Deep learning in fracture detection: a narrative review

Kalmet, Pishtiwan H. S.; Sanduleanu, Sebastian; Primakov, Sergey; Wu, Guangyao; Jochems, Arthur; Ibrahim, Abdalla; Hulst, Luca V.; Lambin, Philippe; Poeze, Martijn

doi:10.1080/17453674.2019.1711323

Cited by 93 publications

(49 citation statements)

References 49 publications

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“…Second, we integrated the HAI system into the clinical flow and verified its use in real-world trauma bays. For orthopedic radiology, fracture detection with computed-aid diagnosis is one of the first applications of AI in radiologic imaging [ 32 , 33 ]. In this study, with the assistance of the HAI system, the physicians detected hip fractures with an increased diagnostic accuracy ranging from 2% to 22%.…”

Section: Discussionmentioning

confidence: 99%

A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study

Cheng¹,

Chen²,

Cheng³

et al. 2020

JMIR Med Inform

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Background Hip fracture is the most common type of fracture in elderly individuals. Numerous deep learning (DL) algorithms for plain pelvic radiographs (PXRs) have been applied to improve the accuracy of hip fracture diagnosis. However, their efficacy is still undetermined. Objective The objective of this study is to develop and validate a human-algorithm integration (HAI) system to improve the accuracy of hip fracture diagnosis in a real clinical environment. Methods The HAI system with hip fracture detection ability was developed using a deep learning algorithm trained on trauma registry data and 3605 PXRs from August 2008 to December 2016. To compare their diagnostic performance before and after HAI system assistance using an independent testing dataset, 34 physicians were recruited. We analyzed the physicians’ accuracy, sensitivity, specificity, and agreement with the algorithm; we also performed subgroup analyses according to physician specialty and experience. Furthermore, we applied the HAI system in the emergency departments of different hospitals to validate its value in the real world. Results With the support of the algorithm, which achieved 91% accuracy, the diagnostic performance of physicians was significantly improved in the independent testing dataset, as was revealed by the sensitivity (physician alone, median 95%; HAI, median 99%; P<.001), specificity (physician alone, median 90%; HAI, median 95%; P<.001), accuracy (physician alone, median 90%; HAI, median 96%; P<.001), and human-algorithm agreement [physician alone κ, median 0.69 (IQR 0.63-0.74); HAI κ, median 0.80 (IQR 0.76-0.82); P<.001. With the help of the HAI system, the primary physicians showed significant improvement in their diagnostic performance to levels comparable to those of consulting physicians, and both the experienced and less-experienced physicians benefited from the HAI system. After the HAI system had been applied in 3 departments for 5 months, 587 images were examined. The sensitivity, specificity, and accuracy of the HAI system for detecting hip fractures were 97%, 95.7%, and 96.08%, respectively. Conclusions HAI currently impacts health care, and integrating this technology into emergency departments is feasible. The developed HAI system can enhance physicians’ hip fracture diagnostic performance.

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Section: Discussionmentioning

confidence: 99%

A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study

Cheng¹,

Chen²,

Cheng³

et al. 2020

JMIR Med Inform

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“…A review of the application of AI systems to clinical decisions has been published. 12 An application of an AI system with two contour-based fracture detection schemes has been reported, 13 and the authors of this publication also used X-ray images. In total, 19 features could be extracted using their proposed approaches.…”

Section: Introductionmentioning

confidence: 99%

Investigation of bone fracture diagnosis system using transverse vibration response

Yoon

Woo

Sim

et al. 2021

Proc Inst Mech Eng H

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In this study, a new diagnostic system is developed to easily identify bone fractures in non-medical environments. It is difficult to determine the extent of cracks, fractures, and the healing process inside humans owing to the differences among people and limitations of state-of-the-art medical devices. Thus, various medical techniques, such as X-ray, computed tomography, or fork tuning systems have been developed, and more advanced technologies are emerging in the medical engineering field. In hazardous circumstances, medical devices to detect bone fracture are not available or cannot be easily applied. Thus, there is a need for the rapid detection of bone fractures without medical devices. To this end, this study analyzes the transverse vibration responses of bones because bone fractures cause different mechanical vibration reactions. By comparing the transverse vibration responses of both healthy and fractured bones, the modal assurance criterion can be calculated and applied to detect the existence of bone fractures. The transverse vibration responses at low and high frequencies are different and exhibit different modal assurance criteria depending on whether or not they are abnormal. Then, the virtual spectrogram of the differences between the signals from non-fractured and fractured bones is calculated. With the help of the present criterion with transverse vibration data, this difference can be analyzed quantitatively and effectively. To validate the proposed system, experiments with artificial specimens, animal legs, and a cadaver are performed.

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“…After that, it can quickly identify the special areas and pathological changes in the image, which provides doctors with a second choice [4][5][6]. The most popular area of research in AI are pattern detection and image interpretation, which are widely used in various conditions of orthopedics， including diagnosis of bone fracture [15], automatic detection of knee joints and quantification of knee osteoarthritis severity, assessment of bone strength and quality through evaluating trabecular bone microarchitecture [16][17]. Jakub et al firstly select 5 popular deep learning networks (BVLC Reference CaffeNet network, VGG CNN S network, VGG CNN and Network-in-network) to perform the classification of orthopedic trauma radiographs, and the results demonstrate that AI can do the job equal to or even better than humans [18].…”

Section: Introductionmentioning

confidence: 99%

Ground Truth Annotated Femoral X-Ray Image Dataset and Object Detection Based Method for Fracture Types Classification

Zhao

Shi³

et al. 2020

IEEE Access

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Precise classification of femoral fractures contributes to accurate surgical strategies and better prognosis after surgery. An effective and accurate system for diagnosing femoral fractures and classifying its types will play a vital role in clinical work. This work aims to achieve the automatic detection and classification of femoral fractures in X-ray images. We build a benchmark which includes 2333 X-ray images with 9 different fracture types, and each of them is manually labeled with the ground truth boxes indicating the femoral shaft fractures and its corresponding categories according to the Association for the Study of Internal Fixation (AO). An anchor-based Faster RCNN detection model, with the backbone of ResNet-50 being constructed in a multi-resolution feature pyramid networks (FPN), is used for locating fractures regions and classifying its types. The total image level accuracy reaches 71.5%, which is higher than some of the orthopedic surgeons can get, especially young orthopedic surgeons. Therefore, it is practicable to take advantage of artificial intelligence to detect and classify the femoral shaft fractures. INDEX TERMS orthopedic procedures; orthotics; image classification; image processing; image analysis I.

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Deep learning in fracture detection: a narrative review

Cited by 93 publications

References 49 publications

A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study

A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study

Investigation of bone fracture diagnosis system using transverse vibration response

Ground Truth Annotated Femoral X-Ray Image Dataset and Object Detection Based Method for Fracture Types Classification

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