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

Cheng, Chi‐Tung; Chen, Chih-Chi; Cheng, Fu-Jen; Chen, Huan‐Wu; Su, Yi-Siang; Yeh, Chun‐Nan; Chung, I‐Fang; Liao, Chien‐Hung

doi:10.2196/19416

Cited by 22 publications

(20 citation statements)

References 41 publications

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“…( 80,83,85,91 ) In addition, a recent study further validated one of the reviewed studies ( 78 ) and demonstrated how AI recommendations were able to improve the identification of hip fractures in clinical routine. ( 132 ) Prediction and diagnosis models increasingly prove to be more accurate than experts, expanding on the limits of human performance. This raises the ethical question whether AI decisions may have undesired side effects.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

101

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Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high‐dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12‐point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML‐based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model‐based decision‐making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).

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Section: Discussionmentioning

confidence: 99%

Machine Learning Solutions for Osteoporosis—A Review

Smets

Shevroja

Hügle

et al. 2020

Journal of Bone and Mineral Research

101

View full text Add to dashboard Cite

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“…Among the included studies, the architecture based on the GoogLeNet architectural model [ 7 , 11 , 18 ] or the DenseNet architectural model [ 13 , 14 , 20 ] was the most common with three each. Among the data input proportions, the study of Adams et al had the lowest training rate of 57% [ 11 ], and the study of Yamada et al had the largest training rate of 95% [ 19 ].…”

Section: Resultsmentioning

confidence: 99%

“…In 14 studies, 5 studies used Grad-CAM for highlight important regions. The information on AI for all included studies is presented in Table 3 [ 1 , 8 , 16 , 20 , 21 ].…”

Section: Resultsmentioning

confidence: 99%

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Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review

Cha

Kim

Park³

et al. 2022

J Orthop Surg Res

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Background In the emergency room, clinicians spend a lot of time and are exposed to mental stress. In addition, fracture classification is important for determining the surgical method and restoring the patient's mobility. Recently, with the help of computers using artificial intelligence (AI) or machine learning (ML), diagnosis and classification of hip fractures can be performed easily and quickly. The purpose of this systematic review is to search for studies that diagnose and classify for hip fracture using AI or ML, organize the results of each study, analyze the usefulness of this technology and its future use value. Methods PubMed Central, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched to identify relevant studies published up to June 2022 with English language restriction. The following search terms were used [All Fields] AND (", "[MeSH Terms] OR (""[All Fields] AND "bone"[All Fields]) OR "bone fractures"[All Fields] OR "fracture"[All Fields]). The following information was extracted from the included articles: authors, publication year, study period, type of image, type of fracture, number of patient or used images, fracture classification, reference diagnosis of fracture diagnosis and classification, and augments of each studies. In addition, AI name, CNN architecture type, ROI or important region labeling, data input proportion in training/validation/test, and diagnosis accuracy/AUC, classification accuracy/AUC of each studies were also extracted. Results In 14 finally included studies, the accuracy of diagnosis for hip fracture by AI was 79.3–98%, and the accuracy of fracture diagnosis in AI aided humans was 90.5–97.1. The accuracy of human fracture diagnosis was 77.5–93.5. AUC of fracture diagnosis by AI was 0.905–0.99. The accuracy of fracture classification by AI was 86–98.5 and AUC was 0.873–1.0. The forest plot represented that the mean AI diagnosis accuracy was 0.92, the mean AI diagnosis AUC was 0.969, the mean AI classification accuracy was 0.914, and the mean AI classification AUC was 0.933. Among the included studies, the architecture based on the GoogLeNet architectural model or the DenseNet architectural model was the most common with three each. Among the data input proportions, the study with the lowest training rate was 57%, and the study with the highest training rate was 95%. In 14 studies, 5 studies used Grad-CAM for highlight important regions. Conclusion We expected that our study may be helpful in making judgments about the use of AI in the diagnosis and classification of hip fractures. It is clear that AI is a tool that can help medical staff reduce the time and effort required for hip fracture diagnosis with high accuracy. Further studies are needed to determine what effect this causes in actual clinical situations.

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“…There have been some papers on the use of a deep learning algorithm to diagnose hip fractures. Some of these studies diagnose from antero-posterior images only [ 17 – 20 ], one from both antero-posterior and lateral images [ 21 ], one can predict not only presence of fractures but also fracture type [ 22 ], and one of the same algorithms can be used to diagnose both proximal femur and pelvic fractures [ 23 ] . Additionally, a previous study reported that deep learning algorithm improved the diagnostic accuracy of fracture detection by clinicians [ 20 , 22 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence improves the accuracy of residents in the diagnosis of hip fractures: a multicenter study

Sato

Takegami

Asamoto³

et al. 2021

BMC Musculoskelet Disord

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Background Less experienced clinicians sometimes make misdiagnosis of hip fractures. We developed computer-aided diagnosis (CAD) system for hip fractures on plain X-rays using a deep learning model trained on a large dataset. In this study, we examined whether the accuracy of the diagnosis of hip fracture of the residents could be improved by using this system. Methods A deep convolutional neural network approach was used for machine learning. Pytorch 1.3 and Fast.ai 1.0 were applied as frameworks, and an EfficientNet-B4 model (a pre-trained ImageNet model) was used. We handled the 5295 X-rays from the patients with femoral neck fracture or femoral trochanteric fracture from 2009 to 2019. We excluded cases in which the bilateral hips were not included within an image range, and cases of femoral shaft fracture and periprosthetic fracture. Finally, we included 5242 AP pelvic X-rays from 4851 cases. We divided these 5242 images into two images per image, and prepared 5242 images including fracture site and 5242 images without fracture site. Thus, a total of 10,484 images were used for machine learning. The accuracy, sensitivity, specificity, F-value, and area under the curve (AUC) were assessed. Gradient-weighted class activation mapping (Grad-CAM) was used to conceptualize the basis for the diagnosis of the fracture by the deep learning algorithm. Secondly, we conducted a controlled experiment with clinicians. Thirty-one residents;young doctors within 2 years of graduation from medical school who rotate through various specialties, were tested using 300 hip fracture images that were randomly extracted from the dataset. We evaluated the diagnostic accuracy with and without the use of the CAD system for each of the 300 images. Results The accuracy, sensitivity, specificity, F-value, and AUC were 96.1, 95.2, 96.9%, 0.961, and 0.99, respectively, with the correct diagnostic basis generated by Grad-CAM. In the controlled experiment, the diagnostic accuracy of the residents significantly improved when they used the CAD system. Conclusions We developed a newly CAD system with a deep learning algorithm from a relatively large dataset from multiple institutions. Our system achieved high diagnostic performance. Our system improved the diagnostic accuracy of residents for hip fractures. Level of evidence Level III, Foundational evidence, before-after study. Clinical relevance: high

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

Cited by 22 publications

References 41 publications

Machine Learning Solutions for Osteoporosis—A Review

Machine Learning Solutions for Osteoporosis—A Review

Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review

Artificial intelligence improves the accuracy of residents in the diagnosis of hip fractures: a multicenter study

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