Abstract
Down syndrome is a genetic disorder which occurs due to the presence of an extra chromosome. This trisomy known as trisomy 21 leads to varying degrees of disability from physical disabilities like growth delays to mental disabilities. The paper focuses on exploring the different methodologies for the enhancement of ultrasound images and to extract the feature set from them; using machine learning algorithms to determine the probabilistic measure of the foetus being born with down syndrome. The proposed system aims to develop an end to end application, built upon the previous works in this field, utilising a dataset consisting of ultrasound scans of the first trimester, and hence would provide insights on the prominence of DS in India, and produces the probabilistic measure of the foetus being born with DS, to aid the doctor in prescribing further invasive test.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Nicolaides, K., Azar, G., Byrne, D., Mansur, C. Marks, K.: Fetal nuchal translucency: ultrasound screening for chromosomal defects in first trimester of pregnancy. BMJ Publishing Group 304(6831), 867–869 (1992). https://doi.org/10.1136/bmj.304.6831.867
Otaño, L., Aiello, H., Igarzábal, L., Matayoshi, T. Gadow, E.: Association between first trimester absence of fetal nasal bone on ultrasound and down syndrome. Prenat. Diagn. 22(10), 930–932 (2002). https://doi.org/10.1002/pd.431
Cho, J., Kim, K., Lee, Y., Toi, A.: Measurement of nuchal skin fold thickness in the second trimester: influence of imaging angle and fetal presentation. Ultrasound Obstet. Gyneco. 25(3), 253–257 (2005). https://doi.org/10.1002/uog.1847
Sahli, H., Mouelhi, A., Hadada, F., Rachdi, R., Sayadi, M., Fnaiech, F.: Statistical analysis based on biometrie measures for fetal head anomaly characterization. In: 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME) (2018). https://doi.org/10.1109/mecbme.2018.8402440
Khashman, A., Curtis, K. (n.d.).: Automatic edge detection of foetal head and abdominal circumferences using neural network arbitration. In: ISIE ‘97 Proceeding of the IEEE International Symposium on Industrial Electronics (1997). https://doi.org/10.1109/isie.1997.648910
Wee, L.K., Min, T.Y., Arooj, A., Supriyanto, E.: Nuchal translucency marker detection based on artificial neural network and measurement via bidirectional iteration forward propagation. WSEAS Trans. Info. Sci. App (2010)
Anjit, T., Rishidas, S.: Identification of nasal bone for the early detection of down syndrome using back propagation neural network. In: 2011 International Conference on Communications and Signal Processing (2011). https://doi.org/10.1109/iccsp.2011.5739286
Wojtowicz, H., Wajs, W.: Medical decision support system for assessment of dermatoglyphic indices and diagnosis of down’s syndrome. Intell. Decis. Technol., 69–78 (2012). https://doi.org/10.1007/978-3-642-29920-9_8
Soleimani, F., Teymouri, R., Biglarian, A.: Predicting developmental disorder in infants using an artificial neural network (2013)
Sahli, H., Ben Slama, A., Zaafouri, A., Sayadi, M., Rachdi, R.: Automated detection of current fetal head in ultrasound sequences. In: 2016 International Image Processing, Applications and Systems (IPAS) (2016). https://doi.org/10.1109/ipas.2016.7880142
Catic, A., Gurbeta, L., Kurtovic-Kozaric, A., Mehmedbasic, S., Badnjevic, A.: Application of neural networks for classification of patau, edwards, down, turner and klinefelter syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. BMC Med. Genomics 11(1) (2018). https://doi.org/10.1186/s12920-018-0333-2
Ramanathan, S., Sangeetha, M., Talwai, S., Natarajan, S.: Probabilistic determination of down’s syndrome using machine learning techniques. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2018). https://doi.org/10.1109/icacci.2018.8554392
Sciortino G, e. (2019). Automatic detection and measurement of nuchal translucency. PubMed—NCBI. [online] Ncbi.nlm.nih.gov. https://www.ncbi.nlm.nih.gov/pubmed/28126630. Retrieved 30 Nov. 2019. https://doi.org/10.1016/j.compbiomed.2017.01.008
Sinclair, M., Baumgartner, C., Matthew, J., Bai, W., Martinez, J., Li, Y., Smith, S., Knight, C., Kainz, B., Hajnal, J., King, A., Rueckert, D.: Human-level performance on automatic head biometrics in fetal ultrasound using fully convolutional neural networks. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2018). https://doi.org/10.1109/embc.2018.8512278
Sobhaninia, Z., Rafiei, S., Emami, A., Karimi, N., Najarian, K., Samavi, S., Soroushmehr, S.: Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning (2019). https://doi.org/10.1109/embc.2019.8856981
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Megha, I.M., Vyshnavi Kowshik, S., Ali, S., Malagi, V.P. (2021). A Comprehensive Survey on Down Syndrome Detection in Foetus Using Modern Technologies. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_73
Download citation
DOI: https://doi.org/10.1007/978-981-15-5679-1_73
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5678-4
Online ISBN: 978-981-15-5679-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)