Detecting pediatric wrist fractures using deep-learning-based object detection

Zech, John R.; Carotenuto, Giuseppe; Igbinoba, Zenas; Tran, Clement Vinh; Insley, Elena; Baccarella, Alyssa; Wong, Tony T.

doi:10.1007/s00247-023-05588-8

Cited by 18 publications

(4 citation statements)

References 34 publications

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“…However, models intended for fracture classification were not yet ready for clinical application. In line with previous findings Zech et al demonstrated high accuracy of pediatric wrist fractures using an objective-detection-based deep learning approach [ 21 ].…”

Section: Discussionsupporting

confidence: 89%

RETRACTED ARTICLE: Diagnostic power of ChatGPT 4 in distal radius fracture detection through wrist radiographs

Mert,

Stoerzer,

Brauer

et al. 2024

Arch Orthop Trauma Surg

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Distal radius fractures rank among the most prevalent fractures in humans, necessitating accurate radiological imaging and interpretation for optimal diagnosis and treatment. In addition to human radiologists, artificial intelligence systems are increasingly employed for radiological assessments. Since 2023, ChatGPT 4 has offered image analysis capabilities, which can also be used for the analysis of wrist radiographs. This study evaluates the diagnostic power of ChatGPT 4 in identifying distal radius fractures, comparing it with a board-certified radiologist, a hand surgery resident, a medical student, and the well-established AI Gleamer BoneView™. Results demonstrate ChatGPT 4’s good diagnostic accuracy (sensitivity 0.88, specificity 0.98, diagnostic power (AUC) 0.93), surpassing the medical student (sensitivity 0.98, specificity 0.72, diagnostic power (AUC) 0.85; p = 0.04) significantly. Nevertheless, the diagnostic power of ChatGPT 4 lags behind the hand surgery resident (sensitivity 0.99, specificity 0.98, diagnostic power (AUC) 0.985; p = 0.014) and Gleamer BoneView™(sensitivity 1.00, specificity 0.98, diagnostic power (AUC) 0.99; p = 0.006). This study highlights the utility and potential applications of artificial intelligence in modern medicine, emphasizing ChatGPT 4 as a valuable tool for enhancing diagnostic capabilities in the field of medical imaging.

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Section: Discussionsupporting

confidence: 89%

RETRACTED ARTICLE: Diagnostic power of ChatGPT 4 in distal radius fracture detection through wrist radiographs

Mert,

Stoerzer,

Brauer

et al. 2024

Arch Orthop Trauma Surg

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“…Previous researches investigated the feasibility of using deep learning to detect fractures on X-ray films and showed good results, which is consistent with our study ( Table 6 ). Zech et al ( 17 ) trained the Faster RCNN model for the detection of carpal fractures in children groups, and reached an accuracy of 88%, a sensitivity of 88%, and a specificity of 89%, and the use of this model was effective in improving the diagnostic ability of radiology trainees. Ashkani-Esfahani et al ( 32 ) trained two deep learning models, Inception V3 and Renet-50, for the detection of ankle fractures.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, deep learning has also been successfully applied to the identification and severity assessment of bone and joint lesions, such as knee joint lesions, osteoarthritis, and spinal degenerative lesions. In addition, some deep learning-based models have been used to assess bone age ( 17 ). Recently, more and more studies have shown that artificial intelligence models based on deep learning have great potential in fracture recognition, classification, segmentation, and visual interpretation ( 18 – 20 ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures

Zhang,

Li,

Lin

et al. 2023

Front. Med.

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ObjectivesTo explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method.MethodsA total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals.ResultsThe deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group.ConclusionThis deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency.

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“…Object detection models work by assigning the label class of the object and drawing a bounding box around it. This method provides location information to direct researchers to observe the detected event, such as a fracture [42]. Most object detection models also enable the detection of multiple objects of various classes, which can be useful when multiple events are of interest (Figure 2).…”

Section: Diagnostic Aimentioning

confidence: 99%

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy

Oeding,

Yang,

Sanchez‐Sotelo

et al. 2024

Knee surg. sports traumatol. arthrosc.

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Deep learning is a subset of artificial intelligence (AI) with enormous potential to transform orthopaedic surgery. As has already become evident with the deployment of Large Language Models (LLMs) like ChatGPT (OpenAI Inc.), deep learning can rapidly enter clinical and surgical practices. As such, it is imperative that orthopaedic surgeons acquire a deeper understanding of the technical terminology, capabilities and limitations associated with deep learning models. The focus of this series thus far has been providing surgeons with an overview of the steps needed to implement a deep learning‐based pipeline, emphasizing some of the important technical details for surgeons to understand as they encounter, evaluate or lead deep learning projects. However, this series would be remiss without providing practical examples of how deep learning models have begun to be deployed and highlighting the areas where the authors feel deep learning may have the most profound potential. While computer vision applications of deep learning were the focus of Parts I and II, due to the enormous impact that natural language processing (NLP) has had in recent months, NLP‐based deep learning models are also discussed in this final part of the series. In this review, three applications that the authors believe can be impacted the most by deep learning but with which many surgeons may not be familiar are discussed: (1) registry construction, (2) diagnostic AI and (3) data privacy. Deep learning‐based registry construction will be essential for the development of more impactful clinical applications, with diagnostic AI being one of those applications likely to augment clinical decision‐making in the near future. As the applications of deep learning continue to grow, the protection of patient information will become increasingly essential; as such, applications of deep learning to enhance data privacy are likely to become more important than ever before.Level of Evidence: Level IV.

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Detecting pediatric wrist fractures using deep-learning-based object detection

Cited by 18 publications

References 34 publications

RETRACTED ARTICLE: Diagnostic power of ChatGPT 4 in distal radius fracture detection through wrist radiographs

RETRACTED ARTICLE: Diagnostic power of ChatGPT 4 in distal radius fracture detection through wrist radiographs

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy

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