Automatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
04 Mar 2024
04 Mar 2024
Historique:
received:
18
03
2023
accepted:
29
02
2024
medline:
5
3
2024
pubmed:
5
3
2024
entrez:
4
3
2024
Statut:
epublish
Résumé
This study aims at suggesting an end-to-end algorithm based on a U-net-optimized generative adversarial network to predict anterior neck lower jaw angles (ANLJA), which are employed to define fetal head posture (FHP) during nuchal translucency (NT) measurement. We prospectively collected 720 FHP images (half hyperextension and half normal posture) and regarded manual measurement as the gold standard. Seventy percent of the FHP images (half hyperextension and half normal posture) were used to fit models, and the rest to evaluate them in the hyperextension group, normal posture group (NPG), and total group. The root mean square error, explained variation, and mean absolute percentage error (MAPE) were utilized for the validity assessment; the two-sample t test, Mann-Whitney U test, Wilcoxon signed-rank test, Bland-Altman plot, and intraclass correlation coefficient (ICC) for the reliability evaluation. Our suggested algorithm outperformed all the competitors in all groups and indices regarding validity, except for the MAPE, where the Inception-v3 surpassed ours in the NPG. The two-sample t test and Mann-Whitney U test indicated no significant difference between the suggested method and the gold standard in group-level comparison. The Wilcoxon signed-rank test revealed significant differences between our new approach and the gold standard in personal-level comparison. All points in Bland-Altman plots fell between the upper and lower limits of agreement. The inter-ICCs of ultrasonographers, our proposed algorithm, and its opponents were graded good reliability, good or moderate reliability, and moderate or poor reliability, respectively. Our proposed approach surpasses the competition and is as reliable as manual measurement.
Identifiants
pubmed: 38438512
doi: 10.1038/s41598-024-55974-x
pii: 10.1038/s41598-024-55974-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5351Subventions
Organisme : Health Research Project of Hunan Provincial Health Commission
ID : B202309026062
Organisme : Health Research Project of Hunan Provincial Health Commission
ID : 20200951
Organisme : Health Research Project of Hunan Provincial Health Commission
ID : B2019030
Organisme : Ruixin project of Hunan Provincial Maternal and Child Health Care Hospital
ID : 2023RX21
Organisme : Major Scientific and Technological Projects for collaborative prevention and control of birth defects in Hunan Province, China
ID : 2019SK1010
Organisme : Natural Science Foundation of Hunan Province, China
ID : 2021JJ70008
Organisme : Natural Science Foundation of Changsha, China
ID : kq2208341
Organisme : Hunan Talent Program for Eminent Medical Specialists
ID : 20220323-1004
Informations de copyright
© 2024. The Author(s).
Références
Nicolaides, K. H., Azar, G., Byrne, D., Mansur, C. & Marks, K. Fetal nuchal translucency: Ultrasound screening for chromosomal defects in first trimester of pregnancy. BMJ 304, 867–869. https://doi.org/10.1136/bmj.304.6831.867 (1992).
doi: 10.1136/bmj.304.6831.867
Whitlow, B. J., Chatzipapas, I. K. & Economides, D. L. The effect of fetal neck position on nuchal translucency measurement. Br. J. Obstet. Gynaecol. 105, 872–876. https://doi.org/10.1111/j.1471-0528.1998.tb10232.x (1998).
doi: 10.1111/j.1471-0528.1998.tb10232.x
Spencer, K., Souter, V., Tul, N., Snijders, R. & Nicolaides, K. H. A screening program for trisomy 21 at 10–14 weeks using fetal nuchal translucency, maternal serum free beta-human chorionic gonadotropin and pregnancy-associated plasma protein-A. Ultrasound Obstet. Gynecol. 13, 231–237. https://doi.org/10.1046/j.1469-0705.1999.13040231.x (1999).
doi: 10.1046/j.1469-0705.1999.13040231.x
Malone, F. D. & D’Alton, M. E. First-trimester sonographic screening for down syndrome. Obstet. Gynecol. 102, 1066–1079. https://doi.org/10.1016/j.obstetgynecol.2003.08.004 (2003).
doi: 10.1016/j.obstetgynecol.2003.08.004
Lee, Y.-B. & Kim, M.-H. Automated ultrasonic measurement of fetal nuchal translucency using dynamic programming. In Progress in Pattern Recognition, Image Analysis and Applications (eds Martínez-Trinidad, J. F. et al.) 157–167 (Springer, 2006).
doi: 10.1007/11892755_16
Deng, Y.-H., Wang, Y.-Y. & Chen, P. Estimating fetal nuchal translucency parameters from its ultrasound image. In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering 2643–2646 (2008). https://doi.org/10.1109/ICBBE.2008.994 .
Catanzariti, E. et al. A semi-automated method for the measurement of the fetal nuchal translucency in ultrasound images. In Image Analysis and Processing: ICIAP 2009 (eds. Foggia, P., Sansone, C. & Vento, M.) 613–622 (Springer, 2009). https://doi.org/10.1007/978-3-642-04146-4_66 .
Moratalla, J. et al. Semi-automated system for measurement of nuchal translucency thickness. Ultrasound Obstet. Gynecol. 36, 412–416. https://doi.org/10.1002/uog.7737 (2010).
doi: 10.1002/uog.7737
Deng, Y., Wang, Y. & Chen, P. Automated detection of fetal nuchal translucency based on hierarchical structural model. In 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS) 78–84 (2010). https://doi.org/10.1109/CBMS.2010.6042618 .
Deng, Y., Wang, Y., Chen, P. & Yu, J. A hierarchical model for automatic nuchal translucency detection from ultrasound images. Comput. Biol. Med. 42, 706–713. https://doi.org/10.1016/j.compbiomed.2012.04.002 (2012).
doi: 10.1016/j.compbiomed.2012.04.002
Park, J., Sofka, M., Lee, S., Kim, D. & Zhou, S. K. Automatic nuchal translucency measurement from ultrasonography. In Medical Image Computing and Computer-Assisted Intervention: MICCAI 2013 (eds. Mori, K., Sakuma, I., Sato, Y., Barillot, C. & Navab, N.) 243–250 (Springer, 2013). https://doi.org/10.1007/978-3-642-40760-4_31 .
Anzalone, A. et al. A system for the automatic measurement of the nuchal translucency thickness from ultrasound video stream of the foetus. In Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems 239–244 (2013). https://doi.org/10.1109/CBMS.2013.6627795 .
Sciortino, G., Tegolo, D. & Valenti, C. Automatic detection and measurement of nuchal translucency. Comput. Biol. Med. 82, 12–20. https://doi.org/10.1016/j.compbiomed.2017.01.008 (2017).
doi: 10.1016/j.compbiomed.2017.01.008
Sciortino, G., Tegolo, D. & Valenti, C. A non-supervised approach to locate and to measure the nuchal translucency by means of wavelet analysis and neural networks. In 2017 XXVI International Conference on Information, Communication and Automation Technologies (ICAT) 1–7 (2017). https://doi.org/10.1109/ICAT.2017.8171631 .
Nie, S. et al. Automatic measurement of fetal Nuchal translucency from three-dimensional ultrasound data. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 3417–3420 (2017). https://doi.org/10.1109/EMBC.2017.8037590 .
Liu, T. et al. Direct detection and measurement of nuchal translucency with neural networks from ultrasound images. In Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis (eds. Wang, Q. et al.) 20–28 (Springer, 2019). https://doi.org/10.1007/978-3-030-32875-7_3 .
Sciortino, G., Tegolo, D. & Valenti, C. Morphological analysis combined with a machine learning approach to detect utrasound median sagittal sections for the nuchal translucency measurement. Pattern Recognit. https://doi.org/10.1007/978-3-319-59226-8_25 (2017).
doi: 10.1007/978-3-319-59226-8_25
Sciortino, G., Orlandi, E., Valenti, C. & Tegolo, D. Wavelet analysis and neural network classifiers to detect mid-sagittal sections for nuchal translucency measurement. Image Anal. Stereol. 35, 105–115. https://doi.org/10.5566/ias.135 (2016).
doi: 10.5566/ias.135
Nie, S., Yu, J., Chen, P., Wang, Y. & Zhang, J. Q. Automatic detection of standard sagittal plane in the first trimester of pregnancy using 3-D ultrasound data. Ultrasound Med. Biol. 43, 286–300. https://doi.org/10.1016/j.ultrasmedbio.2016.08.034 (2017).
doi: 10.1016/j.ultrasmedbio.2016.08.034
Zhang, L. et al. Development and validation of a deep learning model to screen for trisomy 21 during the first trimester from nuchal ultrasonographic images. JAMA Netw Open 5, e2217854. https://doi.org/10.1001/jamanetworkopen.2022.17854 (2022).
doi: 10.1001/jamanetworkopen.2022.17854
Deniz, A. & Yilmaz, Y. B. Computer-aided monitoring of fetus health from ultrasound images: A review. Acta Infologica 6, 283–302. https://doi.org/10.26650/acin.1099106 (2022).
doi: 10.26650/acin.1099106
Kore, S. et al. Effects of period of gestation and position of fetal neck on nuchal translucency measurement. J. Obstet. Gynaecol. India 63, 244–248. https://doi.org/10.1007/s13224-012-0341-7 (2013).
doi: 10.1007/s13224-012-0341-7
Chen, P. W., Chen, M., Leung, T. Y. & Lau, T. K. Effect of image settings on nuchal translucency thickness measurement by a semi-automated system. Ultrasound Obstet. Gynecol. 39, 169–174. https://doi.org/10.1002/uog.9088 (2012).
doi: 10.1002/uog.9088
AIUM-ACR-ACOG-SMFM-SRU practice parameter for the performance of standard diagnostic obstetric ultrasound examinations. J. Ultrasound Med. 37, E13–E24. https://doi.org/10.1002/jum.14831 (2018).
Nuchal Translucency Quality Review Program. NT MEASUREMENT CRITERIA. vol. 2020. https://ntqr.perinatalquality.org/MyFTP/Documents/NTCriteria.pdf (2023).
Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3431–3440 (2015). https://doi.org/10.1109/CVPR.2015.7298965 .
Tiškus, E., Bučas, M., Gintauskas, J., Kataržytė, M. & Vaičiūtė, D. U-net performance for beach wrack segmentation: Effects of UAV camera bands, height measurements, and spectral indices. Drones 7, 670. https://doi.org/10.3390/drones7110670 (2023).
doi: 10.3390/drones7110670
Clevert, D.-A., Unterthiner, T. & Hochreiter, S. Fast and accurate deep network learning by Exponential Linear Units (ELUs). https://doi.org/10.48550/arXiv.1511.07289 (2016).
Isensee, F. et al. Abstract: nnU-Net: Self-adapting framework for U-Net-based medical image segmentation. In Bildverarbeitung für die Medizin 2019 (eds. Handels, H. et al.) 22–22 (Springer, 2019). https://doi.org/10.1007/978-3-658-25326-4_7 .
Goodfellow, I. et al. Generative adversarial nets. In Advances in Neural Information Processing Systems 27 (NIPS 2014) 2672–2680 (Curran Associates, Inc., 2014).
Mirza, M. & Osindero, S. Conditional generative adversarial nets. https://doi.org/10.48550/arXiv.1411.1784 (2014).
Dumoulin, V. et al. Adversarially learned inference. https://doi.org/10.48550/arXiv.1606.00704 (2017).
Donahue, J., Krähenbühl, P. & Darrell, T. Adversarial feature learning. https://doi.org/10.48550/arXiv.1605.09782 (2017).
Fiorentino, M. C., Villani, F. P., Di Cosmo, M., Frontoni, E. & Moccia, S. A review on deep-learning algorithms for fetal ultrasound-image analysis. Med. Image Anal. 83, 102629. https://doi.org/10.1016/j.media.2022.102629 (2022).
doi: 10.1016/j.media.2022.102629
Rosner, B. Fundamentals of Biostatistics 8th edn, 232–365 (Cengage Learning, 2015).
Fletcher, G. S. Clinical Epidemiology: The Essentials 6th edn, 34–35 (Lippincott Williams & Wilkins, 2019).
Peng, Y. et al. Cross-sectional reference values of cerebral ventricle for Chinese neonates born at 25–41 weeks of gestation. Eur. J. Pediatr. 181, 3645–3654. https://doi.org/10.1007/s00431-022-04547-z (2022).
doi: 10.1007/s00431-022-04547-z
Peng, Y., Zeng, S. & Luo, Y. Diagnosis and treatment for incarceration of retroverted uterus during pregnancy: A report of four cases. Chin. J. Perinat. Med. 24, 141–146. https://doi.org/10.3760/cma.j.cn113903-20200524-00487 (2021).
doi: 10.3760/cma.j.cn113903-20200524-00487
Bossuyt, P. M. et al. STARD 2015: An updated list of essential items for reporting diagnostic accuracy studies. BMJ 351, h5527. https://doi.org/10.1136/bmj.h5527 (2015).
doi: 10.1136/bmj.h5527
Kottner, J. et al. Guidelines for Reporting Reliability and Agreement Studies (GRRAS) were proposed. Int. J. Nurs. Stud. 48, 661–671. https://doi.org/10.1016/j.ijnurstu.2011.01.016 (2011).
doi: 10.1016/j.ijnurstu.2011.01.016
Stevens, L. M., Mortazavi, B. J., Deo, R. C., Curtis, L. & Kao, D. P. Recommendations for reporting machine learning analyses in clinical research. Circ. Cardiovasc. Qual. Outcomes 13, e006556. https://doi.org/10.1161/circoutcomes.120.006556 (2020).
doi: 10.1161/circoutcomes.120.006556
Guyon, I. et al. Improved training of Wasserstein GANs. In Advances in Neural Information Processing Systems 30 5768–5778 (Curran Associates, Inc., 2017).
Fu, J. et al. Low-light image enhancement base on brightness attention mechanism generative adversarial networks. Multimed. Tools Appl. 83, 10341–10365. https://doi.org/10.1007/s11042-023-15815-x (2024).
doi: 10.1007/s11042-023-15815-x
Maqueda, A. I., Loquercio, A., Gallego, G., García, N. & Scaramuzza, D. Event-based vision meets deep learning on steering prediction for self-driving cars. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 5419–5427 (2018). https://doi.org/10.1109/CVPR.2018.00568 .
Bartlett, J. W. & Frost, C. Reliability, repeatability and reproducibility: Analysis of measurement errors in continuous variables. Ultrasound Obstet. Gynecol. 31, 466–475. https://doi.org/10.1002/uog.5256 (2008).
doi: 10.1002/uog.5256
Beilei, H. et al. Reference values for cerebral ventricular size in neonates with gestational age of 33 +0-41 +6 weeks. Chin. J. Perinat. Med. 26, 650–657. https://doi.org/10.3760/cma.j.cn113903-20230302-00108 (2023).
doi: 10.3760/cma.j.cn113903-20230302-00108
Woodman, R. J. Bland–Altman beyond the basics: Creating confidence with badly behaved data. Clin. Exp. Pharmacol. Physiol. 37, 141–142. https://doi.org/10.1111/j.1440-1681.2009.05320.x (2010).
doi: 10.1111/j.1440-1681.2009.05320.x
Koo, T. K. & Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15, 155–163. https://doi.org/10.1016/j.jcm.2016.02.012 (2016).
doi: 10.1016/j.jcm.2016.02.012
Benchoufi, M., Matzner-Lober, E., Molinari, N., Jannot, A. S. & Soyer, P. Interobserver agreement issues in radiology. Diagn. Interv. Imaging 101, 639–641. https://doi.org/10.1016/j.diii.2020.09.001 (2020).
doi: 10.1016/j.diii.2020.09.001