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Toward automated interpretable AAST grading for blunt splenic injury

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Abstract

Background

The American Association for the Surgery of Trauma (AAST) splenic organ injury scale (OIS) is the most frequently used CT-based grading system for blunt splenic trauma. However, reported inter-rater agreement is modest, and an algorithm that objectively automates grading based on transparent and verifiable criteria could serve as a high-trust diagnostic aid.

Purpose

To pilot the development of an automated interpretable multi-stage deep learning-based system to predict AAST grade from admission trauma CT.

Methods

Our pipeline includes 4 parts: (1) automated splenic localization, (2) Faster R-CNN-based detection of pseudoaneurysms (PSA) and active bleeds (AB), (3) nnU-Net segmentation and quantification of splenic parenchymal disruption (SPD), and (4) a directed graph that infers AAST grades from detection and segmentation results. Training and validation is performed on a dataset of adult patients (age ≥ 18) with voxelwise labeling, consensus AAST grading, and hemorrhage-related outcome data (n = 174).

Results

AAST classification agreement (weighted κ) between automated and consensus AAST grades was substantial (0.79). High-grade (IV and V) injuries were predicted with accuracy, positive predictive value, and negative predictive value of 92%, 95%, and 89%. The area under the curve for predicting hemorrhage control intervention was comparable between expert consensus and automated AAST grading (0.83 vs 0.88). The mean combined inference time for the pipeline was 96.9 s.

Conclusions

The results of our method were rapid and verifiable, with high agreement between automated and expert consensus grades. Diagnosis of high-grade lesions and prediction of hemorrhage control intervention produced accurate results in adult patients.

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References

  1. Dreizin D, Munera F (2012) Blunt polytrauma: evaluation with 64-section whole-body CT angiography. Radiographics 32(3):609–631

    Article  Google Scholar 

  2. Chahine AH, Gilyard S, Hanna TN, Fan S, Risk B, Johnson JO, Duszak R Jr, Newsome J, Xing M, Kokabi N (2021) Management of splenic trauma in contemporary clinical practice: a national trauma data bank study. Acad Radiol 28:S138–S147

    Article  Google Scholar 

  3. Dreizin D, Champ K, Dattwyler M, Bodanapally U, Smith EB, Li G, Singh R, Wang Z, Liang Y (2022) Blunt splenic injury in adults: association between volumetric quantitative CT parameters and intervention. J Trauma Acute Care Surg. https://doi.org/10.1097/ta.0000000000003684

    Article  Google Scholar 

  4. Zarzaur BL, Kozar R, Myers JG, Claridge JA, Scalea TM, Neideen TA, Maung AA, Alarcon L, Corcos A, Kerwin A (2015) The splenic injury outcomes trial: an American Association for the Surgery of Trauma multi-institutional study. J Trauma Acute Care Surg 79(3):335–342

    Article  Google Scholar 

  5. Haan JM, Biffl W, Knudson MM, Davis KA, Oka T, Majercik S, Dicker R, Marder S, Scalea TM (2004) Committee WTAM-IT Splenic embolization revisited: a multicenter review. J Trauma Acute Care Surg 56(3):542–547

    Article  Google Scholar 

  6. Krausz MM, Hirsh M (2003) Bolus versus continuous fluid resuscitation and splenectomy for treatment of uncontrolled hemorrhagic shock after massive splenic injury. J Trauma Acute Care Surg 55(1):62–68

    Article  Google Scholar 

  7. Zarzaur BL, Kozar RA, Fabian TC, Coimbra R (2011) A survey of American Association for the Surgery of Trauma member practices in the management of blunt splenic injury. J Trauma Acute Care Surg 70(5):1026–1031

    Article  Google Scholar 

  8. Barquist ES, Pizano LR, Feuer W, Pappas PA, McKenney KA, LeBlang SD, Henry RP, Rivas LA, Cohn SM (2004) Inter-and intrarater reliability in computed axial tomographic grading of splenic injury: why so many grading scales? J Trauma Acute Care Surg 56(2):334–338

    Article  Google Scholar 

  9. Clark R, Hird K, Misur P, Ramsay D, Mendelson R (2011) CT grading scales for splenic injury: Why can’t we agree? J Med Imaging Radiat Oncol 55(2):163–169

    Article  Google Scholar 

  10. Banaste N, Caurier B, Bratan F, Bergerot J-F, Thomson V, Millet I (2018) Whole-body CT in patients with multiple traumas: factors leading to missed injury. Radiology 289(2):374–383

    Article  Google Scholar 

  11. Watchorn J, Miles R, Moore N (2013) The role of CT angiography in military trauma. Clin Radiol 68(1):39–46

    Article  CAS  Google Scholar 

  12. Glover M IV, Almeida RR, Schaefer PW, Lev MH, Mehan WA Jr (2017) Quantifying the impact of noninterpretive tasks on radiology report turn-around times. J Am Coll Radiol 14(11):1498–1503

    Article  Google Scholar 

  13. Hunter TB, Taljanovic MS, Krupinski E, Ovitt T, Stubbs AY (2007) Academic radiologists’ on-call and late-evening duties. J Am Coll Radiol 4(10):716–719

    Article  Google Scholar 

  14. Hanna TN, Loehfelm T, Khosa F, Rohatgi S, Johnson J-O (2016) Overnight shift work: factors contributing to diagnostic discrepancies. Emerg Radiol 23(1):41–47

    Article  Google Scholar 

  15. Cruz-Romero C, Agarwal S, Abujudeh HH, Thrall J, Hahn PF (2016) Spleen volume on CT and the effect of abdominal trauma. Emerg Radiol 23(4):315–323

    Article  Google Scholar 

  16. Wood A, Soroushmehr SR, Farzaneh N, Fessell D, Ward KR, Gryak J, Kahrobaei D, Na K. Fully automated spleen localization and segmentation using machine learning and 3D active contours. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): IEEE, 2018; p. 53–56.

  17. Dandin O, Teomete U, Osman O, Tulum G, Ergin T, Sabuncuoglu MZ (2016) Automated segmentation of the injured spleen. Int J Comput Assist Radiol Surg 11(3):351–368

    Article  Google Scholar 

  18. Wang J, Wood A, Gao C, Najarian K, Gryak J (2021) Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning. Entropy 23(4):382

    Article  Google Scholar 

  19. Teomete U, Tulum G, Ergin T, Cuce F, Koksal M, Dandin O, Osman O (2018) Automated computer-aided diagnosis of splenic lesions due to abdominal trauma. Hippokratia 22(2):80

    CAS  Google Scholar 

  20. DeGrave AJ, Janizek JD, Lee S-I (2021) AI for radiographic COVID-19 detection selects shortcuts over signal. Nature Machine Intelligence 3(7):610–619

    Article  Google Scholar 

  21. Zapaishchykova A, Dreizin D, Li Z, Wu JY, Roohi SF, Unberath M. An interpretable approach to automated severity scoring in pelvic trauma. arXiv preprint arXiv:210510238 2021.

  22. Chen H, Gomez C, Huang C-M, Unberath M. INTRPRT: A systematic review of and guidelines for designing and validating transparent AI in medical image analysis. arXiv preprint arXiv:211212596 2021.

  23. Vlontzos A, Rueckert D, Kainz B. A review of causality for learning algorithms in medical image analysis. arXiv preprint arXiv:220605498 2022.

  24. Arrieta AB, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, García S, Gil-López S, Molina D, Benjamins R (2020) Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion 58:82–115

    Article  Google Scholar 

  25. Kozar RA, Crandall M, Shanmuganathan K, Zarzaur BL, Coburn M, Cribari C, Kaups K, Schuster K, Tominaga GT, Committee APA (2018) Organ injury scaling 2018 update: spleen, liver, and kidney. J Trauma Acute Care Surg 85(6):1119–1122

    Article  Google Scholar 

  26. Boscak AR, Shanmuganathan K, Mirvis SE, Fleiter TR, Miller LA, Sliker CW, Steenburg SD, Alexander M (2013) Optimizing trauma multidetector CT protocol for blunt splenic injury: need for arterial and portal venous phase scans. Radiology 268(1):79–88

    Article  Google Scholar 

  27. Uyeda JW, LeBedis CA, Penn DR, Soto JA, Anderson SW (2014) Active hemorrhage and vascular injuries in splenic trauma: utility of the arterial phase in multidetector CT. Radiology 270(1):99–106

    Article  Google Scholar 

  28. Zhou Y, Dreizin D, Wang Y, Liu F, Shen W, Yuille AL. External attention assisted multi-phase splenic vascular injury segmentation with limited data. IEEE Transactions on Medical Imaging 2021.

  29. Antonelli M, Reinke A, Bakas S, Farahani K, Landman BA, Litjens G, Menze B, Ronneberger O, Summers RM, van Ginneken B. The medical segmentation decathlon. arXiv preprint arXiv:210605735 2021.

  30. Cooney R, Ku J, Cherry R, Maish GO III, Carney D, Scorza LB, Smith JS (2005) Limitations of splenic angioembolization in treating blunt splenic injury. J Trauma Acute Care Surg 59(4):926–932

    Article  Google Scholar 

  31. Bhullar IS, Frykberg ER, Siragusa D, Chesire D, Paul J, Tepas JJ III, Kerwin AJ (2012) Selective angiographic embolization of blunt splenic traumatic injuries in adults decreases failure rate of nonoperative management. J Trauma Acute Care Surg 72(5):1127–1134

    Article  Google Scholar 

  32. Crichton JCI, Naidoo K, Yet B, Brundage SI, Perkins Z (2017) The role of splenic angioembolization as an adjunct to nonoperative management of blunt splenic injuries: a systematic review and meta-analysis. J Trauma Acute Care Surg 83(5):934–943

    Article  Google Scholar 

  33. Requarth JA, D’Agostino RB Jr, Miller PR (2011) Nonoperative management of adult blunt splenic injury with and without splenic artery embolotherapy: a meta-analysis. J Trauma Acute Care Surg 71(4):898–903

    Article  Google Scholar 

  34. Moore EE, Cogbill TH, Jurkovich GJ, Shackford SR, Malangoni MA, Champion HR (1995) Organ injury scaling: spleen and liver (1994 revision). J Trauma Acute Care Surg 38(3):323–324

    Article  CAS  Google Scholar 

  35. Haan JM, Bochicchio GV, Kramer N, Scalea TM (2005) Nonoperative management of blunt splenic injury: a 5-year experience. J Trauma Acute Care Surg 58(3):492–498

    Article  Google Scholar 

  36. Miller PR, Chang MC, Hoth JJ, Mowery NT, Hildreth AN, Martin RS, Holmes JH, Meredith JW, Requarth JA (2014) Prospective trial of angiography and embolization for all grade III to V blunt splenic injuries: nonoperative management success rate is significantly improved. J Am Coll Surg 218(4):644–648

    Article  Google Scholar 

  37. Ren S, He K, Girshick R, Sun J. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015;28.

  38. Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203–211

  39. Xie Q, Luong M-T, Hovy E, Le QV. Self-training with noisy student improves imagenet classification. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition2020; p. 10687–10698.

  40. Consortium M. Project MONAI. Zenodo. Available online: https://zenodoorg/record/4323059#YXaMajgzaUk (accessed on 25 May 2020) 2020.

  41. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention: Springer, 2015; p. 234–241.

  42. Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: Common objects in context. Springer, European conference on computer vision, pp 740–755

    Google Scholar 

  43. Zhang R, Tian Z, Shen C, You M, Yan Y. Mask encoding for single shot instance segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition2020; p. 10226–10235.

  44. Wu Y, Kirillov A, Massa F, Lo W-Y, Girshick R. Detectron2 (2019). 2019.

  45. Bhangu A, Nepogodiev D, Lal N, Bowley DM (2012) Meta-analysis of predictive factors and outcomes for failure of non-operative management of blunt splenic trauma. Injury 43(9):1337–1346

    Article  Google Scholar 

  46. Dreizin D, Yu T, Motley K, Li G, Morrison JJ, Liang Y. Blunt splenic injury: assessment of follow-up CT utility using quantitative volumetry. Frontiers in radiology 2022:23.

  47. Dreizin D, Chen T, Liang Y, Zhou Y, Paes F, Wang Y, Yuille AL, Roth P, Champ K, Li G. Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis. Abdominal Radiology 2021:1–11.

  48. Dreizin D, Zhou Y, Fu S, Wang Y, Li G, Champ K, Siegel E, Wang Z, Chen T, Yuille AL (2020) A multiscale deep learning method for quantitative visualization of traumatic hemoperitoneum at CT: assessment of feasibility and comparison with subjective categorical estimation. Radiology Artificial Intelligence 2(6):e190220

    Article  Google Scholar 

  49. Dreizin D, Zhou Y, Chen T, Li G, Yuille AL, McLenithan A, Morrison JJ (2020) Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: potential role in personalized forecasting and decision support. J Trauma Acute Care Surg 88(3):425–433

    Article  Google Scholar 

  50. Dreizin D, Zhou Y, Zhang Y, Tirada N, Yuille AL (2020) Performance of a deep learning algorithm for automated segmentation and quantification of traumatic pelvic hematomas on CT. J Digit Imaging 33(1):243–251

    Article  Google Scholar 

  51. Dreizin D, Nixon B, Hu J, Albert B, Yan C, Yang G, Chen H, Liang Y, Kim N, Jeudy J. A pilot study of deep learning-based CT volumetry for traumatic hemothorax. Emergency Radiology 2022:1–8.

  52. Landis JR, Koch GG. The measurement of observer agreement for categorical data. biometrics 1977:159–174.

  53. Morell-Hofert D, Primavesi F, Fodor M, Gassner E, Kranebitter V, Braunwarth E, Haselbacher M, Nitsche UP, Schmid S, Blauth M (2020) Validation of the revised 2018 AAST-OIS classification and the CT severity index for prediction of operative management and survival in patients with blunt spleen and liver injuries. Eur Radiol 30(12):6570–6581

    Article  Google Scholar 

  54. Jeavons C, Hacking C, Beenen LF, Gunn ML (2018) A review of split-bolus single-pass CT in the assessment of trauma patients. Emerg Radiol 25(4):367–374

    Article  Google Scholar 

  55. Beenen LF, Sierink JC, Kolkman S, Nio CY, Saltzherr TP, Dijkgraaf MG, Goslings JC (2015) Split bolus technique in polytrauma: a prospective study on scan protocols for trauma analysis. Acta Radiol 56(7):873–880

    Article  Google Scholar 

  56. Lopez JM Jr, McGonagill PW, Gross JL, Hoth JJ, Chang MC, Parker K, Requarth JA, Miller PR (2015) Subcapsular hematoma in blunt splenic injury: a significant predictor of failure of nonoperative management. J Trauma Acute Care Surg 79(6):957–960

    Article  Google Scholar 

  57. Scatamacchia SA, Raptopoulos V, Fink MP, Silva WE (1989) Splenic trauma in adults: impact of CT grading on management. Radiology 171(3):725–729

    Article  CAS  Google Scholar 

  58. Rowell SE, Biffl WL, Brasel K, Moore EE, Albrecht RA, DeMoya M, Namias N, Schreiber MA, Cohen MJ, Shatz DV (2017) Western Trauma Association Critical Decisions in Trauma: Management of adult blunt splenic trauma—2016 updates. J Trauma Acute Care Surg 82(4):787–793

    Article  Google Scholar 

  59. Coccolini F, Montori G, Catena F, Kluger Y, Biffl W, Moore EE, Reva V, Bing C, Bala M, Fugazzola P (2017) Splenic trauma: WSES classification and guidelines for adult and pediatric patients. World J Emerg Surg 12(1):1–26

    Google Scholar 

  60. Boscak A, Shanmuganathan K (2012) Splenic trauma: what is new? Radiologic Clinics 50(1):105–122

    Google Scholar 

  61. Lee JT, Slade E, Uyeda J, Steenburg SD, Chong ST, Tsai R, Raptis D, Linnau KF, Chinapuvvula NR, Dattwyler MP (2021) American Society of Emergency Radiology multicenter blunt splenic trauma study: CT and clinical findings. Radiology 299(1):122

    Article  Google Scholar 

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Funding

NIH K08 EB027141-01A1 (PI: David Dreizin, MD).

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Authors and Affiliations

  1. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    Haomin Chen & Mathias Unberath

  2. Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA

    David Dreizin

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  1. Haomin Chen
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Correspondence to David Dreizin.

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Chen, H., Unberath, M. & Dreizin, D. Toward automated interpretable AAST grading for blunt splenic injury. Emerg Radiol 30, 41–50 (2023). https://doi.org/10.1007/s10140-022-02099-1

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