Literature DB >> 26172532

Automated Detection, Localization, and Classification of Traumatic Vertebral Body Fractures in the Thoracic and Lumbar Spine at CT.

Joseph E Burns1, Jianhua Yao1, Hector Muñoz1, Ronald M Summers1.   

Abstract

PURPOSE: To design and validate a fully automated computer system for the detection and anatomic localization of traumatic thoracic and lumbar vertebral body fractures at computed tomography (CT).
MATERIALS AND METHODS: This retrospective study was HIPAA compliant. Institutional review board approval was obtained, and informed consent was waived. CT examinations in 104 patients (mean age, 34.4 years; range, 14-88 years; 32 women, 72 men), consisting of 94 examinations with positive findings for fractures (59 with vertebral body fractures) and 10 control examinations (without vertebral fractures), were performed. There were 141 thoracic and lumbar vertebral body fractures in the case set. The locations of fractures were marked and classified by a radiologist according to Denis column involvement. The CT data set was divided into training and testing subsets (37 and 67 subsets, respectively) for analysis by means of prototype software for fully automated spinal segmentation and fracture detection. Free-response receiver operating characteristic analysis was performed.
RESULTS: Training set sensitivity for detection and localization of fractures within each vertebra was 0.82 (28 of 34 findings; 95% confidence interval [CI]: 0.68, 0.90), with a false-positive rate of 2.5 findings per patient. The sensitivity for fracture localization to the correct vertebra was 0.88 (23 of 26 findings; 95% CI: 0.72, 0.96), with a false-positive rate of 1.3. Testing set sensitivity for the detection and localization of fractures within each vertebra was 0.81 (87 of 107 findings; 95% CI: 0.75, 0.87), with a false-positive rate of 2.7. The sensitivity for fracture localization to the correct vertebra was 0.92 (55 of 60 findings; 95% CI: 0.79, 0.94), with a false-positive rate of 1.6. The most common cause of false-positive findings was nutrient foramina (106 of 272 findings [39%]).
CONCLUSION: The fully automated computer system detects and anatomically localizes vertebral body fractures in the thoracic and lumbar spine on CT images with a high sensitivity and a low false-positive rate. © RSNA, 2015

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Year:  2015        PMID: 26172532      PMCID: PMC4699497          DOI: 10.1148/radiol.2015142346

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  25 in total

1.  Reliability of the thoracolumbar injury classification and severity score and comparison with the denis classification for injury to the thoracic and lumbar spine.

Authors:  Peter Lewkonia; Elizabeth Oddone Paolucci; Ken Thomas
Journal:  Spine (Phila Pa 1976)       Date:  2012-12-15       Impact factor: 3.468

Review 2.  Relationships between the Arbeitsgemeinschaft für Osteosynthesefragen Spine System and the Thoracolumbar Injury Classification System: an analysis of the literature.

Authors:  Andrei F Joaquim; Alpesh A Patel
Journal:  J Spinal Cord Med       Date:  2013-02-05       Impact factor: 1.985

Review 3.  Bone health and prostate cancer.

Authors:  P J Saylor; M R Smith
Journal:  Prostate Cancer Prostatic Dis       Date:  2009-11-10       Impact factor: 5.554

4.  The three column spine and its significance in the classification of acute thoracolumbar spinal injuries.

Authors:  F Denis
Journal:  Spine (Phila Pa 1976)       Date:  1983 Nov-Dec       Impact factor: 3.468

5.  Classification of thoracic and lumbar spine fractures: problems of reproducibility. A study of 53 patients using CT and MRI.

Authors:  F C Oner; L M P Ramos; R K J Simmermacher; P T D Kingma; C H Diekerhof; W J A Dhert; A J Verbout
Journal:  Eur Spine J       Date:  2002-01-29       Impact factor: 3.134

6.  A review of the TLICS system: a novel, user-friendly thoracolumbar trauma classification system.

Authors:  Jeffrey A Rihn; David T Anderson; Eric Harris; James Lawrence; Hakan Jonsson; Jared Wilsey; R John Hurlbert; Alexander R Vaccaro
Journal:  Acta Orthop       Date:  2008-08       Impact factor: 3.717

7.  The value of computed tomography in thoracolumbar fractures. An analysis of one hundred consecutive cases and a new classification.

Authors:  P C McAfee; H A Yuan; B E Fredrickson; J P Lubicky
Journal:  J Bone Joint Surg Am       Date:  1983-04       Impact factor: 5.284

8.  Assessment of osteoporotic vertebral fractures using specialized workflow software for 6-point morphometry.

Authors:  Giuseppe Guglielmi; Francesco Palmieri; Maria Grazia Placentino; Francesco D'Errico; Luca Pio Stoppino
Journal:  Eur J Radiol       Date:  2008-02-01       Impact factor: 3.528

9.  A comprehensive classification of thoracic and lumbar injuries.

Authors:  F Magerl; M Aebi; S D Gertzbein; J Harms; S Nazarian
Journal:  Eur Spine J       Date:  1994       Impact factor: 3.134

Review 10.  Thoracolumbar spine trauma: Evaluation and surgical decision-making.

Authors:  Andrei F Joaquim; Alpesh A Patel
Journal:  J Craniovertebr Junction Spine       Date:  2013-01
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  16 in total

Review 1.  Progress in Fully Automated Abdominal CT Interpretation.

Authors:  Ronald M Summers
Journal:  AJR Am J Roentgenol       Date:  2016-04-21       Impact factor: 3.959

Review 2.  Current Applications and Future Impact of Machine Learning in Radiology.

Authors:  Garry Choy; Omid Khalilzadeh; Mark Michalski; Synho Do; Anthony E Samir; Oleg S Pianykh; J Raymond Geis; Pari V Pandharipande; James A Brink; Keith J Dreyer
Journal:  Radiology       Date:  2018-06-26       Impact factor: 11.105

3.  [Imaging techniques in modern trauma diagnostics].

Authors:  T J Vogl; K Eichler; I Marzi; S Wutzler; K Zacharowski; C Frellessen
Journal:  Radiologe       Date:  2017-10       Impact factor: 0.635

4.  A multi-center milestone study of clinical vertebral CT segmentation.

Authors:  Jianhua Yao; Joseph E Burns; Daniel Forsberg; Alexander Seitel; Abtin Rasoulian; Purang Abolmaesumi; Kerstin Hammernik; Martin Urschler; Bulat Ibragimov; Robert Korez; Tomaž Vrtovec; Isaac Castro-Mateos; Jose M Pozo; Alejandro F Frangi; Ronald M Summers; Shuo Li
Journal:  Comput Med Imaging Graph       Date:  2016-01-02       Impact factor: 4.790

5.  [Imaging techniques in modern trauma diagnostics].

Authors:  T J Vogl; K Eichler; I Marzi; S Wutzler; K Zacharowski; C Frellessen
Journal:  Med Klin Intensivmed Notfmed       Date:  2017-10       Impact factor: 0.840

Review 6.  [Imaging techniques in modern trauma diagnostics].

Authors:  T J Vogl; K Eichler; I Marzi; S Wutzler; K Zacharowski; C Frellessen
Journal:  Unfallchirurg       Date:  2017-05       Impact factor: 1.000

Review 7.  Radiological images and machine learning: Trends, perspectives, and prospects.

Authors:  Zhenwei Zhang; Ervin Sejdić
Journal:  Comput Biol Med       Date:  2019-02-27       Impact factor: 4.589

8.  Assessment of a Deep Learning Algorithm for the Detection of Rib Fractures on Whole-Body Trauma Computed Tomography.

Authors:  Thomas Weikert; Luca Andre Noordtzij; Jens Bremerich; Bram Stieltjes; Victor Parmar; Joshy Cyriac; Gregor Sommer; Alexander Walter Sauter
Journal:  Korean J Radiol       Date:  2020-07       Impact factor: 3.500

9.  Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Cervical Spine Fractures.

Authors:  A F Voter; M E Larson; J W Garrett; J-P J Yu
Journal:  AJNR Am J Neuroradiol       Date:  2021-06-11       Impact factor: 4.966

10.  A Vertebral Segmentation Dataset with Fracture Grading.

Authors:  Maximilian T Löffler; Anjany Sekuboyina; Alina Jacob; Anna-Lena Grau; Andreas Scharr; Malek El Husseini; Mareike Kallweit; Claus Zimmer; Thomas Baum; Jan S Kirschke
Journal:  Radiol Artif Intell       Date:  2020-07-29
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