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Abstract

Introduction:

In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy.

Methods:

Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity.

Results:

These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViT-B-16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95.

Conclusion:

This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into current medical imaging systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-10-02
2024-11-22

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References

  1. Saeed W. Waseem M. Elbow Fractures Overview StatPearls 2023
    [Google Scholar]
  2. Midtgaard K.S. Ruzbarsky J.J. Hackett T.R. Viola R.W. Elbow Fractures. Clin. Sports Med. 2020 39 3 623 636 10.1016/j.csm.2020.03.002 32446579
    [Google Scholar]
  3. Kuo R.Y. Harrison C. Curran T.A. Jones B. Freethy A. Cussons D. Stewart M. Collins G.S. Furniss D. Artificial intelligence in fracture detection: a systematic review and meta-analysis. Radiology 2022 304 1 50 62 10.1148/radiol.211785 35348381
    [Google Scholar]
  4. Kim D.H. MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin. Radiol. 2018 73 5 439 445 10.1016/j.crad.2017.11.015 29269036
    [Google Scholar]
  5. Kohli M. Prevedello L.M. Filice R.W. Geis J.R. Implementing machine learning in radiology practice and research. AJR Am. J. Roentgenol. 2017 208 4 754 760 10.2214/AJR.16.17224 28125274
    [Google Scholar]
  6. Yang W. Wang F. Multislice spiral computed tomography postprocessing technology in the imaging diagnosis of extremities and joints. Computational and Mathematical Methods in Medicine Wiley 2021
    [Google Scholar]
  7. Guan B. Zhang G. Yao J. Wang X. Wang M. Arm fracture detection in X-rays based on improved deep convolutional neural network. Comput. Electr. Eng. 2020 81 106530 10.1016/j.compeleceng.2019.106530
    [Google Scholar]
  8. Hussain T. Shouno H. Explainable deep learning approach for multi-class brain magnetic resonance imaging tumor classification and localization using gradient-weighted class activation mapping. Information 2023 14 12 642 10.3390/info14120642
    [Google Scholar]
  9. Hussain T. Shouno H. MAGRes-UNet: Improved medical image segmentation through a deep learning paradigm of multi-attention gated residual u-net. IEEE Access 2024 12 40290 40310 10.1109/ACCESS.2024.3374108
    [Google Scholar]
  10. Alam T. Shia W.C. Hsu F.R. Hassan T. Improving breast cancer detection and diagnosis through semantic segmentation using the Unet3+ deep learning framework. Biomedicines 2023 11 6 1536 10.3390/biomedicines11061536 37371631
    [Google Scholar]
  11. Xia L. Zhang H. Wu Y. Song R. Ma Y. Mou L. Liu J. Xie Y. Ma M. Zhao Y. 3D vessel-like structure segmentation in medical images by an edge-reinforced network. Med. Image Anal. 2022 82 102581 10.1016/j.media.2022.102581 36058052
    [Google Scholar]
  12. Zhao C. Xiang S. Wang Y. Cai Z. Shen J. Zhou S. Zhao D. Su W. Guo S. Li S. Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium. Expert Syst. Appl. 2023 214 119105 10.1016/j.eswa.2022.119105
    [Google Scholar]
  13. Sharma S. Artificial intelligence for fracture diagnosis in orthopedic X-rays: current developments and future potential. SICOT-J 2023 9 1 12
    [Google Scholar]
  14. Deo G. Totlani J. Mahamuni C.V. A survey on bone fracture detection methods using image processing and artificial intelligence (AI) approaches AIP Conference Proceedings AIP Publishing 2024 10.1063/5.0188460
    [Google Scholar]
  15. Azizzadeh Mehmandost Olya B. Mohebian R. Bagheri H. Mahdavi Hezaveh A. Khan Mohammadi A. Toward real-time fracture detection on image logs using deep convolutional neural network YOLOv5. Interpretation 2024 12 2 SB9 SB18 10.1190/INT‑2022‑0104.1
    [Google Scholar]
  16. Medaramatla S.C. Samhitha C.V. Pande S.D. Vinta S.R. Detection of hand bone fractures in x-ray images using hybrid YOLO NAS. IEEE Access 2024 12 57661 57673 10.1109/ACCESS.2024.3379760
    [Google Scholar]
  17. Malik S. Amin J. Sharif M. Yasmin M. Kadry S. Anjum S. Fractured elbow classification using hand-crafted and deep feature fusion and selection based on whale optimization approach. Mathematics 2022 10 18 3291 10.3390/math10183291
    [Google Scholar]
  18. Li J. Hu W. Wu H. Chen Z. Chen J. Lai Q. Wang Y. Li Y. Detection of hidden pediatric elbow fractures in X-ray images based on deep learning. J. Rad. Rese. Appl. Sci. 2024 17 2 100893 10.1016/j.jrras.2024.100893
    [Google Scholar]
  19. El-Saadawy H. Tantawi M.M. Shedeed H.A. Tolba M.F. A two-stage method for bone x-rays abnormality detection using mobilenet network. In International Conferences on Artificial Intelligence and Computer Vision Springer, Cham, 24 March 2020, pp 372–380. 10.1007/978‑3‑030‑44289‑7_35
    [Google Scholar]
  20. Warin K. Limprasert W. Suebnukarn S. Inglam S. Jantana P. Vicharueang S. Assessment of deep convolutional neural network models for mandibular fracture detection in panoramic radiographs. Int. J. Oral Maxillofac. Surg. 2022 51 11 1488 1494 10.1016/j.ijom.2022.03.056 35397969
    [Google Scholar]
  21. Ashkani-Esfahani S. Mojahed Yazdi R. Bhimani R. Kerkhoffs G.M. Maas M. DiGiovanni C.W. Lubberts B. Guss D. Detection of ankle fractures using deep learning algorithms. Foot Ankle Surg. 2022 28 8 1259 1265 10.1016/j.fas.2022.05.005 35659710
    [Google Scholar]
  22. He J. Jiang D. Fully automatic model based on se-resnet for bone age assessment. IEEE Access 2021 9 62460 62466 10.1109/ACCESS.2021.3074713
    [Google Scholar]
  23. Tanzi L. Audisio A. Cirrincione G. Aprato A. Vezzetti E. Vision transformer for femur fracture classification. Injury 2022 53 7 2625 2634 10.1016/j.injury.2022.04.013 35469638
    [Google Scholar]
  24. Bloice M.D. Stocker C. Holzinger A. Augmentor: an image augmentation library for machine learning preprint arXiv:1708.04680 2017
    [Google Scholar]
  25. Pérez-García F. Sparks R. Ourselin S. TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Comput. Methods Programs Biomed. 2021 208 106236 10.1016/j.cmpb.2021.106236 34311413
    [Google Scholar]
  26. Vasuki P. Kanimozhi J. Devi M.B. A survey on image preprocessing techniques for diverse fields of medical imagery 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE) Karur, India, 27-28 April 2017, pp. 1-6. 10.1109/ICEICE.2017.8192443
    [Google Scholar]
  27. Inui A. Mifune Y. Nishimoto H. Mukohara S. Fukuda S. Kato T. Furukawa T. Tanaka S. Kusunose M. Takigami S. Ehara Y. Kuroda R. Detection of elbow OCD in the ultrasound image by artificial intelligence using YOLOv8. Appl. Sci. 2023 13 13 7623 10.3390/app13137623
    [Google Scholar]
  28. Lu S. Wang S. Wang G. Automated universal fractures detection in X-ray images based on deep learning approach. Multimed. Tools Appl. 2022 81 30 44487 44503 10.1007/s11042‑022‑13287‑z
    [Google Scholar]
  29. Wei Z. Na M. Huisheng S. Hongqi F. Feature extraction of X-ray fracture image and fracture classification 2009 International Conference on Artificial Intelligence and Computational Intelligence Shanghai, China, 07-08 November 2009, pp. 408-412. 10.1109/AICI.2009.40
    [Google Scholar]
  30. Joshi D. Singh T.P. A survey of fracture detection techniques in bone X-ray images. Artif. Intell. Rev. 2020 53 6 4475 4517 10.1007/s10462‑019‑09799‑0
    [Google Scholar]
  31. Uysal F. Hardalaç F. Peker O. Tolunay T. Tokgöz N. Classification of shoulder x-ray images with deep learning ensemble models. Appl. Sci. 2021 11 6 2723 10.3390/app11062723
    [Google Scholar]
  32. Thian Y.L. Li Y. Jagmohan P. Sia D. Chan V.E. Tan R.T. Convolutional neural networks for automated fracture detection and localization on wrist radiographs. Radiol. Artif. Intell. 2019 1 1 e180001 10.1148/ryai.2019180001 33937780
    [Google Scholar]
  33. Skaggs D. Pershad J. Pediatric elbow trauma. Pediatr. Emerg. Care 1997 13 6 425 434 10.1097/00006565‑199712000‑00021 9435010
    [Google Scholar]
  34. Rayan J.C. Reddy N. Kan J.H. Zhang W. Annapragada A. Binomial classification of pediatric elbow fractures using a deep learning multiview approach emulating radiologist decision making. Radiol. Artif. Intell. 2019 1 1 e180015 10.1148/ryai.2019180015 33937781
    [Google Scholar]
  35. Sgualdino D.G. Neep M.J. Spuur K. Hughes C. Is there benefit to concurrent x‐ray imaging of the wrist, forearm and elbow in paediatric patients following a fall on the outstretched hand? J. Med. Radiat. Sci. 2022 69 4 431 438 10.1002/jmrs.614 35973970
    [Google Scholar]
  36. Pranata Y.D. Wang K.C. Wang J.C. Idram I. Lai J.Y. Liu J.W. Hsieh I.H. Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Comput. Methods Programs Biomed. 2019 171 27 37 10.1016/j.cmpb.2019.02.006 30902248
    [Google Scholar]
  37. Lennon R.I. Riyat M.S. Hilliam R. Anathkrishnan G. Alderson G. Can a normal range of elbow movement predict a normal elbow x ray? Emerg. Med. J. 2007 24 2 86 88 10.1136/emj.2006.039792 17251609
    [Google Scholar]
  38. Laguna B. Livingston K. Brar R. Jagodzinski J. Pandya N. Sabatini C. Courtier J. Assessing the value of a novel augmented reality application for presurgical planning in adolescent elbow fractures. Front. Virt. Real. 2020 1 528810 10.3389/frvir.2020.528810
    [Google Scholar]
  39. Shen D. Wu G. Suk H.I. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 2017 19 1 221 248 10.1146/annurev‑bioeng‑071516‑044442 28301734
    [Google Scholar]
  40. Rundo L. Tangherloni A. Nobile M.S. Militello C. Besozzi D. Mauri G. Cazzaniga P. MedGA: A novel evolutionary method for image enhancement in medical imaging systems. Expert Syst. Appl. 2019 119 387 399 10.1016/j.eswa.2018.11.013
    [Google Scholar]
  41. Kalyankar A. Harkude G. A. Sumanth L. S. Modeling radiologist decision making for paediatric elbow fractures by deep learning multiview. IJRASET 2023 2023 10.22214/ijraset.2023.50583
    [Google Scholar]
  42. Sonnow L. Salimova N. Behrendt L. Wacker F.K. Örgel M. Plagge J. Weidemann F. Photon-counting CT of elbow joint fractures: image quality in a simulated post-trauma setting with off-center positioning. Eur. Radiol. Exp. 2023 7 1 15 10.1186/s41747‑023‑00329‑w 36967394
    [Google Scholar]
  43. Sirisha U. Chandana B.S. Privacy preserving image encryption with optimal deep transfer learning based accident severity classification model. Sensors 2023 23 1 519 10.3390/s23010519 36617116
    [Google Scholar]
  44. Ju R.Y. Cai W. Fracture detection in pediatric wrist trauma X-ray images using YOLOv8 algorithm arXiv:2304.05071 2023 10.1038/s41598‑023‑47460‑7
    [Google Scholar]
/content/journals/cmir/10.2174/0115734056309890240912054616
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"An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture Prediction in X-ray Images of the Elbow"
Bentham Science Publishers ; https://doi.org/10.2174/0115734056309890240912054616
/content/journals/cmir/10.2174/0115734056309890240912054616
/content/journals/cmir/10.2174/0115734056309890240912054616
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  • Article Type:     Research Article
Keywords: Fracture ; Medical images ; X-ray ; ROI ; YOLO ; ResNet ; Vision transformer ; SeResNet

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