuries, but generalized integration requires expanded prospective multi-center validation. Injury scoring models currently experience calibration gaps in mortality quantification and lesion localization that can undermine clinical utility by permitting false negatives. Triage predictive models now confront transparency, explainability, and healthcare ecosystem integration barriers limiting real-world translation. The most significant literature gap centers on treatment-oriented generative AI applications that provide real-time guidance for urgent trauma interventions rather than just analytical support.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review

Ventura,

Denton,

David

2024

MDER

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“…In fracture determination, DCNN has a proven ability to detect fractures with expert-level accuracy. 18 …”

Section: Introductionmentioning

confidence: 99%

The ensemble artificial intelligence (AI) method: Detection of hip fractures in AP pelvis plain radiographs by majority voting using a multi-center dataset

Beyaz,

Yayli,

Kılıc

et al. 2023

DIGITAL HEALTH

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Introduction This article was undertaken to explore the potential of AI in enhancing the diagnostic accuracy and efficiency in identifying hip fractures using X-ray radiographs. In the study, we trained three distinct deep learning models, and we utilized majority voting to evaluate their outcomes, aiming to yield the most reliable and precise diagnoses of hip fractures from X-ray radiographs. Methods An initial study was conducted of 10,849 AP pelvis X-rays obtained from five hospitals affiliated with Başkent University. Two expert orthopedic surgeons initially labeled 2,291 radiographs as fractures and 8,558 as non-fractures. The algorithm was trained on 6,943 (64%) radiographs, validated on 1,736 (16%) radiographs, and tested on 2,170 (20%) radiographs, ensuring an even distribution of fracture presence, age, and gender. We employed three advanced deep learning architectures, Xception (Model A), EfficientNet (Model B), and NfNet (Model C), with a final decision aggregated through a majority voting technique (Model D). Results For each model, we achieved the following metrics: For Model A: F1 Score 0.895, Accuracy 0.956, Specificity 0.973, Sensitivity 0.893. For Model B: F1 Score 0.900, Accuracy 0.960, Specificity 0.991, Sensitivity 0.845. For Model C: F1 Score 0.919, Accuracy 0.966, Specificity 0.984, Sensitivity 0.899. For Model D: F1 Score 0.929, Accuracy 0.971, Specificity 0.991, Sensitivity 0.897. We concluded that Model D (majority voting) achieved the best results in terms of the F1 score, accuracy, and specificity values. Conclusions Our study demonstrates that the results obtained by aggregating the decisions of multiple models through voting, rather than relying solely on the decision of a single algorithm, are more consistent. The practical application of these algorithms will be difficult due to ethical, legal, and confidentiality issues, despite the theoretical success achieved. Developing successful algorithms and methodologies should not be viewed as the ultimate goal; it is important to understand how these algorithms will be used in real-life situations. In order to achieve more consistent results, feedback from clinical practice will be helpful.

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