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Artificial Intelligence in Trauma Imaging

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Trauma Computed Tomography

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Abstract

Medical imaging, particularly computed tomography (CT), has become an indispensable tool in routine trauma care, aiding healthcare professionals in accurate diagnosis and treatment planning. Recent advancements in computational performance and machine learning algorithms have created new avenues for artificial intelligence (AI) to boost the field of medical imaging. In this chapter, we will discuss the role of the new emerging technology of artificial intelligence in trauma imaging. We will start with a gentle introduction to artificial intelligence, followed by a detailed discussion of artificial intelligence applications in trauma imaging. Finally, we touch on some future directions and additional resources.

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References

  1. Erickson BJ, et al. Machine learning for medical imaging. RadioGraphics. 2017;37(2):505–15.

    Article  PubMed  Google Scholar 

  2. Cheng PM, et al. Deep learning: an update for radiologists. Radiographics. 2021;41(5):1427–45.

    Article  PubMed  Google Scholar 

  3. Chartrand G, et al. Deep learning: a primer for radiologists. Radiographics. 2017;37(7):2113–31.

    Article  PubMed  Google Scholar 

  4. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Cham: Springer; 2015.

    Google Scholar 

  5. Raghu M, et al. Transfusion: understanding transfer learning for medical imaging. Adv Neural Inf Proces Syst. 2019;32:3347–57.

    Google Scholar 

  6. Kim HE, et al. Transfer learning for medical image classification: a literature review. BMC Med Imaging. 2022;22(1):69.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Sellergren AB, et al. Simplified transfer learning for chest radiography models using less data. Radiology. 2022;305(2):454–65.

    Article  PubMed  Google Scholar 

  8. Savatmongkorngul S, Wongwaisayawan S, Kaewlai R. Focused assessment with sonography for trauma: current perspectives. Open Access Emerg Med. 2017;9:57–62.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Cheng CY, et al. Deep learning assisted detection of abdominal free fluid in Morison’s pouch during focused assessment with sonography in trauma. Front Med. 2021;8:707437.

    Article  Google Scholar 

  10. Kornblith AE, et al. Development and validation of a deep learning strategy for automated view classification of pediatric focused assessment with sonography for trauma. J Ultrasound Med. 2022;41(8):1915–24.

    Article  PubMed  Google Scholar 

  11. Lin Z, et al. Deep learning for emergency ascites diagnosis using ultrasonography images. J Appl Clin Med Phys. 2022;23(7):e13695.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Sjogren AR, et al. Image segmentation and machine learning for detection of abdominal free fluid in focused assessment with sonography for trauma examinations: a pilot study. J Ultrasound Med. 2016;35(11):2501–9.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Dreizin D, et al. Performance of a deep learning algorithm for automated segmentation and quantification of traumatic pelvic hematomas on CT. J Digit Imaging. 2020;33(1):243–51.

    Article  PubMed  Google Scholar 

  14. Kane NM, et al. Traumatic pneumoperitoneum. Implications of computed tomography diagnosis. Investig Radiol. 1991;26(6):574–8.

    Article  CAS  Google Scholar 

  15. Brejnebøl MW, et al. Artificial Intelligence based detection of pneumoperitoneum on CT scans in patients presenting with acute abdominal pain: a clinical diagnostic test accuracy study. Eur J Radiol. 2022;150:110216.

    Article  PubMed  Google Scholar 

  16. Winkel DJ, et al. Evaluation of an AI-based detection software for acute findings in abdominal computed tomography scans: toward an automated work list prioritization of routine CT examinations. Investig Radiol. 2019;54(1):55–9.

    Article  Google Scholar 

  17. Taubmann O, et al. Automatic detection of free intra-abdominal air in computed tomography. In: Medical image computing and computer assisted intervention – MICCAI 2020. Cham: Springer; 2020.

    Google Scholar 

  18. Su C-Y, et al. A deep learning method for alerting emergency physicians about the presence of subphrenic free air on chest radiographs. J Clin Med. 2021;10(2):254.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Kim M, et al. Detection of pneumoperitoneum in the abdominal radiograph images using artificial neural networks. Eur J Radiol Open. 2021;8:100316.

    Article  PubMed  Google Scholar 

  20. Goyal M, et al. Sensitivity and specificity evaluation of deep learning models for detection of pneumoperitoneum on chest radiographs. 2020.

    Google Scholar 

  21. Kozar RA, et al. Organ injury scaling 2018 update: spleen, liver, and kidney. J Trauma Acute Care Surg. 2018;85(6):1119–22.

    Article  PubMed  Google Scholar 

  22. Farzaneh N, et al. A deep learning framework for automated detection and quantitative assessment of liver trauma. BMC Med Imaging. 2022;22(1):39.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Dreizin D, et al. Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis. Abdom Radiol. 2021;46(6):2556–66.

    Article  Google Scholar 

  24. Chen H, Unberath M, Dreizin D. Toward automated interpretable AAST grading for blunt splenic injury. Emerg Radiol. 2022;30(1):41–50.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Li X, et al. Deep learning-enabled system for rapid pneumothorax screening on chest CT. Eur J Radiol. 2019;120:108692.

    Article  PubMed  Google Scholar 

  26. Röhrich S, et al. Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography. Eur Radiol Exp. 2020;4(1):26.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Malhotra P, et al. Deep learning-based computer-aided pneumothorax detection using chest X-ray images. Sensors. 2022;22(6):2278.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Hillis JM, et al. Evaluation of an artificial intelligence model for detection of pneumothorax and tension pneumothorax in chest radiographs. JAMA Netw Open. 2022;5(12):e2247172.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Boice EN, et al. Training ultrasound image classification deep-learning algorithms for pneumothorax detection using a synthetic tissue phantom apparatus. J Imaging. 2022;8(9):249.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Thian YL, et al. Deep learning systems for pneumothorax detection on chest radiographs: a multicenter external validation study. Radiol Artif Intell. 2021;3(4):e200190.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Kitamura G, Deible C. Retraining an open-source pneumothorax detecting machine learning algorithm for improved performance to medical images. Clin Imaging. 2020;61:15–9.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Tolkachev A, et al. Deep learning for diagnosis and segmentation of pneumothorax: the results on the kaggle competition and validation against radiologists. IEEE J Biomed Health Inform. 2021;25(5):1660–72.

    Article  PubMed  Google Scholar 

  33. Dreizin D, et al. A pilot study of deep learning-based CT volumetry for traumatic hemothorax. Emerg Radiol. 2022;29(6):995–1002.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Wang S, et al. Assessment of automatic rib fracture detection on chest CT using a deep learning algorithm. Eur Radiol. 2022;33(3):1824–34.

    Article  PubMed  Google Scholar 

  35. Niiya A, et al. Development of an artificial intelligence-assisted computed tomography diagnosis technology for rib fracture and evaluation of its clinical usefulness. Sci Rep. 2022;12(1):8363.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Gao Y, et al. Deep learning-based framework for segmentation of multiclass rib fractures in CT utilizing a multi-angle projection network. Int J Comput Assist Radiol Surg. 2022;17(6):1115–24.

    Article  PubMed  Google Scholar 

  37. Zhang B, et al. Improving rib fracture detection accuracy and reading efficiency with deep learning-based detection software: a clinical evaluation. Br J Radiol. 2021;94(1118):20200870.

    Article  PubMed  Google Scholar 

  38. Yao L, et al. Rib fracture detection system based on deep learning. Sci Rep. 2021;11(1):23513.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wu M, et al. Development and evaluation of a deep learning algorithm for rib segmentation and fracture detection from multicenter chest CT images. Radiol Artif Intell. 2021;3(5):e200248.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Meng XH, et al. A fully automated rib fracture detection system on chest CT images and its impact on radiologist performance. Skelet Radiol. 2021;50(9):1821–8.

    Article  Google Scholar 

  41. Kaiume M, et al. Rib fracture detection in computed tomography images using deep convolutional neural networks. Medicine. 2021;100(20):e26024.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Lewis BT, et al. Imaging manifestations of chest trauma. Radiographics. 2021;41(5):1321–34.

    Article  PubMed  Google Scholar 

  43. Choi J, et al. Scalable deep learning algorithm to compute percent pulmonary contusion among patients with rib fractures. J Trauma Acute Care Surg. 2022;93(4):461–6.

    Article  PubMed  Google Scholar 

  44. Tanioka S, et al. Machine learning prediction of hematoma expansion in acute intracerebral hemorrhage. Sci Rep. 2022;12(1):12452.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Arab A, et al. A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT. Sci Rep. 2020;10(1):19389.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Yu N, et al. A robust deep learning segmentation method for hematoma volumetric detection in intracerebral hemorrhage. Stroke. 2022;53(1):167–76.

    Article  PubMed  Google Scholar 

  47. Phaphuangwittayakul A, et al. An optimal deep learning framework for multi-type hemorrhagic lesions detection and quantification in head CT images for traumatic brain injury. Appl Intell. 2022;52(7):7320–38.

    Article  Google Scholar 

  48. Kamnitsas K, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61–78.

    Article  PubMed  Google Scholar 

  49. Ginat DT. Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage. Neuroradiology. 2020;62(3):335–40.

    Article  PubMed  Google Scholar 

  50. Rao B, et al. Utility of artificial intelligence tool as a prospective radiology peer reviewer - detection of unreported intracranial hemorrhage. Acad Radiol. 2021;28(1):85–93.

    Article  PubMed  Google Scholar 

  51. Seyam M, et al. Utilization of artificial intelligence-based intracranial hemorrhage detection on emergent noncontrast CT images in clinical workflow. Radiol Artif Intell. 2022;4(2):e210168.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Alagic Z, et al. Deep learning versus iterative image reconstruction algorithm for head CT in trauma. Emerg Radiol. 2022;29(2):339–52.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Nagayama Y, et al. Deep learning-based reconstruction for lower-dose pediatric CT: technical principles, image characteristics, and clinical implementations. Radiographics. 2021;41(7):1936–53.

    Article  PubMed  Google Scholar 

  54. Immonen E, et al. The use of deep learning towards dose optimization in low-dose computed tomography: a scoping review. Radiography. 2022;28(1):208–14.

    Article  CAS  PubMed  Google Scholar 

  55. Doerr SA, et al. Automated prediction of the thoracolumbar injury classification and severity score from CT using a novel deep learning algorithm. Neurosurg Focus. 2022;52(4):E5.

    Article  PubMed  Google Scholar 

  56. Li Y, et al. Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning. Eur Radiol. 2021;31(12):9612–9.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Rosenberg GS, et al. Artificial intelligence accurately detects traumatic thoracolumbar fractures on sagittal radiographs. Medicina. 2022;58(8):998.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Kong SH, et al. Development of a spine X-ray-based fracture prediction model using a deep learning algorithm. Endocrinol Metab. 2022;37(4):674–83.

    Article  Google Scholar 

  59. Chen X, Liu Y. A classification method for thoracolumbar vertebral fractures due to basketball sports injury based on deep learning. Comput Math Methods Med. 2022;2022:8747487.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Chen W, et al. A deep-learning model for identifying fresh vertebral compression fractures on digital radiography. Eur Radiol. 2022;32(3):1496–505.

    Article  PubMed  Google Scholar 

  61. Boonrod A, et al. Diagnostic accuracy of deep learning for evaluation of C-spine injury from lateral neck radiographs. Heliyon. 2022;8(8):e10372.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Tay B, Hyun JK, Oh S. A machine learning approach for specification of spinal cord injuries using fractional anisotropy values obtained from diffusion tensor images. Comput Math Methods Med. 2014;2014:276589.

    Article  PubMed  PubMed Central  Google Scholar 

  63. McCoy DB, et al. Convolutional neural network-based automated segmentation of the spinal cord and contusion injury: deep learning biomarker correlates of motor impairment in acute spinal cord injury. AJNR Am J Neuroradiol. 2019;40(4):737–44.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Collaborators GBDF. Global, regional, and national burden of bone fractures in 204 countries and territories, 1990-2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet Healthy Longev. 2021;2(9):e580–92.

    Article  Google Scholar 

  65. Inoue T, et al. Automated fracture screening using an object detection algorithm on whole-body trauma computed tomography. Sci Rep. 2022;12(1):16549.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Costantini TW, et al. Current management of hemorrhage from severe pelvic fractures: Results of an American Association for the Surgery of Trauma multi-institutional trial. J Trauma Acute Care Surg. 2016;80(5):717–23; discussion 723-5.

    Article  CAS  PubMed  Google Scholar 

  67. Dreizin D, et al. An automated deep learning method for Tile AO/OTA pelvic fracture severity grading from trauma whole-body CT. J Digit Imaging. 2021;34(1):53–65.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Ukai K, et al. Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images. Sci Rep. 2021;11(1):11716.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Yoon SJ, et al. Automatic multi-class intertrochanteric femur fracture detection from CT images based on AO/OTA classification using faster R-CNN-BO method. J Appl Biomed. 2020;18(4):97–105.

    Article  PubMed  Google Scholar 

  70. Wang D, et al. Accuracy and reliability analysis of a machine learning based segmentation tool for intertrochanteric femoral fracture CT. Front Surg. 2022;9:913385.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Ozkaya E, et al. Evaluation of an artificial intelligence system for diagnosing scaphoid fracture on direct radiography. Eur J Trauma Emerg Surg. 2022;48(1):585–92.

    Article  PubMed  Google Scholar 

  72. Cohen M, et al. Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs. Eur Radiol. 2022;33(6):3974–83.

    Article  PubMed  Google Scholar 

  73. Raisuddin AM, et al. Critical evaluation of deep neural networks for wrist fracture detection. Sci Rep. 2021;11(1):6006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Oka K, et al. Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays. J Orthop Surg Res. 2021;16(1):694.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Chung SW, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop. 2018;89(4):468–73.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Pranata YD, et al. Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Comput Methods Prog Biomed. 2019;171:27–37.

    Article  Google Scholar 

  77. Wang X, et al. Detection and classification of mandibular fracture on CT scan using deep convolutional neural network. Clin Oral Investig. 2022;26(6):4593–601.

    Article  PubMed  Google Scholar 

  78. Seol YJ, et al. A study on 3D deep learning-based automatic diagnosis of nasal fractures. Sensors. 2022;22(2):506.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Lin X, et al. Fracture R-CNN: an anchor-efficient anti-interference framework for skull fracture detection in CT images. Med Phys. 2022;49(11):7179–92.

    Article  PubMed  Google Scholar 

  80. SIIM. The pneumothorax challenge - society for imaging informatics in medicine. 2023. Available at https://siim.org/page/pneumothorax_challenge.

  81. RSNA. RSNA cervical spine fracture AI challenge. 2022. Available at https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/cervical-spine-fractures-ai-detection-challenge-2022.

  82. RSNA. RSNA intracranial hemorrhage detection challenge. 2019. Available at https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/RSNA-Intracranial-Hemorrhage-Detection-Challenge-2019.

  83. Pease M, et al. Outcome prediction in patients with severe traumatic brain injury using deep learning from head CT scans. Radiology. 2022;304(2):385–94.

    Article  PubMed  Google Scholar 

  84. Evans CS, et al. A natural language processing and machine learning approach to identification of incidental radiology findings in trauma patients discharged from the emergency department. Ann Emerg Med. 2022;81(3):262–9.

    Article  PubMed  Google Scholar 

  85. O’Neill TJ, et al. Active reprioritization of the reading worklist using artificial intelligence has a beneficial effect on the turnaround time for interpretation of head CT with intracranial hemorrhage. Radiol Artif Intell. 2021;3(2):e200024.

    Article  PubMed  Google Scholar 

  86. Stonko DP, et al. Artificial intelligence can predict daily trauma volume and average acuity. J Trauma Acute Care Surg. 2018;85(2):393–7.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Division of Abdominal Imaging, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA

    Mohamed Elbanan

  2. Division of Abdominal Imaging, Department of Radiology, University of Missouri, Columbia, MO, USA

    Mohamed Elbanan

  3. Division of Abdominal Imaging, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA

    Hersh Sagreiya

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  1. Mohamed Elbanan
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  2. Hersh Sagreiya
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Correspondence to Hersh Sagreiya .

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Editors and Affiliations

  1. Department of Medical Imaging, Penn Presbyterian Medical Center, Philadelphia, PA, USA

    Friedrich Knollmann

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Elbanan, M., Sagreiya, H. (2023). Artificial Intelligence in Trauma Imaging. In: Knollmann, F. (eds) Trauma Computed Tomography. Springer, Cham. https://doi.org/10.1007/978-3-031-45746-3_14

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  • DOI: https://doi.org/10.1007/978-3-031-45746-3_14

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