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AI for Automated Segmentation and Characterization of Median Nerve Volume

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Abstract

Purpose

Carpal tunnel syndrome (CTS) is characterized anatomically by enlargement of the median nerve (MN) at the wrist. To better understand the 3D morphology and volume of the enlargement, we studied its volume using automated segmentation of ultrasound (US) images in 10 volunteers and 4 patients diagnosed with CTS.

Method

US images were acquired axially for a 4 cm MN segment from the proximal carpal tunnel region to mid-forearm in 10 volunteers and 4 patients with CTS, yielding over 18,000 images. We used U-Net with ConvNet blocks to create a model of MN segmentation for CTS study, compared to manual measurements by two readers.

Results

The average Dice Similarity Coefficient (DSC) on the internal and external validation datasets was 0.82 and 0.81, respectively, and the area under the curve (AUC) was 0.92 and 0.88, respectively. The inter-reader correlation DSC was 0.83, and the AUC was 0.98. The correlation between U-Net and manual tracing was best when the MN was near the surface. A US phantom mimicking the MN, imaged at varied scanning speeds from 7 to 45 mm/s, showed the volume measurements were consistent.

Conclusion

Our AI model effectively segmented the MN to calculate MN volume, which can now be studied as a potential biomarker for CTS, along with the already established biomarker, cross-sectional area.

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Data availability

The data that support the findings of this study are not openly available due to reasons of sensitivity related to patient confidentiality and protected intellectual property, but are available, as permitted by law, from the corresponding author upon reasonable request. Data are located in controlled access data storage at Mayo Clinic.

References

  1. Tsamis, K. I., Kontogiannis, P., Gourgiotis, I., Ntabos, S., Sarmas, I., & Manis, G. (2021). Automatic electrodiagnosis of carpal tunnel syndrome using machine learning. Bioengineering (Basel). https://doi.org/10.3390/bioengineering8110181

    Article  PubMed  Google Scholar 

  2. Wong, S. M., Griffith, J. F., Hui, A. C. F., Lo, S. K., Fu, M., & Wong, K. S. (2004). Carpal tunnel syndrome: DIAGNOSTIC usefulness of sonography. Radiology, 232, 93–99.

    Article  PubMed  Google Scholar 

  3. Hosseini-Farid, M., Schrier, V. J. M. M., Starlinger, J., & Amadio, P. C. (2021). Carpal tunnel syndrome treatment and the subsequent alterations in median nerve transverse mobility. Journal of Ultrasound in Medicine, 40, 1555–1568.

    Article  PubMed  Google Scholar 

  4. Mooar, P. A., Doherty, W. J., Murray, J. N., Pezold, R., & Sevarino, K. S. (2018). Management of carpal tunnel syndrome. Journal of American Academy of Orthopaedic Surgeons, 26, e128–e130.

    Article  Google Scholar 

  5. Fowler, J. R., Gaughan, J. P., & Ilyas, A. M. (2011). The sensitivity and specificity of ultrasound for the diagnosis of carpal tunnel syndrome: A meta-analysis. Clinical Orthopaedics & Related Research. https://doi.org/10.1007/s11999-010-1637-5

    Article  Google Scholar 

  6. Fowler, J. R., Hirsch, D., & Kruse, K. (2015). The reliability of ultrasound measurements of the median nerve at the carpal tunnel inlet. The Journal of Hand Surgery. https://doi.org/10.1016/j.jhsa.2015.07.010

    Article  PubMed  Google Scholar 

  7. Gonzalez-Suarez, C. B., Buenavente, L. D., Cua, R. C. A., Fidel, M. B. C., Cabrera, J.-T.C., & Regala, C. F. G. (2018). Inter-rater and intra-rater reliability of sonographic median nerve and wrist measurements. Journal of Ultrasound, 26, 14–23.

    Google Scholar 

  8. Ghasemi-Esfe, A. R., Khalilzadeh, O., Vaziri-Bozorg, S. M., Jajroudi, M., Shakiba, M., Mazloumi, M., et al. (2011). Color and power Doppler US for diagnosing carpal tunnel syndrome and determining its severity: A quantitative image processing method. Radiology, 261, 499–506.

    Article  PubMed  Google Scholar 

  9. El Miedany, Y., El Gaafary, M., Youssef, S., Ahmed, I., & Nasr, A. (2015). Ultrasound assessment of the median nerve: A biomarker that can help in setting a treat to target approach tailored for carpal tunnel syndrome patients. Springerplus, 4, 13.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Sasaki, T., Nimura, A., Kuroiwa, T., Koyama, T., Okawa, A., & Fujita, K. (2022). Assessment of pain during nerve conduction studies in patients with carpal tunnel syndrome. Journal of Hand Surgery Global Online. https://doi.org/10.1016/j.jhsg.2021.12.004

    Article  PubMed  PubMed Central  Google Scholar 

  11. Finsen, V., & Russwurm, H. (2001). Neurophysiology not required before surgery for typical carpal tunnel syndrome. The Journal of Hand Surgery: British, 26, 61–64.

    Article  CAS  Google Scholar 

  12. Sasaki, T., Koyama, T., Kuroiwa, T., Nimura, A., Okawa, A., Wakabayashi, Y., et al. (2022). Evaluation of the existing electrophysiological severity classifications in carpal tunnel syndrome. Journal of Clinical Medical Research, 11, 1685.

    Google Scholar 

  13. Naranjo, A., Ojeda, S., Araña, V., Baeta, P., Fernández-Palacios, J., García-Duque, O., et al. (2009). Usefulness of clinical findings, nerve conduction studies and ultrasonography to predict response to surgical release in idiopathic carpal tunnel syndrome. Clinical and Experimental Rheumatology, 27, 786–793.

    CAS  PubMed  Google Scholar 

  14. Buchberger, W., Schön, G., Strasser, K., & Jungwirth, W. (1991). High-resolution ultrasonography of the carpal tunnel. Journal of Ultrasound in Medicine. https://doi.org/10.7863/jum.1991.10.10.531

    Article  PubMed  Google Scholar 

  15. Yesildag, A., Kutluhan, S., Sengul, N., Koyuncuoglu, H. R., Oyar, O., Guler, K., et al. (2004). The role of ultrasonographic measurements of the median nerve in the diagnosis of carpal tunnel syndrome. Clinical Radiology, 59, 910–915.

    Article  CAS  PubMed  Google Scholar 

  16. Ziswiler, H.-R., Reichenbach, S., Vögelin, E., Bachmann, L. M., Villiger, P. M., & Jüni, P. (2005). Diagnostic value of sonography in patients with suspected carpal tunnel syndrome: A prospective study. Arthritis and Rheumatism, 52, 304–311.

    Article  PubMed  Google Scholar 

  17. Werner, R. A., & Andary, M. (2002). Carpal tunnel syndrome: Pathophysiology and clinical neurophysiology. Clinical Neurophysiology, 113, 1373–1381.

    Article  PubMed  Google Scholar 

  18. Richman, J. A., Gelberman, R. H., Rydevik, B. L., Hajek, P. C., Braun, R. M., Gylys-Morin, V. M., et al. (1989). Carpal tunnel syndrome: Morphologic changes after release of the transverse carpal ligament. J Hand Surg Am., 14, 852–857.

    Article  CAS  PubMed  Google Scholar 

  19. Bleecker, M. L., Bohlman, M., Moreland, R., & Tipton, A. (1985). Carpal tunnel syndrome: Role of carpal canal size. Neurology, 35, 1599–1604.

    Article  CAS  PubMed  Google Scholar 

  20. Nanno, M., Kodera, N., Tomori, Y., Hagiwara, Y., & Takai, S. (2017). Median nerve movement in the carpal tunnel before and after carpal tunnel release using transverse ultrasound. Journal of Orthopaedic Surgery, 25, 2309499017730422.

    Article  PubMed  Google Scholar 

  21. Momose, T., Uchiyama, S., Kobayashi, S., Nakagawa, H., & Kato, H. (2014). Structural changes of the carpal tunnel, median nerve and flexor tendons in MRI before and after endoscopic carpal tunnel release. Hand Surgery, 19, 193–198.

    Article  PubMed  Google Scholar 

  22. Ng, A. W. H., Griffith, J. F., Tsoi, C., Fong, R. C. W., Mak, M. C. K., Tse, W. L., et al. (2021). Ultrasonography findings of the carpal tunnel after endoscopic carpal tunnel release for carpal tunnel syndrome. Korean Journal of Radiology, 22, 1132–1141.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Ardakani, A. A., Afshar, A., Bhatt, S., Bureau, N. J., Tahmasebi, A., Acharya, U. R., et al. (2020). Diagnosis of carpal tunnel syndrome: A comparative study of shear wave elastography, morphometry and artificial intelligence techniques. Pattern Recognit Lett., 133, 77–85.

    Article  Google Scholar 

  24. Park, D., Kim, B. H., Lee, S.-E., Kim, D. Y., Kim, M., Kwon, H. D., et al. (2021). Machine learning-based approach for disease severity classification of carpal tunnel syndrome. Science and Reports, 11, 1–10.

    Google Scholar 

  25. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-24574-4_28

    Article  Google Scholar 

  26. Wang, Y.-W., Chang, R.-F., Horng, Y.-S., & Chen, C.-J. (2020). MNT-DeepSL: Median nerve tracking from carpal tunnel ultrasound images with deep similarity learning and analysis on continuous wrist motions. Computerized Medical Imaging and Graphics, 80, 101687.

    Article  PubMed  Google Scholar 

  27. Horng, M.-H., Yang, C.-W., Sun, Y.-N., & Yang, T.-H. (2020). DeepNerve: A new convolutional neural network for the localization and segmentation of the median nerve in ultrasound image sequences. Ultrasound in Medicine and Biology, 46, 2439–2452.

    Article  PubMed  Google Scholar 

  28. Wu, C.-H., Syu, W.-T., Lin, M.-T., Yeh, C.-L., Boudier-Revéret, M., Hsiao, M.-Y., et al. (2021). Automated segmentation of median nerve in dynamic sonography using deep learning: evaluation of model performance. Diagnostics (Basel). https://doi.org/10.3390/diagnostics11101893

    Article  PubMed  PubMed Central  Google Scholar 

  29. Perazzi, F., Khoreva, A., Benenson, R., Schiele, B., & Sorkine-Hornung, A. (2017). Learning video object segmentation from static images. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.372

    Article  Google Scholar 

  30. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735–1780.

    Article  CAS  PubMed  Google Scholar 

  31. Festen, R. T., Schrier, V. J. M., & Amadio, P. C. (2021). Automated segmentation of the median nerve in the carpal tunnel using U-Net. Ultrasound in Medicine & Biology. https://doi.org/10.1016/j.ultrasmedbio.2021.03.018

    Article  Google Scholar 

  32. Mhoon, J. T., Juel, V. C., & Hobson-Webb, L. D. (2012). Median nerve ultrasound as a screening tool in carpal tunnel syndrome: Correlation of cross-sectional area measures with electrodiagnostic abnormality. Muscle and Nerve, 46, 871–878.

    Article  PubMed  Google Scholar 

  33. Hobson-Webb, L. D., Massey, J. M., Juel, V. C., & Sanders, D. B. (2008). The ultrasonographic wrist-to-forearm median nerve area ratio in carpal tunnel syndrome. Clinical Neurophysiology, 119, 1353–1357.

    Article  PubMed  Google Scholar 

  34. Kuroiwa, T., Jagtap, J., Starlinger, J., Lui, H., Akkus, Z., Erickson, B., et al. (2022). Deep learning estimation of median nerve volume using ultrasound imaging in a human cadaver model. Ultrasound in Medicine and Biology, 48, 2237–2248.

    Article  PubMed  Google Scholar 

  35. Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Medical Imaging, 15, 29.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Sugimoto, H., Miyaji, N., & Ohsawa, T. (1994). Carpal tunnel syndrome: Evaluation of median nerve circulation with dynamic contrast-enhanced MR imaging. Radiology, 190, 459–466.

    Article  CAS  PubMed  Google Scholar 

  37. Myers, R. R., Heekman, H. M., & Powell, H. C. (1985). Pathology of experimental nerve compression. Journal of Neuropathology and Experimental Neurology. https://doi.org/10.1097/00005072-198505000-00128

    Article  PubMed  Google Scholar 

  38. Padua, L., Pazzaglia, C., Caliandro, P., Granata, G., Foschini, M., Briani, C., et al. (2008). Carpal tunnel syndrome: Ultrasound, neurophysiology, clinical and patient-oriented assessment. Clinical Neurophysiology, 119, 2064–2069.

    Article  CAS  PubMed  Google Scholar 

  39. Dietterich, T. (1995). Overfitting and undercomputing in machine learning. ACM Computing Surveys. https://doi.org/10.1145/212094.212114

    Article  Google Scholar 

  40. Cosmo, M. D., Chiara Fiorentino, M., Villani, F. P., Sartini, G., Smerilli, G., Filippucci, E., et al. (2021). Learning-based median nerve segmentation from ultrasound images for carpal tunnel syndrome evaluation. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2021, 3025–3028.

    Google Scholar 

  41. Yoshii, Y., Zhao, C., & Amadio, P. C. (2020). Recent advances in ultrasound diagnosis of carpal tunnel syndrome. Diagnostics (Basel). https://doi.org/10.3390/diagnostics10080596

    Article  PubMed  Google Scholar 

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Acknowledgements

We would like to thank the Mayo Clinic Department of Radiology for computational resources and the Department of Orthopedic for data collection.

Funding

Funding for this work was provided by Mayo Clinic and a grant from NIH/NIAMS (AR62613). NIH/NIAMS had no involvement in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article.

Author information

Authors and Affiliations

  1. Department of Radiology, Mayo Clinic, 200 First St. SW, Rochester, MN, USA

    Jaidip M. Jagtap, Zeynettin Akkus & Bradley J. Erickson

  2. Department of Orthopedic Surgery, Mayo Clinic, 200 First St. SW, Rochester, MN, USA

    Tomoyuki Kuroiwa, Julia Starlinger, Mohammad Hosseini Farid, Hayman Lui & Peter Amadio

  3. College of Computing and Engineering, Nova Southeastern University, Fort Lauderdale, FL, USA

    Mohammad Hosseini Farid

  4. School of Medicine and Dentistry, Griffith University, Gold Coast, QLD, Australia

    Hayman Lui

  5. Department of Radiology, Mayo Clinic, Jacksonville, FL, USA

    Zeynettin Akkus

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  1. Jaidip M. Jagtap
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Contributions

Conceptualization: PA, BJE; Methodology: JMJ, JS, MHF, ZA and PA; Software: JMJ, ZA, BJE; Visualization: All; Formal Analysis: JMJ and TK; Investigation: JS, MHF and ZA; Data Curation: JMJ, TK, JS, MHF and HL; Writing – Original Draft Preparation: JMJ; Writing – Review & Editing, all.; Project Administration: PA and BJE.

Corresponding authors

Correspondence to Bradley J. Erickson or Peter Amadio.

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All research subjects were consented to an approved IRB protocol by a research study coordinator not otherwise involved in this project.

Ethical Approval

This study was approved by our institutional review board.

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Jagtap, J.M., Kuroiwa, T., Starlinger, J. et al. AI for Automated Segmentation and Characterization of Median Nerve Volume. J. Med. Biol. Eng. 43, 405–416 (2023). https://doi.org/10.1007/s40846-023-00805-z

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  • DOI: https://doi.org/10.1007/s40846-023-00805-z

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