骨折検出AIの論文リンク集（2020年5月版）

# 骨折検出AIの論文リンク
2020年5月現在, 整形外科領域、AIによる骨折検出の論文をリストアップしました。
キーワードは"deep learning"&"fracture" ないしは”neural net"&"fracture"
by 人工知能医学研究部　部長　imedix

##頭蓋骨
- [Weakly-Supervised Learning for Attention-Guided Skull Fracture Classification In Computed Tomography Imaging - IEEE Conference Publication](https://ieeexplore.ieee.org/abstract/document/8803404)
 - Yang, C. Y., Lo, C. H., Wang, H. C., Chou, J. H., & FrankWang, Y. C. (2019, September). Weakly-Supervised Learning for Attention-Guided Skull Fracture Classification In Computed Tomography Imaging. In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 1337-1341). IEEE.

- [Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study - ScienceDirect](https://www.sciencedirect.com/science/article/pii/S0140673618316453)
 - Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N. G., Venugopal, V. K., ... & Warier, P. (2018). Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. The Lancet, 392(10162), 2388-2396.

##歯根破折（歯科）
- [Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography | SpringerLink](https://link.springer.com/article/10.1007/s11282-019-00409-x)
 - Fukuda, M., Inamoto, K., Shibata, N., Ariji, Y., Yanashita, Y., Kutsuna, S., ... & Ariji, E. (2019). Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral radiology, 1-7.

- [Attention-based multi-scale transfer ResNet for skull fracture image classification](
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11198/111980D/Attention-based-multi-scale-transfer-ResNet-for-skull-fracture-image/10.1117/12.2540498.short?SSO=1)
 - Ning, D., Liu, G., Jiang, R., & Wang, C. (2019, July). Attention-based multi-scale transfer ResNet for skull fracture image classification. In Fourth International Workshop on Pattern Recognition (Vol. 11198, p. 111980D). International Society for Optics and Photonics.

##手首（橈骨、尺骨）
- [Convolutional Neural Networks for Automated Fracture Detection and Localization on Wrist Radiographs | Radiology: Artificial Intelligence}(https://pubs.rsna.org/doi/abs/10.1148/ryai.2019180001)
 - Thian, Y. L., Li, Y., Jagmohan, P., Sia, D., Chan, V. E. Y., & Tan, R. T. (2019). Convolutional neural networks for automated fracture detection and localization on wrist radiographs. Radiology: Artificial Intelligence, 1(1), e180001.

-[Automatic Detection of Wrist Fractures From Posteroanterior and Lateral Radiographs: A Deep Learning-Based Approach | SpringerLink](https://link.springer.com/chapter/10.1007/978-3-030-11166-3_10)
 - Ebsim, R., Naqvi, J., & Cootes, T. F. (2018, September). Automatic detection of wrist fractures from posteroanterior and lateral radiographs: a deep learning-based approach. In International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging (pp. 114-125). Springer, Cham.

- ["Artificial Intelligence Based Wrist Fracture Classification" by Dineep Thomas](https://digitalcommons.lsu.edu/gradschool_theses/4991/)
 - Thomas, D. (2019). Artificial Intelligence Based Wrist Fracture Classification.

##肘
- [Binomial Classification of Pediatric Elbow Fractures Using a Deep Learning Multiview Approach Emulating Radiologist Decision Making | Radiology: Artificial Intelligence](https://pubs.rsna.org/doi/abs/10.1148/ryai.2019180015)
 - Rayan, J. C., Reddy, N., Kan, J. H., Zhang, W., & Annapragada, A. (2019). Binomial classification of pediatric elbow fractures using a deep learning multiview approach emulating radiologist decision making. Radiology: Artificial Intelligence, 1(1), e180015.

##上腕骨
- [An exemplar pyramid feature extraction based humerus fracture classification method - ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0306987720301985)
 - Demir, S., Key, S., Tuncer, T., & Dogan, S. (2020). An Exemplar Pyramid Feature Extraction based Humerus Fracture Classification Method. Medical Hypotheses, 109663.

##肋骨（胸部）
- [[1908.05467] Automated Rib Fracture Detection of Postmortem Computed Tomography Images Using Machine Learning Techniques](https://arxiv.org/abs/1908.05467)
 - Gunz, S., Erne, S., Rawdon, E. J., Ampanozi, G., Sieberth, T., Affolter, R., ... & Dobay, A. (2019). Automated Rib Fracture Detection of Postmortem Computed Tomography Images Using Machine Learning Techniques. arXiv preprint arXiv:1908.05467.

- [Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation | Radiology](https://pubs.rsna.org/doi/full/10.1148/radiol.2019191293)
 - Majkowska, A., Mittal, S., Steiner, D. F., Reicher, J. J., McKinney, S. M., Duggan, G. E., ... & Ding, A. (2020). Chest radiograph interpretation with deep learning models: Assessment with radiologist-adjudicated reference standards and population-adjusted evaluation. Radiology, 294(2), 421-431.

##脊椎
-  [[1904.07624] Automatic detection and segmentation of lumbar vertebra from X-ray images for compression fracture evaluation](https://arxiv.org/abs/1904.07624)
 - Kim, K. C., Cho, H. C., Jang, T. J., Choi, J. M., & Seo, J. K. (2019). Automatic detection and segmentation of lumbar vertebra from X-ray images for compression fracture evaluation. arXiv preprint arXiv:1904.07624.

##股関節
- [Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning | Radiology: Artificial Intelligence](https://pubs.rsna.org/doi/abs/10.1148/ryai.2020190023)
 - Krogue, J. D., Cheng, K. V., Hwang, K. M., Toogood, P., Meinberg, E. G., Geiger, E. J., ... & Wright, A. (2020). Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning. Radiology: Artificial Intelligence, 2(2), e190023.

- [Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs | SpringerLink](https://link.springer.com/article/10.1007/s00330-019-06167-y)
 - Cheng, C. T., Ho, T. Y., Lee, T. Y., Chang, C. C., Chou, C. C., Chen, C. C., ... & Liao, C. H. (2019). Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs. European radiology, 29(10), 5469-5477.

- [Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network | SpringerLink](https://link.springer.com/article/10.1007/s00256-018-3016-3)
 - Urakawa, T., Tanaka, Y., Goto, S., Matsuzawa, H., Watanabe, K., & Endo, N. (2019). Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal radiology, 48(2), 239-244.

- [Deep Learning to Predict Hip Fracture Risk from Clinical DXA-images](https://lup.lub.lu.se/student-papers/search/publication/8964114)
 - Yip, K., & Husein, M. (2018). Deep Learning to Predict Hip Fracture Risk from Clinical DXA-images.

- [Femoral-neck-fracture-detection-in-X-ray-images-using-deep-learning-and-genetic-algorithm-approaches.pdf](https://www.researchgate.net/profile/Salih_Beyaz2/publication/340236751_Femoral_neck_fracture_detection_in_X-ray_images_using_deep_learning_and_genetic_algorithm_approaches/links/5e7e4db092851caef4a56b23/Femoral-neck-fracture-detection-in-X-ray-images-using-deep-learning-and-genetic-algorithm-approaches.pdf)
 - Beyaz, S., Açıcı, K., & Sümer, E. (2020). Femoral neck fracture detec-tion in X-ray images using deep learning and genetic algorithm approaches. Jt Dis Relat Surg, 31.

- [Deep learning predicts hip fracture using confounding patient and healthcare variables | npj Digital Medicine](https://www.nature.com/articles/s41746-019-0105-1)
 - Badgeley, M. A., Zech, J. R., Oakden-Rayner, L., Glicksberg, B. S., Liu, M., Gale, W., ... & Dudley, J. T. (2019). Deep learning predicts hip fracture using confounding patient and healthcare variables. NPJ digital medicine, 2(1), 1-10.

- [submissions.mirasmart.com/ISMRM2017/ViewSubmission.aspx?sbmID=3860](https://scholar.harvard.edu/files/spencer_hallyburton/files/ismrm_abstract.pdf)
 - Hallyburton, S., Chang, G., Honig, S., Cho, K., & Deniz, C. M. (2017). Automatic segmentation of mr images of the proximal femur using deep learning. In Proceedings 25th Scientific Meeting, ISMRM, Hawaii (Vol. 3986).

- [Medical Data Augmentation Using Generative Adversarial Networks : X-ray Image Generation for Transfer Learning of Hip Fracture Detection - IEEE Conference Publication](https://ieeexplore.ieee.org/abstract/document/8959908)
 - Lin, Y. J., & Chung, I. F. (2019, November). Medical Data Augmentation Using Generative Adversarial Networks: X-ray Image Generation for Transfer Learning of Hip Fracture Detection. In 2019 International Conference on Technologies and Applications of Artiﬁcial Intelligence (TAAI) (pp. 1-5). IEEE.

- [2004.00482.pdf](https://arxiv.org/pdf/2004.00482.pdf)
 - Jiménez-Sánchez, A., Mateus, D., Kirchhoff, S., Kirchhoff, C., Biberthaler, P., Navab, N., ... & Piella, G. (2019, October). Medical-based Deep Curriculum Learning for Improved Fracture Classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 694-702). Springer, Cham.

- [[1711.06504] Detecting hip fractures with radiologist-level performance using deep neural networks](https://arxiv.org/abs/1711.06504)
 - Gale, W., Oakden-Rayner, L., Carneiro, G., Bradley, A. P., & Palmer, L. J. (2017). Detecting hip fractures with radiologist-level performance using deep neural networks. arXiv preprint arXiv:1711.06504.

- [indexsmart.mirasmart.com/ISMRM2017/PDFfiles/0693.html](http://indexsmart.mirasmart.com/ISMRM2017/PDFfiles/0693.html)
 - MRI、股関節の論文
 - Deniz, C. M., Cho, K., Honig, S., Egol, K. A., Sodickson, D. K., & Chang, G. Fracture Risk Assessment using Deep Learning and Hip Microarchitecture MRI.


##大腿骨(股関節も含む）
- [Thigh fracture detection using deep learning method based on new dilated convolutional feature pyramid network - ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0167865519301825)
 - Guan, B., Yao, J., Zhang, G., & Wang, X. (2019). Thigh fracture detection using deep learning method based on new dilated convolutional feature pyramid network. Pattern Recognition Letters, 125, 521-526.

- [A Method for Detecting Femur Fracture Based on SK-DenseNet | Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing](https://dl.acm.org/doi/abs/10.1145/3358331.3358402)
 - Miao, Y., Zhao, P. F., Tang, X. F., Li, Y. Q., Zhang, L. Y., Shi, W. L., ... & Liu, J. H. (2019, October). A Method for Detecting Femur Fracture Based on SK-DenseNet. In Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (pp. 1-7).

##膝関節（骨折以外の靭帯損傷なども含む、MRI)
- [Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet](https://journals.plos.org/plosmedicine/article%3Fid%3D10.1371/journal.pmed.1002699)
 - Bien, N., Rajpurkar, P., Ball, R. L., Irvin, J., Park, A., Jones, E., ... & Halabi, S. (2018). Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS medicine, 15(11), e1002699.

##足首（踵骨）
- [Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images - ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0169260718314652)
 - Pranata, Y. D., Wang, K. C., Wang, J. C., Idram, I., Lai, J. Y., Liu, J. W., & Hsieh, I. H. (2019). Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Computer methods and programs in biomedicine, 171, 27-37.

- [Ankle Fracture Detection Utilizing a Convolutional Neural Network Ensemble Implemented with a Small Sample, De Novo Training, and Multiview Incorporation | SpringerLink](https://link.springer.com/article/10.1007/s10278-018-0167-7)
 - Kitamura, G., Chung, C. Y., & Moore, B. E. (2019). Ankle fracture detection utilizing a convolutional neural network ensemble implemented with a small sample, de novo training, and multiview incorporation. Journal of digital imaging, 32(4), 672-677.


##その他
- [Long bone fracture type classification for limited number of CT data with deep learning | Proceedings of the 35th Annual ACM Symposium on Applied Computing](https://dl.acm.org/doi/abs/10.1145/3341105.3373900)
 - Lee, K. M., Lee, S. Y., Han, C. S., & Choi, S. M. (2020, March). Long bone fracture type classification for limited number of CT data with deep learning. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (pp. 1090-1095).

- [Prediction of hip fracture in post-menopausal women using artificial neural network approach - IEEE Conference Publication](https://ieeexplore.ieee.org/abstract/document/8037784)
 - Ho-Le, T. P., Center, J. R., Eisman, J. A., Nguyen, T. V., & Nguyen, H. T. (2017, July). Prediction of hip fracture in post-menopausal women using artificial neural network approach. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4207-4210). IEEE.

- [Detecting Bone Fracture Using Transfer Learning | SpringerLink](https://link.springer.com/chapter/10.1007/978-981-15-1100-4_10)
 - Verma, S., Kulshrestha, S., Rajput, C., & Patel, S. (2020). Detecting Bone Fracture Using Transfer Learning. In Advancement of Machine Intelligence in Interactive Medical Image Analysis (pp. 215-228). Springer, Singapore.

- [What Are the Applications and Limitations of Artificial Inte... : Clinical Orthopaedics and Related Research®](https://journals.lww.com/clinorthop/Fulltext/2019/11000/What_Are_the_Applications_and_Limitations_of.18.aspx)
 - Langerhuizen, D. W., Janssen, S. J., Mallee, W. H., van den Bekerom, M. P., Ring, D., Kerkhoffs, G. M., ... & Doornberg, J. N. (2019). What are the applications and limitations of artificial intelligence for fracture detection and classification in orthopaedic trauma imaging? A Systematic Review. Clinical Orthopaedics and Related Research®, 477(11), 2482-2491.

- [Entropy | Free Full-Text | Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection | HTML](https://www.mdpi.com/1099-4300/21/4/338/htm)
 - Hržić, F., Štajduhar, I., Tschauner, S., Sorantin, E., & Lerga, J. (2019). Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection. Entropy, 21(4), 338.

- [Application of artificial neural networks to fracture analysis at the Äspö HRL, Sweden: fracture sets classification - ScienceDirect](https://www.sciencedirect.com/science/article/pii/S1365160901000302)
 - Sirat, M., & Talbot, C. J. (2001). Application of artificial neural networks to fracture analysis at the Äspö HRL, Sweden: fracture sets classification. International Journal of Rock Mechanics and Mining Sciences, 38(5), 621-639.

- [Deep neural network improves fracture detection by clinicians | PNAS](https://www.pnas.org/content/115/45/11591.full)
 - Lindsey, R., Daluiski, A., Chopra, S., Lachapelle, A., Mozer, M., Sicular, S., ... & Potter, H. (2018). Deep neural network improves fracture detection by clinicians. Proceedings of the National Academy of Sciences, 115(45), 11591-11596.

- [Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks - ScienceDirect](https://www.sciencedirect.com/science/article/pii/S0009926017305354)
 - Kim, D. H., & MacKinnon, T. (2018). Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clinical radiology, 73(5), 439-445.

- [Full article: Deep learning in fracture detection: a narrative review](https://www.tandfonline.com/doi/full/10.1080/17453674.2019.1711323)
 - Kalmet, P. H., Sanduleanu, S., Primakov, S., Wu, G., Jochems, A., Refaee, T., ... & Poeze, M. (2020). Deep learning in fracture detection: a narrative review. Acta Orthopaedica, 1-6.

- [Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI - Mazurowski - 2019 - Journal of Magnetic Resonance Imaging - Wiley Online Library](https://onlinelibrary.wiley.com/doi/full/10.1002/jmri.26534)
 - 総説、MRI,研究のやり方について解説
 - Mazurowski, M. A., Buda, M., Saha, A., & Bashir, M. R. (2019). Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. Journal of magnetic resonance imaging, 49(4), 939-954.