Deep neural networks with promising diagnostic accuracy for the classification of atypical femoral fractures

Zdolsek, Georg; Chen, Yu-Pei; Bögl, Hans-Peter; Wang, Chunliang; Woisetschläger, Mischa; Schilcher, Jörg

doi:10.1080/17453674.2021.1891512

Cited by 7 publications

(11 citation statements)

References 32 publications

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“…In addition, Badgeley et al incorporated patient characteristics into the deep learning model to predict hip fractures ( 34 ). Zdolsek et al also identified atypical femoral fractures from normal femoral shaft fractures on conventional X-rays by deep learning models, and the ResNet had the best performance ( 35 ). Ankle fractures are considered as one of the most common fractures in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Feng

Zhou

Wang

et al. 2022

Front. Public Health

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BackgroundAs a research hotspot, deep learning has been continuously combined with various research fields in medicine. Recently, there is a growing amount of deep learning-based researches in orthopedics. This bibliometric analysis aimed to identify the hotspots of deep learning applications in orthopedics in recent years and infer future research trends.MethodsWe screened global publication on deep learning applications in orthopedics by accessing the Web of Science Core Collection. The articles and reviews were collected without language and time restrictions. Citespace was applied to conduct the bibliometric analysis of the publications.ResultsA total of 822 articles and reviews were finally retrieved. The analysis showed that the application of deep learning in orthopedics has great prospects for development based on the annual publications. The most prolific country is the USA, followed by China. University of California San Francisco, and Skeletal Radiology are the most prolific institution and journal, respectively. LeCun Y is the most frequently cited author, and Nature has the highest impact factor in the cited journals. The current hot keywords are convolutional neural network, classification, segmentation, diagnosis, image, fracture, and osteoarthritis. The burst keywords are risk factor, identification, localization, and surgery. The timeline viewer showed two recent research directions for bone tumors and osteoporosis.ConclusionPublications on deep learning applications in orthopedics have increased in recent years, with the USA being the most prolific. The current research mainly focused on classifying, diagnosing and risk predicting in osteoarthritis and fractures from medical images. Future research directions may put emphasis on reducing intraoperative risk, predicting the occurrence of postoperative complications, screening for osteoporosis, and identification and classification of bone tumors from conventional imaging.

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Section: Discussionmentioning

confidence: 99%

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Feng

Zhou

Wang

et al. 2022

Front. Public Health

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“…Research on utilizing AI for diagnosing AFF has been conducted extensively. Zdolsek et al employed transfer learning techniques, incorporating models such as ResNet50 and VGG19, achieving an impressive AUC accuracy of 0.94 for classifying normal femur factors (NFF) and AFF 21 . Similarly, other studies have successfully improved diagnostic accuracy, attaining an accuracy rate of 94.4% using transfer learning with models like VGG19 21 .…”

Section: Introductionmentioning

confidence: 99%

“…Zdolsek et al employed transfer learning techniques, incorporating models such as ResNet50 and VGG19, achieving an impressive AUC accuracy of 0.94 for classifying normal femur factors (NFF) and AFF 21 . Similarly, other studies have successfully improved diagnostic accuracy, attaining an accuracy rate of 94.4% using transfer learning with models like VGG19 21 . While these studies have demonstrated high accuracy in classifying complete AFF from NFF, it is crucial to diagnose AFF accurately, even in its early and incomplete stages.…”

Section: Introductionmentioning

confidence: 99%

Detection of incomplete atypical femoral fracture on anteroposterior radiographs via explainable artificial intelligence

Kim

Moon

Goh

et al. 2023

Sci Rep

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One of the key aspects of the diagnosis and treatment of atypical femoral fractures is the early detection of incomplete fractures and the prevention of their progression to complete fractures. However, an incomplete atypical femoral fracture can be misdiagnosed as a normal lesion by both primary care physicians and orthopedic surgeons; expert consultation is needed for accurate diagnosis. To overcome this limitation, we developed a transfer learning-based ensemble model to detect and localize fractures. A total of 1050 radiographs, including 100 incomplete fractures, were preprocessed by applying a Sobel filter. Six models (EfficientNet B5, B6, B7, DenseNet 121, MobileNet V1, and V2) were selected for transfer learning. We then composed two ensemble models; the first was based on the three models having the highest accuracy, and the second was based on the five models having the highest accuracy. The area under the curve (AUC) of the case that used the three most accurate models was the highest at 0.998. This study demonstrates that an ensemble of transfer-learning-based models can accurately classify and detect fractures, even in an imbalanced dataset. This artificial intelligence (AI)-assisted diagnostic application could support decision-making and reduce the workload of clinicians with its high speed and accuracy.

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“…It minimizes the loss function of the model through a gradient descent algorithm and implements the speed and performance of gradient-boosted decision trees (11). Furthermore, artificial neural networks (ANN) comprise a fundamental component of deep learning algorithms, demonstrating great potential in building high prediction accuracy (12)(13)(14)(15). Currently, AI algorithms have proven successful in processing clinical image data, obtaining desired prediction results (16)(17)(18).…”

Section: Introductionmentioning

confidence: 99%

The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer

Zeng

Liu²,

Chen³

et al. 2023

Front. Oncol.

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PurposeThis study aimed to develop a machine learning model to retrospectively study and predict the recurrence risk of breast cancer patients after surgery by extracting the clinicopathological features of tumors from unstructured clinical electronic health record (EHR) data.MethodsThis retrospective cohort included 1,841 breast cancer patients who underwent surgical treatment. To extract the principal features associated with recurrence risk, the clinical notes and histopathology reports of patients were collected and feature engineering was used. Predictive models were next conducted based on this important information. All algorithms were implemented using Python software. The accuracy of prediction models was further verified in the test cohort. The area under the curve (AUC), precision, recall, and F1 score were adopted to evaluate the performance of each model.ResultsA training cohort with 1,289 patients and a test cohort with 552 patients were recruited. From 2011 to 2019, a total of 1,841 textual reports were included. For the prediction of recurrence risk, both LSTM, XGBoost, and SVM had favorable accuracies of 0.89, 0.86, and 0.78. The AUC values of the micro-average ROC curve corresponding to LSTM, XGBoost, and SVM were 0.98 ± 0.01, 0.97 ± 0.03, and 0.92 ± 0.06. Especially the LSTM model achieved superior execution than other models. The accuracy, F1 score, macro-avg F1 score (0.87), and weighted-avg F1 score (0.89) of the LSTM model produced higher values. All P values were statistically significant. Patients in the high-risk group predicted by our model performed more resistant to DNA damage and microtubule targeting drugs than those in the intermediate-risk group. The predicted low-risk patients were not statistically significant compared with intermediate- or high-risk patients due to the small sample size (188 low-risk patients were predicted via our model, and only two of them were administered chemotherapy alone after surgery). The prognosis of patients predicted by our model was consistent with the actual follow-up records.ConclusionsThe constructed model accurately predicted the recurrence risk of breast cancer patients from EHR data and certainly evaluated the chemoresistance and prognosis of patients. Therefore, our model can help clinicians to formulate the individualized management of breast cancer patients.

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Deep neural networks with promising diagnostic accuracy for the classification of atypical femoral fractures

Cited by 7 publications

References 32 publications

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Detection of incomplete atypical femoral fracture on anteroposterior radiographs via explainable artificial intelligence

The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer

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