Skip to content
2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Background

Real-time shear wave elastography (SWE) has emerged as a useful imaging modality for detecting the lesion location of various diseases, including cervical diseases.

Objectives

In this paper, the SWE was used to quantitatively determine the tissue hardness of the internal and external orifice of the cervix (IOC & EOC) and to relatively objectively analyze the impact of different production methods on the hardness of the cervical tissue.

Methods

A total of 48 patients were selected, and they were divided into three groups according to different production methods: control group (16 cases), cesarean section group (16 cases), and spontaneous delivery group (16 cases). Artificial intelligence has also been incorporated into this work. A deep flexible neural tree model and a new set of FNT models were proposed to assist in classifying cervical physical data in different states. The physical data was extracted as the features, and the different states were considered as category labels.

Results

There was no statistically significant difference in the elasticity of the IOC and the EOC between the groups. However, the difference in the elasticity of the IOC and the EOC within each group was statistically significant. The classification results corresponded with the results of the statistical analysis. The hardness of the EOC is generally lower than that of the IOC, and there was no significant difference in hardness between the IOC and the EOC in the three groups.

Conclusion

There is no significant difference in the cervical elasticity hardness between different delivery modes.

© 2024 Song et al.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056263380231107093752
2024-01-01
2025-10-18

  • From This Site

    /content/journals/cmir/10.2174/0115734056263380231107093752
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cmir/20/1/CMIR-20-E15734056263380.html?itemId=/content/journals/cmir/10.2174/0115734056263380231107093752&mimeType=html&fmt=ahah

References

  1. ChenJ.M. Jia-HongJ.I. Guo-DongL.I. Principle of acoustic imaging systems and latest technologies in clinic.Inform. Med. Equip.200621122830
     [Google Scholar]
  2. OphirJ. CéspedesI. PonnekantiH. YazdiY. LiX. Elastography: A quantitative method for imaging the elasticity of biological tissues.Ultrason. Imaging199113211113410.1177/0161734691013002011858217
     [Google Scholar]
  3. GuJ. PolleyE.C. DenisM. CarterJ.M. PruthiS. GregoryA.V. BougheyJ.C. FazzioR.T. FatemiM. AlizadA. Early assessment of shear wave elastography parameters foresees the response to neoadjuvant chemotherapy in patients with invasive breast cancer.Breast Cancer Res.20212315210.1186/s13058‑021‑01429‑433926522
     [Google Scholar]
  4. LiH. KangC. XueJ. JingL. MiaoJ. Influence of lesion size on shear wave elastography in the diagnosis of benign and malignant thyroid nodules.Sci. Rep.20211112161610.1038/s41598‑021‑01114‑834732826
     [Google Scholar]
  5. PolatA.V. OzturkM. PolatA.K. KarabacakU. BekciT. MuratN. Efficacy of ultrasound and shear wave elastography for the diagnosis of breast cancer–related lymphedema.J. Ultrasound Med.202039479580310.1002/jum.1516231705687
     [Google Scholar]
  6. SvenssonW.E. AmirasD. Ultrasound elasticity imaging.Breast Cancer Online200696e2410.1017/S1470903106002835
     [Google Scholar]
  7. LocatelliM ChersevaniR RizzattoG. Real-time ultrasound elastography: Diagnostic tool or electronic gadget?200515159
     [Google Scholar]
  8. XuH.X. YanK. LiuB.J. LiuW.Y. TangL.N. ZhouQ. WuJ.Y. XueE.S. ShenB. TangQ. ChenQ. XueH.Y. LiY.J. GuoJ. WangB. LiF. YanC.Y. LiQ.S. WangY.Q. ZhangW. WuC.J. YuW.H. ZhouS.J. Guidelines and recommendations on the clinical use of shear wave elastography for evaluating thyroid nodule1.Clin. Hemorheol. Microcirc.2019721396010.3233/CH‑18045230320562
     [Google Scholar]
  9. QiuT. WangH. SongJ. GuoG. ShiY. LuoY. LiuJ. Could ultrasound elastography reflect liver function?Ultrasound Med. Biol.201844477978510.1016/j.ultrasmedbio.2017.12.01529402486
     [Google Scholar]
  10. DongY. ZhouC. ZhouJ. YangZ. ZhangJ. ZhanW. Breast strain elastography: Observer variability in data acquisition and interpretation.Eur. J. Radiol.201810115716110.1016/j.ejrad.2018.02.02529571790
     [Google Scholar]
  11. XieM. ZhangX. YuM. WangW. HuaK. Evaluation of the cervix after cervical conization by transvaginal elastography.J. Ultrasound Med.20183751109111410.1002/jum.1445729044684
     [Google Scholar]
  12. LiaoJ. YangH. YuJ. LiangX. ChenZ. Progress in the application of ultrasound elastography for brain diseases.J. Ultrasound Med.202039112093210410.1002/jum.1531732385862
     [Google Scholar]
  13. McQueenA.S. BhatiaK.S. Head and neck ultrasound: Technical advances, novel applications and the role of elastography.Clin. Radiol.2018731819310.1016/j.crad.2017.08.00328985885
     [Google Scholar]
  14. ZhangQ. XiaoY. DaiW. SuoJ. WangC. ShiJ. ZhengH. Deep learning based classification of breast tumors with shear-wave elastography.Ultrasonics20167215015710.1016/j.ultras.2016.08.00427529139
     [Google Scholar]
  15. AhmedS. KamalU. HasanM.K. DSWE-Net: A deep learning approach for shear wave elastography and lesion segmentation using single push acoustic radiation force.Ultrasonics202111010628310.1016/j.ultras.2020.10628333166787
     [Google Scholar]
  16. DestrempesF. TropI. AllardL. ChayerB. Garcia-DuitamaJ. El KhouryM. LalondeL. CloutierG. Added value of quantitative ultrasound and machine learning in bi-rads 4–5 assessment of solid breast lesions.Ultrasound Med. Biol.202046243644410.1016/j.ultrasmedbio.2019.10.02431785840
     [Google Scholar]
  17. YuY. XiaoY. ChengJ. ChiuB. Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images.Comput. Biol. Med.201893314610.1016/j.compbiomed.2017.12.00629275098
     [Google Scholar]
  18. DurotI. AkhbardehA. SagreiyaH. LoeningA.M. RubinD.L. A new multimodel machine learning framework to improve hepatic fibrosis grading using ultrasound elastography systems from different vendors.Ultrasound Med. Biol.2020461263310.1016/j.ultrasmedbio.2019.09.00431611074
     [Google Scholar]
  19. BaoW. HuangD.S. ChenY.H. MSIT: Malonylation sites identification tree.Curr. Bioinform.2020151596710.2174/1574893614666190730110747
     [Google Scholar]
  20. BaoW YangB HuangD-S IMKPse: Identification of protein malonylation sites by the key features into general PseAAC.IEEE Access20197540738310.1109/ACCESS.2019.2900275
     [Google Scholar]
  21. HeQ LuoJW Research progress of ultrafast ultrasound imaging.201430812511255
     [Google Scholar]
  22. GandhiJ. ZaidiS. ShahJ. JoshiG. KhanS.A. The evolving role of shear wave elastography in the diagnosis and treatment of prostate cancer.Ultrasound Q.201834424524910.1097/RUQ.000000000000038530531430
     [Google Scholar]
  23. WangC.Z. ZhengJ. HuangZ.P. XiaoY. SongD. ZengJ. ZhengH.R. ZhengR.Q. Influence of measurement depth on the stiffness assessment of healthy liver with real-time shear wave elastography.Ultrasound Med. Biol.201440346146910.1016/j.ultrasmedbio.2013.10.02124361224
     [Google Scholar]
  24. ClarkK. JiH. FeltovichH. JanowskiJ. CarrollC. ChienE.K. Mifepristone-induced cervical ripening: Structural, biomechanical, and molecular events.Am. J. Obstet. Gynecol.200619451391139810.1016/j.ajog.2005.11.02616647925
     [Google Scholar]
  25. LeppertP.C. KokenyesiR. KlemenichC.A. FisherJ. Further evidence of a decorin-collagen interaction in the disruption of cervical collagen fibers during rat gestation.Am. J. Obstet. Gynecol.2000182480581210.1016/S0002‑9378(00)70329‑210764456
     [Google Scholar]
  26. Hernandez-AndradeE. MaymonE. LuewanS. BhattiG. MehrmohammadiM. ErezO. PacoraP. DoneB. HassanS.S. RomeroR. A soft cervix, categorized by shear-wave elastography, in women with short or with normal cervical length at 18–24 weeks is associated with a higher prevalence of spontaneous preterm delivery.J. Perinat. Med.201846548950110.1515/jpm‑2018‑006229813033
     [Google Scholar]
  27. ShahN. KaulA. Strain cervical elastography in pregnancy: Feasibility study and its usefulness in prediction of preterm birth.J. Foetal Med.20185315515810.1007/s40556‑018‑0178‑y
     [Google Scholar]
  28. HanR.Z. WangD. ChenY.H. DongL.K. FanY.L. Prediction of phosphorylation sites based on the integration of multiple classifiers.Genet. Mol. Res.201716110.4238/gmr1601935428252167
     [Google Scholar]
  29. XuJ WuP ChenY MengQ DawoodH KhanMM A novel deep flexible neural forest model for classification of cancer subtypes based on gene expression data.IEEE Access20197220869510.1109/ACCESS.2019.2898723
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056263380231107093752
Loading
Quantitative Analysis of the Impact of Different Delivery Modes on Cervical Elasticity Based on Real-time Shear Wave Imaging Technology and Artificial Intelligence
Curr. Med. Imaging 20, e15734056263380 (2024); https://doi.org/10.2174/0115734056263380231107093752
/content/journals/cmir/10.2174/0115734056263380231107093752
/content/journals/cmir/10.2174/0115734056263380231107093752
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Cervix; Cesarean section; Delivery modes; Quantitative analysis; Shear wave; Spontaneous delivery

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test