A Novel Framework for Fetal Nuchal Translucency Abnormality Detection Using Hybrid Maxpool Matrix Histogram Analysis
Birth defects affect 1 to 3 percent of the population and are mostly detected in pregnant women through double, triple, and quadruple testing. Ultrasonography helps to discover and define such anomalies in fetuses. Ultrasound pictures of nuchal translucency (NT) are routinely used to detect genetic disorders in fetuses. The NT area lacks identifiable local behaviors and detection algorithms are required to classify the fetal head. On the other hand, explicit identification of other body parts comes at a higher cost in terms of annotations, implementation, and analysis. In circumstances of ambiguous head placement or non-standard head-NT relationships, it may potentially cause cascading errors. In this research work, a linear contour size filter is used to decrease noise from the image, and then the picture is scaled. Then, a novel hybrid maxpool matrix histogram analysis (HMMHA) is proposed to enhance the initiation and progression. The training and assessment were conducted using a dataset of 33 ultrasound pictures. Extensive testing shows that the direct method reliably identifies and measures NT. The suggested model may assist doctors in making decisions about pregnancies with fetal growth restriction, particularly for patients who have nuchal translucency or congenital anomalies and do not require induced labor due to these abnormalities. The performance of the proposed technique is analyzed in terms of error rate, sensitivity, Matthews correlation coefficient (MCC), accuracy, precision, recall, and F1-score. The error rate of the proposed model is 28.21% and it is found to be better when compared with the conventional approaches. Finally, the error prediction is compared with the existing models obtained from the medical dataset of pregnant women to identify fetal abnormality positions.
Keywordsnuchal translucency, genetic disorders, hybrid maxpool matrix histogram analysis, pregnant women, machine learning,
References1. P.S. Lakshmi, M. Geetha, N.R. Menon, V. Krishnan, P. Nedungadi, Automated screening for trisomy 21 by measuring nuchal translucency and frontomaxillary facial angle, 2018 International Conference on Advances in Computing, Communications, and Informatics (ICACCI), pp. 1738–1743, Bangalore, India, 2018, doi: 10.1109/ICACCI.2018.8554914.
2. T. Liu 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, PIPPI SUSI 2019, Lecture Notes in Computer Science, Vol. 11798, pp. 20–28, Springer, Cham, 2019, doi: 10.1007/978-3-030-32875-7_3.
3. G. Sciortino, D. Tegolo, C. Valenti, Automatic detection and measurement of nuchal translucency, Computers in Biology and Medicine, 82: 12–20, 2017, doi: 10.1016/j.compbiomed.2017.01.008.
4. S. Tiyatha, S. Sirilert, R. Sekararithi, T. Tongsong, Association between unexplained thickened nuchal translucency and adverse pregnancy outcomes, Archives of Gynecology and Obstetrics, 298(1): 97–101, 2018, doi: 10.1007/s00404-018-4790-9.
5. S. Xue et al., Genetic examination for fetuses with increased fetal nuchal translucency by genomic technology, Cytogenetic and Genome Research, 160(2): 57–62, 2020, doi: 10.1159/000506095.
6. V. Rawat, A. Jain, V. Shrimali, S. Raghuvanshi, Performance analysis of different learning algorithms of feed forward neural network regarding fetal abnormality detection, [in:] Thanh Nguyen N., Kowalczyk R. [Eds], Transactions on Computational Collective Intelligence XXX. Lecture Notes in Computer Science, Vol. 11120, pp. 118–132, Springer, Cham, 2018, doi: 10.1007/978-3-319-99810-7_6.
7. M.K. Hoffman, N. Ma, A. Roberts, A machine learning algorithm for predicting maternal readmission for hypertensive disorders of pregnancy, American Journal of Obstetrics & Gynecology MFM, 3(1): 100250, 2021, doi: 10.1016/j.ajogmf.2020.100250.
8. Y. Lu, X. Fu, F. Chen, K.K. Wong, Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning, Artificial Intelligence in Medicine, 102: 101748, 2020, doi: 10.1016/j.artmed.2019.101748.
9. M.S. Iraji, Prediction of fetal state from the cardiotocogram recordings using neural network models, Artificial Intelligence in Medicine, 96: 33–44, 2019, doi: 10.1016/j.artmed.2019.03.005.
10. C. Rydberg, K. Tunón, Detection of fetal abnormalities by second-trimester ultrasound screening in a non-selected population, Acta Obstetricia et Gynecologica Scandinavica, 96(2): 176–182, 2017, doi: 10.1111/aogs.13037.
11. K.W. Choy et al., Prenatal diagnosis of fetuses with increased nuchal translucency by genome sequencing analysis, Frontiers in Genetics, 10: 761, 2019, doi: 10.3389/fgene.2019.00761.
12. L. Hui et al., Reexamining the optimal nuchal translucency cutoff for diagnostic testing in the cell-free DNA and microarray era: Results from the Victorian Perinatal Record Linkage study, American Journal of Obstetrics and Gynecology, 225(5): 527.e1–527.e12, 2021, doi: 10.1016/j.ajog.2021.03.050.
13. A. Ficara, A. Syngelaki, A. Hammami, R. Akolekar, K.H. Nicolaides, Value of routine ultrasound examination at 35–37 weeks’ gestation in diagnosis of fetal abnormalities, Ultrasound in Obstetrics & Gynecology, 55(1): 75–80, 2020, doi: 10.1002/uog.20857.
14. C. Deden et al., Rapid whole exome sequencing in pregnancies to identify the underlying genetic cause in fetuses with congenital anomalies detected by ultrasound imaging, Prenatal Diagnosis, 40(8): 972–983, 2020, doi: 10.1002/pd.5717.
15. N. Corsten-Janssen et al., A prospective study on rapid exome sequencing as a diagnostic test for multiple congenital anomalies on fetal ultrasound, Prenatal Diagnosis, 40(10): 1300–1309, 2020, doi: 10.1002/pd.5781.
16. M. Komatsu et al., Detection of cardiac structural abnormalities in fetal ultrasound videos using deep learning, Applied Sciences, 11(1): 371, 2021, doi: 10.3390/app11010371.
17. A. Fukuda, C. Han, K. Hakamada, Effort-free automated skeletal abnormality detection of rat fetuses on whole-body micro-CT scans, [in:] 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, pp. 279–283, 2021, doi: 10.1109/ICIP42928.2021.9506216.
18. K. He, X. Zhang, S. Ren, J. Sun, Identity mappings in deep residual networks, [in:] B. Leibe, J. Matas, N. Sebe, M. Welling [Eds], Computer Vision – ECCV 2016, Lecture Notes in Computer Science, Vol. 9908, pp. 630–645, Springer, Cham, 2016, doi: 10.1007/978-3-319-46493-0_38.
19. G. Huang, Z. Liu, L. van der Maaten, K.Q. Weinberger, Densely connected convolutional networks, [in:] 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700—4708, Honolulu, HI, USA, 2017, doi: 10.1109/CVPR.2017.243.
This work is licensed under a Creative Commons Attribution 4.0 International License.