Abstract

Incorrect needle placement during an epidural block causes medical complications such as dural puncture or spinal cord injury. We propose a system combining an optical coherence tomography imaging probe with an automatic identification algorithm to objectively identify the epidural needle-tip position and thus reduce complications during epidural needle insertion. Eight quantitative features were extracted from each two-dimensional optical coherence tomography image during insertion of the needle tip from the skin surface to the epidural space. 847 in vivo optical coherence tomography images were obtained from three anesthetized piglets. The area under the receiver operating characteristic curve was used to quantify the discriminative ability of each feature. We found a combination of six image features—mean value of intensity, mean value with depth, entropy, mean absolute deviation, root mean square, and standard deviation—showed the highest differentiating performance with the shortest processing time. Finally, differentiation of the needle tip inside or outside the epidural space was automatically evaluated using five classifiers: k-nearest neighbor, linear discriminant analysis, quadratic discriminant analysis, linear support vector machines, and quadratic support vector machine. We adopted an 8-fold cross-validation strategy with five classifications. Quadratic support vector machine classification showed the highest sensitivity (97.5%), specificity (95%), and accuracy (96.2%) among the five classifiers. This study provides an intelligent method for objective identification of the epidural space that can increase the success rate of epidural needle insertion.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  1. S. Evron, D. Sessler, O. Sadan, M. Boaz, M. Glezerman, and T. Ezri, “Identification of the epidural space: loss of resistance with air, lidocaine, or the combination of air and lidocaine,” Anesth. Analg. 99(1), 245–250 (2004).
    [Crossref] [PubMed]
  2. C. Konrad, G. Schüpfer, M. Wietlisbach, and H. Gerber, “Learning manual skills in anesthesiology: is there a recommended number of cases for anesthetic procedures?” Anesth. Analg. 86(3), 635–639 (1998).
    [Crossref] [PubMed]
  3. G. A. McLeod, B. Munishankar, and M. O. Columb, “Is the clinical efficacy of epidural diamorphine concentration-dependent when used as analgesia for labour?” Br. J. Anaesth. 94(2), 229–233 (2005).
    [Crossref] [PubMed]
  4. J. R. A. Rigg, K. Jamrozik, P. S. Myles, B. S. Silbert, P. J. Peyton, R. W. Parsons, and K. S. Collins, “Epidural anaesthesia and analgesia and outcome of major surgery: a randomised trial,” Lancet 359(9314), 1276–1282 (2002).
    [Crossref] [PubMed]
  5. C.-K. Ting and Y. Chang, “Technique of fiber optics used to localize epidural space in piglets,” Opt. Express 18(11), 11138–11147 (2010).
    [Crossref] [PubMed]
  6. C.-K. Ting, M.-Y. Tsou, P.-T. Chen, K.-Y. Chang, M. S. Mandell, K.-H. Chan, and Y. Chang, “A new technique to assist epidural needle placement: fiberoptic-guided insertion using two wavelengths,” Anesthesiology 112(5), 1128–1135 (2010).
    [Crossref] [PubMed]
  7. J. P. Rathmell, A. E. Desjardins, M. van der Voort, B. H. Hendriks, R. Nachabe, S. Roggeveen, D. Babic, M. Söderman, M. Brynolf, and B. Holmström, “Identification of the epidural space with optical spectroscopy: an in vivo swine study,” Anesthesiology 113(6), 1406–1418 (2010).
    [Crossref] [PubMed]
  8. H. K. Chiang, Q. Zhou, M. S. Mandell, M.-Y. Tsou, S.-P. Lin, K. K. Shung, and C.-K. Ting, “Eyes in the needle: novel epidural needle with embedded high-frequency ultrasound transducer-epidural access in porcine model,” Anesthesiology 114(6), 1320–1324 (2011).
    [Crossref] [PubMed]
  9. B. C. Tsui, “The electrophysiological principles of the electrical stimulation test in the epidural compartment,” Can. J. Anaesth. 60(12), 1270–1271 (2013).
    [Crossref] [PubMed]
  10. W. C. Kuo, M. C. Kao, K. Y. Chang, W. N. Teng, M. Y. Tsou, Y. Chang, and C. K. Ting, “Fiber-needle swept-source optical coherence tomography system for the identification of the epidural space in piglets,” Anesthesiology 122(3), 585–594 (2015).
    [Crossref] [PubMed]
  11. J.-D. Haynes and G. Rees, “Decoding mental states from brain activity in humans,” Nat. Rev. Neurosci. 7(7), 523–534 (2006).
    [Crossref] [PubMed]
  12. N. Kriegeskorte and P. Bandettini, “Combining the tools: activation- and information-based fMRI analysis,” Neuroimage 38(4), 666–668 (2007).
    [Crossref] [PubMed]
  13. R. Quian Quiroga and S. Panzeri, “Extracting information from neuronal populations: information theory and decoding approaches,” Nat. Rev. Neurosci. 10(3), 173–185 (2009).
    [Crossref] [PubMed]
  14. U. Jean de Dieu and T. Ibrikci, “Diagnosing knee osteoarthritis using artificial neural networks and deep learning,” Biomedical Statistics and Informatics 2, 95–102 (2017).
  15. C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
    [Crossref]
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    [Crossref]
  17. G. McLachlan, Discriminant Analysis and Statistical Pattern Recognition (John Wiley & Sons, 2004).
  18. N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” Am. Stat. 46, 175–185 (1992).
  19. W. C. Kuo, M. C. Kao, M. Y. Tsou, and C. K. Ting, “In vivo images of the epidural space with two- and three-dimensional optical coherence tomography in a porcine model,” PLoS One 12(2), e0172149 (2017).
    [Crossref] [PubMed]
  20. M. Majnik and Z. Bosnić, “ROC analysis of classifiers in machine learning: A survey,” Intell. Data Anal. 17, 531–558 (2013).
  21. C. M. Rotello and T. Chen, “ROC curve analyses of eyewitness identification decisions: An analysis of the recent debate,” Cogn Res Princ Implic 1(1), 10 (2016).
    [Crossref] [PubMed]
  22. M. Kohl, “Performance measures in binary classification,” Int. J. Stat. Med. Res. 1, 79–81 (2012).
  23. J. B. Park, H. Chang, and J. K. Lee, “Quantitative analysis of transforming growth factor-beta 1 in ligamentum flavum of lumbar spinal stenosis and disc herniation,” Spine 26(21), E492–E495 (2001).
    [Crossref] [PubMed]
  24. A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys. 66(2), 239–303 (2003).
    [Crossref]

2017 (2)

U. Jean de Dieu and T. Ibrikci, “Diagnosing knee osteoarthritis using artificial neural networks and deep learning,” Biomedical Statistics and Informatics 2, 95–102 (2017).

W. C. Kuo, M. C. Kao, M. Y. Tsou, and C. K. Ting, “In vivo images of the epidural space with two- and three-dimensional optical coherence tomography in a porcine model,” PLoS One 12(2), e0172149 (2017).
[Crossref] [PubMed]

2016 (1)

C. M. Rotello and T. Chen, “ROC curve analyses of eyewitness identification decisions: An analysis of the recent debate,” Cogn Res Princ Implic 1(1), 10 (2016).
[Crossref] [PubMed]

2015 (1)

W. C. Kuo, M. C. Kao, K. Y. Chang, W. N. Teng, M. Y. Tsou, Y. Chang, and C. K. Ting, “Fiber-needle swept-source optical coherence tomography system for the identification of the epidural space in piglets,” Anesthesiology 122(3), 585–594 (2015).
[Crossref] [PubMed]

2013 (2)

M. Majnik and Z. Bosnić, “ROC analysis of classifiers in machine learning: A survey,” Intell. Data Anal. 17, 531–558 (2013).

B. C. Tsui, “The electrophysiological principles of the electrical stimulation test in the epidural compartment,” Can. J. Anaesth. 60(12), 1270–1271 (2013).
[Crossref] [PubMed]

2012 (1)

M. Kohl, “Performance measures in binary classification,” Int. J. Stat. Med. Res. 1, 79–81 (2012).

2011 (1)

H. K. Chiang, Q. Zhou, M. S. Mandell, M.-Y. Tsou, S.-P. Lin, K. K. Shung, and C.-K. Ting, “Eyes in the needle: novel epidural needle with embedded high-frequency ultrasound transducer-epidural access in porcine model,” Anesthesiology 114(6), 1320–1324 (2011).
[Crossref] [PubMed]

2010 (3)

C.-K. Ting and Y. Chang, “Technique of fiber optics used to localize epidural space in piglets,” Opt. Express 18(11), 11138–11147 (2010).
[Crossref] [PubMed]

C.-K. Ting, M.-Y. Tsou, P.-T. Chen, K.-Y. Chang, M. S. Mandell, K.-H. Chan, and Y. Chang, “A new technique to assist epidural needle placement: fiberoptic-guided insertion using two wavelengths,” Anesthesiology 112(5), 1128–1135 (2010).
[Crossref] [PubMed]

J. P. Rathmell, A. E. Desjardins, M. van der Voort, B. H. Hendriks, R. Nachabe, S. Roggeveen, D. Babic, M. Söderman, M. Brynolf, and B. Holmström, “Identification of the epidural space with optical spectroscopy: an in vivo swine study,” Anesthesiology 113(6), 1406–1418 (2010).
[Crossref] [PubMed]

2009 (1)

R. Quian Quiroga and S. Panzeri, “Extracting information from neuronal populations: information theory and decoding approaches,” Nat. Rev. Neurosci. 10(3), 173–185 (2009).
[Crossref] [PubMed]

2007 (1)

N. Kriegeskorte and P. Bandettini, “Combining the tools: activation- and information-based fMRI analysis,” Neuroimage 38(4), 666–668 (2007).
[Crossref] [PubMed]

2006 (1)

J.-D. Haynes and G. Rees, “Decoding mental states from brain activity in humans,” Nat. Rev. Neurosci. 7(7), 523–534 (2006).
[Crossref] [PubMed]

2005 (1)

G. A. McLeod, B. Munishankar, and M. O. Columb, “Is the clinical efficacy of epidural diamorphine concentration-dependent when used as analgesia for labour?” Br. J. Anaesth. 94(2), 229–233 (2005).
[Crossref] [PubMed]

2004 (1)

S. Evron, D. Sessler, O. Sadan, M. Boaz, M. Glezerman, and T. Ezri, “Identification of the epidural space: loss of resistance with air, lidocaine, or the combination of air and lidocaine,” Anesth. Analg. 99(1), 245–250 (2004).
[Crossref] [PubMed]

2003 (1)

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys. 66(2), 239–303 (2003).
[Crossref]

2002 (1)

J. R. A. Rigg, K. Jamrozik, P. S. Myles, B. S. Silbert, P. J. Peyton, R. W. Parsons, and K. S. Collins, “Epidural anaesthesia and analgesia and outcome of major surgery: a randomised trial,” Lancet 359(9314), 1276–1282 (2002).
[Crossref] [PubMed]

2001 (1)

J. B. Park, H. Chang, and J. K. Lee, “Quantitative analysis of transforming growth factor-beta 1 in ligamentum flavum of lumbar spinal stenosis and disc herniation,” Spine 26(21), E492–E495 (2001).
[Crossref] [PubMed]

1998 (1)

C. Konrad, G. Schüpfer, M. Wietlisbach, and H. Gerber, “Learning manual skills in anesthesiology: is there a recommended number of cases for anesthetic procedures?” Anesth. Analg. 86(3), 635–639 (1998).
[Crossref] [PubMed]

1995 (1)

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[Crossref]

1992 (1)

N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” Am. Stat. 46, 175–185 (1992).

1989 (1)

J. H. Friedman, “Regularized discriminant analysis,” J. Am. Stat. Assoc. 84(405), 165–175 (1989).
[Crossref]

Altman, N. S.

N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” Am. Stat. 46, 175–185 (1992).

Babic, D.

J. P. Rathmell, A. E. Desjardins, M. van der Voort, B. H. Hendriks, R. Nachabe, S. Roggeveen, D. Babic, M. Söderman, M. Brynolf, and B. Holmström, “Identification of the epidural space with optical spectroscopy: an in vivo swine study,” Anesthesiology 113(6), 1406–1418 (2010).
[Crossref] [PubMed]

Bandettini, P.

N. Kriegeskorte and P. Bandettini, “Combining the tools: activation- and information-based fMRI analysis,” Neuroimage 38(4), 666–668 (2007).
[Crossref] [PubMed]

Boaz, M.

S. Evron, D. Sessler, O. Sadan, M. Boaz, M. Glezerman, and T. Ezri, “Identification of the epidural space: loss of resistance with air, lidocaine, or the combination of air and lidocaine,” Anesth. Analg. 99(1), 245–250 (2004).
[Crossref] [PubMed]

Bosnic, Z.

M. Majnik and Z. Bosnić, “ROC analysis of classifiers in machine learning: A survey,” Intell. Data Anal. 17, 531–558 (2013).

Brynolf, M.

J. P. Rathmell, A. E. Desjardins, M. van der Voort, B. H. Hendriks, R. Nachabe, S. Roggeveen, D. Babic, M. Söderman, M. Brynolf, and B. Holmström, “Identification of the epidural space with optical spectroscopy: an in vivo swine study,” Anesthesiology 113(6), 1406–1418 (2010).
[Crossref] [PubMed]

Chan, K.-H.

C.-K. Ting, M.-Y. Tsou, P.-T. Chen, K.-Y. Chang, M. S. Mandell, K.-H. Chan, and Y. Chang, “A new technique to assist epidural needle placement: fiberoptic-guided insertion using two wavelengths,” Anesthesiology 112(5), 1128–1135 (2010).
[Crossref] [PubMed]

Chang, H.

J. B. Park, H. Chang, and J. K. Lee, “Quantitative analysis of transforming growth factor-beta 1 in ligamentum flavum of lumbar spinal stenosis and disc herniation,” Spine 26(21), E492–E495 (2001).
[Crossref] [PubMed]

Chang, K. Y.

W. C. Kuo, M. C. Kao, K. Y. Chang, W. N. Teng, M. Y. Tsou, Y. Chang, and C. K. Ting, “Fiber-needle swept-source optical coherence tomography system for the identification of the epidural space in piglets,” Anesthesiology 122(3), 585–594 (2015).
[Crossref] [PubMed]

Chang, K.-Y.

C.-K. Ting, M.-Y. Tsou, P.-T. Chen, K.-Y. Chang, M. S. Mandell, K.-H. Chan, and Y. Chang, “A new technique to assist epidural needle placement: fiberoptic-guided insertion using two wavelengths,” Anesthesiology 112(5), 1128–1135 (2010).
[Crossref] [PubMed]

Chang, Y.

W. C. Kuo, M. C. Kao, K. Y. Chang, W. N. Teng, M. Y. Tsou, Y. Chang, and C. K. Ting, “Fiber-needle swept-source optical coherence tomography system for the identification of the epidural space in piglets,” Anesthesiology 122(3), 585–594 (2015).
[Crossref] [PubMed]

C.-K. Ting, M.-Y. Tsou, P.-T. Chen, K.-Y. Chang, M. S. Mandell, K.-H. Chan, and Y. Chang, “A new technique to assist epidural needle placement: fiberoptic-guided insertion using two wavelengths,” Anesthesiology 112(5), 1128–1135 (2010).
[Crossref] [PubMed]

C.-K. Ting and Y. Chang, “Technique of fiber optics used to localize epidural space in piglets,” Opt. Express 18(11), 11138–11147 (2010).
[Crossref] [PubMed]

Chen, P.-T.

C.-K. Ting, M.-Y. Tsou, P.-T. Chen, K.-Y. Chang, M. S. Mandell, K.-H. Chan, and Y. Chang, “A new technique to assist epidural needle placement: fiberoptic-guided insertion using two wavelengths,” Anesthesiology 112(5), 1128–1135 (2010).
[Crossref] [PubMed]

Chen, T.

C. M. Rotello and T. Chen, “ROC curve analyses of eyewitness identification decisions: An analysis of the recent debate,” Cogn Res Princ Implic 1(1), 10 (2016).
[Crossref] [PubMed]

Chiang, H. K.

H. K. Chiang, Q. Zhou, M. S. Mandell, M.-Y. Tsou, S.-P. Lin, K. K. Shung, and C.-K. Ting, “Eyes in the needle: novel epidural needle with embedded high-frequency ultrasound transducer-epidural access in porcine model,” Anesthesiology 114(6), 1320–1324 (2011).
[Crossref] [PubMed]

Collins, K. S.

J. R. A. Rigg, K. Jamrozik, P. S. Myles, B. S. Silbert, P. J. Peyton, R. W. Parsons, and K. S. Collins, “Epidural anaesthesia and analgesia and outcome of major surgery: a randomised trial,” Lancet 359(9314), 1276–1282 (2002).
[Crossref] [PubMed]

Columb, M. O.

G. A. McLeod, B. Munishankar, and M. O. Columb, “Is the clinical efficacy of epidural diamorphine concentration-dependent when used as analgesia for labour?” Br. J. Anaesth. 94(2), 229–233 (2005).
[Crossref] [PubMed]

Cortes, C.

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[Crossref]

Desjardins, A. E.

J. P. Rathmell, A. E. Desjardins, M. van der Voort, B. H. Hendriks, R. Nachabe, S. Roggeveen, D. Babic, M. Söderman, M. Brynolf, and B. Holmström, “Identification of the epidural space with optical spectroscopy: an in vivo swine study,” Anesthesiology 113(6), 1406–1418 (2010).
[Crossref] [PubMed]

Drexler, W.

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys. 66(2), 239–303 (2003).
[Crossref]

Evron, S.

S. Evron, D. Sessler, O. Sadan, M. Boaz, M. Glezerman, and T. Ezri, “Identification of the epidural space: loss of resistance with air, lidocaine, or the combination of air and lidocaine,” Anesth. Analg. 99(1), 245–250 (2004).
[Crossref] [PubMed]

Ezri, T.

S. Evron, D. Sessler, O. Sadan, M. Boaz, M. Glezerman, and T. Ezri, “Identification of the epidural space: loss of resistance with air, lidocaine, or the combination of air and lidocaine,” Anesth. Analg. 99(1), 245–250 (2004).
[Crossref] [PubMed]

Fercher, A. F.

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys. 66(2), 239–303 (2003).
[Crossref]

Friedman, J. H.

J. H. Friedman, “Regularized discriminant analysis,” J. Am. Stat. Assoc. 84(405), 165–175 (1989).
[Crossref]

Gerber, H.

C. Konrad, G. Schüpfer, M. Wietlisbach, and H. Gerber, “Learning manual skills in anesthesiology: is there a recommended number of cases for anesthetic procedures?” Anesth. Analg. 86(3), 635–639 (1998).
[Crossref] [PubMed]

Glezerman, M.

S. Evron, D. Sessler, O. Sadan, M. Boaz, M. Glezerman, and T. Ezri, “Identification of the epidural space: loss of resistance with air, lidocaine, or the combination of air and lidocaine,” Anesth. Analg. 99(1), 245–250 (2004).
[Crossref] [PubMed]

Haynes, J.-D.

J.-D. Haynes and G. Rees, “Decoding mental states from brain activity in humans,” Nat. Rev. Neurosci. 7(7), 523–534 (2006).
[Crossref] [PubMed]

Hendriks, B. H.

J. P. Rathmell, A. E. Desjardins, M. van der Voort, B. H. Hendriks, R. Nachabe, S. Roggeveen, D. Babic, M. Söderman, M. Brynolf, and B. Holmström, “Identification of the epidural space with optical spectroscopy: an in vivo swine study,” Anesthesiology 113(6), 1406–1418 (2010).
[Crossref] [PubMed]

Hitzenberger, C. K.

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys. 66(2), 239–303 (2003).
[Crossref]

Holmström, B.

J. P. Rathmell, A. E. Desjardins, M. van der Voort, B. H. Hendriks, R. Nachabe, S. Roggeveen, D. Babic, M. Söderman, M. Brynolf, and B. Holmström, “Identification of the epidural space with optical spectroscopy: an in vivo swine study,” Anesthesiology 113(6), 1406–1418 (2010).
[Crossref] [PubMed]

Ibrikci, T.

U. Jean de Dieu and T. Ibrikci, “Diagnosing knee osteoarthritis using artificial neural networks and deep learning,” Biomedical Statistics and Informatics 2, 95–102 (2017).

Jamrozik, K.

J. R. A. Rigg, K. Jamrozik, P. S. Myles, B. S. Silbert, P. J. Peyton, R. W. Parsons, and K. S. Collins, “Epidural anaesthesia and analgesia and outcome of major surgery: a randomised trial,” Lancet 359(9314), 1276–1282 (2002).
[Crossref] [PubMed]

Jean de Dieu, U.

U. Jean de Dieu and T. Ibrikci, “Diagnosing knee osteoarthritis using artificial neural networks and deep learning,” Biomedical Statistics and Informatics 2, 95–102 (2017).

Kao, M. C.

W. C. Kuo, M. C. Kao, M. Y. Tsou, and C. K. Ting, “In vivo images of the epidural space with two- and three-dimensional optical coherence tomography in a porcine model,” PLoS One 12(2), e0172149 (2017).
[Crossref] [PubMed]

W. C. Kuo, M. C. Kao, K. Y. Chang, W. N. Teng, M. Y. Tsou, Y. Chang, and C. K. Ting, “Fiber-needle swept-source optical coherence tomography system for the identification of the epidural space in piglets,” Anesthesiology 122(3), 585–594 (2015).
[Crossref] [PubMed]

Kohl, M.

M. Kohl, “Performance measures in binary classification,” Int. J. Stat. Med. Res. 1, 79–81 (2012).

Konrad, C.

C. Konrad, G. Schüpfer, M. Wietlisbach, and H. Gerber, “Learning manual skills in anesthesiology: is there a recommended number of cases for anesthetic procedures?” Anesth. Analg. 86(3), 635–639 (1998).
[Crossref] [PubMed]

Kriegeskorte, N.

N. Kriegeskorte and P. Bandettini, “Combining the tools: activation- and information-based fMRI analysis,” Neuroimage 38(4), 666–668 (2007).
[Crossref] [PubMed]

Kuo, W. C.

W. C. Kuo, M. C. Kao, M. Y. Tsou, and C. K. Ting, “In vivo images of the epidural space with two- and three-dimensional optical coherence tomography in a porcine model,” PLoS One 12(2), e0172149 (2017).
[Crossref] [PubMed]

W. C. Kuo, M. C. Kao, K. Y. Chang, W. N. Teng, M. Y. Tsou, Y. Chang, and C. K. Ting, “Fiber-needle swept-source optical coherence tomography system for the identification of the epidural space in piglets,” Anesthesiology 122(3), 585–594 (2015).
[Crossref] [PubMed]

Lasser, T.

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys. 66(2), 239–303 (2003).
[Crossref]

Lee, J. K.

J. B. Park, H. Chang, and J. K. Lee, “Quantitative analysis of transforming growth factor-beta 1 in ligamentum flavum of lumbar spinal stenosis and disc herniation,” Spine 26(21), E492–E495 (2001).
[Crossref] [PubMed]

Lin, S.-P.

H. K. Chiang, Q. Zhou, M. S. Mandell, M.-Y. Tsou, S.-P. Lin, K. K. Shung, and C.-K. Ting, “Eyes in the needle: novel epidural needle with embedded high-frequency ultrasound transducer-epidural access in porcine model,” Anesthesiology 114(6), 1320–1324 (2011).
[Crossref] [PubMed]

Majnik, M.

M. Majnik and Z. Bosnić, “ROC analysis of classifiers in machine learning: A survey,” Intell. Data Anal. 17, 531–558 (2013).

Mandell, M. S.

H. K. Chiang, Q. Zhou, M. S. Mandell, M.-Y. Tsou, S.-P. Lin, K. K. Shung, and C.-K. Ting, “Eyes in the needle: novel epidural needle with embedded high-frequency ultrasound transducer-epidural access in porcine model,” Anesthesiology 114(6), 1320–1324 (2011).
[Crossref] [PubMed]

C.-K. Ting, M.-Y. Tsou, P.-T. Chen, K.-Y. Chang, M. S. Mandell, K.-H. Chan, and Y. Chang, “A new technique to assist epidural needle placement: fiberoptic-guided insertion using two wavelengths,” Anesthesiology 112(5), 1128–1135 (2010).
[Crossref] [PubMed]

McLeod, G. A.

G. A. McLeod, B. Munishankar, and M. O. Columb, “Is the clinical efficacy of epidural diamorphine concentration-dependent when used as analgesia for labour?” Br. J. Anaesth. 94(2), 229–233 (2005).
[Crossref] [PubMed]

Munishankar, B.

G. A. McLeod, B. Munishankar, and M. O. Columb, “Is the clinical efficacy of epidural diamorphine concentration-dependent when used as analgesia for labour?” Br. J. Anaesth. 94(2), 229–233 (2005).
[Crossref] [PubMed]

Myles, P. S.

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J. B. Park, H. Chang, and J. K. Lee, “Quantitative analysis of transforming growth factor-beta 1 in ligamentum flavum of lumbar spinal stenosis and disc herniation,” Spine 26(21), E492–E495 (2001).
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J. R. A. Rigg, K. Jamrozik, P. S. Myles, B. S. Silbert, P. J. Peyton, R. W. Parsons, and K. S. Collins, “Epidural anaesthesia and analgesia and outcome of major surgery: a randomised trial,” Lancet 359(9314), 1276–1282 (2002).
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J. R. A. Rigg, K. Jamrozik, P. S. Myles, B. S. Silbert, P. J. Peyton, R. W. Parsons, and K. S. Collins, “Epidural anaesthesia and analgesia and outcome of major surgery: a randomised trial,” Lancet 359(9314), 1276–1282 (2002).
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J. P. Rathmell, A. E. Desjardins, M. van der Voort, B. H. Hendriks, R. Nachabe, S. Roggeveen, D. Babic, M. Söderman, M. Brynolf, and B. Holmström, “Identification of the epidural space with optical spectroscopy: an in vivo swine study,” Anesthesiology 113(6), 1406–1418 (2010).
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J.-D. Haynes and G. Rees, “Decoding mental states from brain activity in humans,” Nat. Rev. Neurosci. 7(7), 523–534 (2006).
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J. R. A. Rigg, K. Jamrozik, P. S. Myles, B. S. Silbert, P. J. Peyton, R. W. Parsons, and K. S. Collins, “Epidural anaesthesia and analgesia and outcome of major surgery: a randomised trial,” Lancet 359(9314), 1276–1282 (2002).
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J. P. Rathmell, A. E. Desjardins, M. van der Voort, B. H. Hendriks, R. Nachabe, S. Roggeveen, D. Babic, M. Söderman, M. Brynolf, and B. Holmström, “Identification of the epidural space with optical spectroscopy: an in vivo swine study,” Anesthesiology 113(6), 1406–1418 (2010).
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S. Evron, D. Sessler, O. Sadan, M. Boaz, M. Glezerman, and T. Ezri, “Identification of the epidural space: loss of resistance with air, lidocaine, or the combination of air and lidocaine,” Anesth. Analg. 99(1), 245–250 (2004).
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J. R. A. Rigg, K. Jamrozik, P. S. Myles, B. S. Silbert, P. J. Peyton, R. W. Parsons, and K. S. Collins, “Epidural anaesthesia and analgesia and outcome of major surgery: a randomised trial,” Lancet 359(9314), 1276–1282 (2002).
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J. P. Rathmell, A. E. Desjardins, M. van der Voort, B. H. Hendriks, R. Nachabe, S. Roggeveen, D. Babic, M. Söderman, M. Brynolf, and B. Holmström, “Identification of the epidural space with optical spectroscopy: an in vivo swine study,” Anesthesiology 113(6), 1406–1418 (2010).
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W. C. Kuo, M. C. Kao, K. Y. Chang, W. N. Teng, M. Y. Tsou, Y. Chang, and C. K. Ting, “Fiber-needle swept-source optical coherence tomography system for the identification of the epidural space in piglets,” Anesthesiology 122(3), 585–594 (2015).
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W. C. Kuo, M. C. Kao, M. Y. Tsou, and C. K. Ting, “In vivo images of the epidural space with two- and three-dimensional optical coherence tomography in a porcine model,” PLoS One 12(2), e0172149 (2017).
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W. C. Kuo, M. C. Kao, K. Y. Chang, W. N. Teng, M. Y. Tsou, Y. Chang, and C. K. Ting, “Fiber-needle swept-source optical coherence tomography system for the identification of the epidural space in piglets,” Anesthesiology 122(3), 585–594 (2015).
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H. K. Chiang, Q. Zhou, M. S. Mandell, M.-Y. Tsou, S.-P. Lin, K. K. Shung, and C.-K. Ting, “Eyes in the needle: novel epidural needle with embedded high-frequency ultrasound transducer-epidural access in porcine model,” Anesthesiology 114(6), 1320–1324 (2011).
[Crossref] [PubMed]

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C.-K. Ting and Y. Chang, “Technique of fiber optics used to localize epidural space in piglets,” Opt. Express 18(11), 11138–11147 (2010).
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W. C. Kuo, M. C. Kao, M. Y. Tsou, and C. K. Ting, “In vivo images of the epidural space with two- and three-dimensional optical coherence tomography in a porcine model,” PLoS One 12(2), e0172149 (2017).
[Crossref] [PubMed]

W. C. Kuo, M. C. Kao, K. Y. Chang, W. N. Teng, M. Y. Tsou, Y. Chang, and C. K. Ting, “Fiber-needle swept-source optical coherence tomography system for the identification of the epidural space in piglets,” Anesthesiology 122(3), 585–594 (2015).
[Crossref] [PubMed]

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H. K. Chiang, Q. Zhou, M. S. Mandell, M.-Y. Tsou, S.-P. Lin, K. K. Shung, and C.-K. Ting, “Eyes in the needle: novel epidural needle with embedded high-frequency ultrasound transducer-epidural access in porcine model,” Anesthesiology 114(6), 1320–1324 (2011).
[Crossref] [PubMed]

C.-K. Ting, M.-Y. Tsou, P.-T. Chen, K.-Y. Chang, M. S. Mandell, K.-H. Chan, and Y. Chang, “A new technique to assist epidural needle placement: fiberoptic-guided insertion using two wavelengths,” Anesthesiology 112(5), 1128–1135 (2010).
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J. P. Rathmell, A. E. Desjardins, M. van der Voort, B. H. Hendriks, R. Nachabe, S. Roggeveen, D. Babic, M. Söderman, M. Brynolf, and B. Holmström, “Identification of the epidural space with optical spectroscopy: an in vivo swine study,” Anesthesiology 113(6), 1406–1418 (2010).
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[Crossref] [PubMed]

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H. K. Chiang, Q. Zhou, M. S. Mandell, M.-Y. Tsou, S.-P. Lin, K. K. Shung, and C.-K. Ting, “Eyes in the needle: novel epidural needle with embedded high-frequency ultrasound transducer-epidural access in porcine model,” Anesthesiology 114(6), 1320–1324 (2011).
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[Crossref] [PubMed]

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[Crossref] [PubMed]

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C.-K. Ting, M.-Y. Tsou, P.-T. Chen, K.-Y. Chang, M. S. Mandell, K.-H. Chan, and Y. Chang, “A new technique to assist epidural needle placement: fiberoptic-guided insertion using two wavelengths,” Anesthesiology 112(5), 1128–1135 (2010).
[Crossref] [PubMed]

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[Crossref] [PubMed]

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[Crossref] [PubMed]

W. C. Kuo, M. C. Kao, K. Y. Chang, W. N. Teng, M. Y. Tsou, Y. Chang, and C. K. Ting, “Fiber-needle swept-source optical coherence tomography system for the identification of the epidural space in piglets,” Anesthesiology 122(3), 585–594 (2015).
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C. M. Rotello and T. Chen, “ROC curve analyses of eyewitness identification decisions: An analysis of the recent debate,” Cogn Res Princ Implic 1(1), 10 (2016).
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J. R. A. Rigg, K. Jamrozik, P. S. Myles, B. S. Silbert, P. J. Peyton, R. W. Parsons, and K. S. Collins, “Epidural anaesthesia and analgesia and outcome of major surgery: a randomised trial,” Lancet 359(9314), 1276–1282 (2002).
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C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
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J.-D. Haynes and G. Rees, “Decoding mental states from brain activity in humans,” Nat. Rev. Neurosci. 7(7), 523–534 (2006).
[Crossref] [PubMed]

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W. C. Kuo, M. C. Kao, M. Y. Tsou, and C. K. Ting, “In vivo images of the epidural space with two- and three-dimensional optical coherence tomography in a porcine model,” PLoS One 12(2), e0172149 (2017).
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J. B. Park, H. Chang, and J. K. Lee, “Quantitative analysis of transforming growth factor-beta 1 in ligamentum flavum of lumbar spinal stenosis and disc herniation,” Spine 26(21), E492–E495 (2001).
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Figures (7)

Fig. 1
Fig. 1 (A) Schematic of the SSOCT system where the obtained images were processed by an automatic identification (AI) program. A/D: analog-to-digital converter. (B)The design of an OCT probe. (C) Representative tomograms of different structures as the needle probe moves from the muscle toward the ES in one insertion path.
Fig. 2
Fig. 2 The flowchart including the training and test process. QSVM: Quadratic support vector machine, LSVM: Linear support vector machines, QDA: Quadratic discriminant analysis, LDA: Linear discriminant analysis, KNN: K-nearest neighbor.
Fig. 3
Fig. 3 In vivo representative images of epidural space (ES), ligamentum flavum (LF), and muscle from three piglets’ experiments.
Fig. 4
Fig. 4 The distribution of eight features extracted from total 847 OCT images which include the ES (246), LF (152), and muscle (449) structure. The red points represent tissues inside the ES (IN). The black points indicate muscle tissue, which is present outside the ES (OUT).
Fig. 5
Fig. 5 (A) Receiver operating characteristics (ROC) curves showing the capacity of using the eight image features. (B) AUC percentage of these eight features where six of them with AUC above 0.7 (i.e., orange chart).
Fig. 6
Fig. 6 (A) The processing time for calculating each feature from one OCT image. Orange charts represent the processing time for calculating the feature with AUC above 0.7. (B) The total processing time of extracting eight features and six selected features.
Fig. 7
Fig. 7 Representative images selected to illustrate misclassification situation. (A) Blood presence, (B) both LF and ES within one OCT image, (C) ES with lots of fat, and (D-F) muscle tissue with non-uniform fiber distribution.

Tables (2)

Tables Icon

Table 1 Different classification method in 8 features. QSVM, LSVM, and LDA showed the highest sensitivity, specificity, and accuracy (>96%) were highlighted by red font.

Tables Icon

Table 2 Different classification method in 6 features.

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