Heart Defect Detection in Fetus

Heart Defect Detection in Fetus

Author Details

1. T. Avinash, Dept. of CSE, VR Siddhartha Engineering College Vijayawada, India
2. B. Jayanag, Dept. of CSE, VR Siddhartha Engineering College Vijayawada, India
3. S. Suhana Sulthana, Dept. of CSE, VR Siddhartha Engineering College Vijayawada, India
4. N. Sravani, Dept. of CSE, VR Siddhartha Engineering College Vijayawada, India

To determine the complications, anomalies of fetus ultrasonography is performed during pregnancy of a woman. Therefore, finding a sign for a more definite anatomic study of the embryo. Ultrasound can identify the majority of major structural fetal abnormalities. Minor anomalies in 15% new-borns. Greater number of new strings cause greater chance of having major birth defects. The aim of the project is to build and train the model to effectively detect the abnormalities using the ultrasound images of the fetus at the early stages. As many of the structural anomalies can be treated if detected in early stages, the manual diagnosis requires considerable effort, time consuming and is prone to misdiagnosis. Therefore, using a software can avoid misdiagnosis and reduce overall time and effort.

Keywords

Ultrasonography, Structural Abnormalities, Fetal Anomalies, Trimesters, Convolutional Neural Network, Image Processing.
  1. H. Chen et al., "Ultrasound Standard Plane Detection Using a Composite Neural Network Framework," in IEEETransactions on Cybernetics, vol. 47, no. 6, pp. 1576-1586, June 2017, doi: 10.1109/TCYB.2017.2685080.
  2. M. Feng, L. Wan, Z. Li, L. Qing and X. Qi, "Fetal Weight Estimation via Ultrasound Using Machine Learning," in IEEE Access, vol. 7, pp. 87783-87791, 2019, doi: 10.1109/ACCESS.2019.2925803.
  3. Gong Y, Zhang Y, Zhu H, Lv J, Cheng Q, Zhang H, He Y, Wang S. Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning. IEEE Trans Med Imaging. 2020 Apr;39(4):1206-1222. doi: 10.1109/TMI.2019.2946059. Epub 2019 Oct 7. PMID: 31603775
  4. S. Nurmainiet al., "Accurate Detection of Septal Defects With Fetal Ultrasonography Images Using Deep Learning-Based Multiclass Instance Segmentation," inIEEE Access, vol. 8, pp. 196160-196174, 2020, doi: 10.1109/ACCESS.2020.3034367.
  5. Komatsu M, Sakai A, Komatsu R, Matsuoka R,Yasutomi S, Shozu K, Dozen A, Machino H, Hidaka H, Arakaki T, Asada K, Kaneko S, Sekizawa A, Hamamoto R. Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning.Applied Sciences. 2021; 11(1):371.
  6. A. M. Oprescu, G. Miró-amarante, L. García-Díaz, L. M. Beltrán, V. E. Rey and M. Romero-Ternero, "Artificial Intelligence in Pregnancy: A Scoping Review," in IEEE Access, vol. 8, pp. 181450-181484, 2020.
  7. R. Raut, A. Dikshit-Ratnaparkhi and D. Bormane, "Development of Algorithm for Extraction of Fetal from Maternal ECG on Benchmark Database and Prototype Development for Acquisition," 2020, doi: 10.1109/ICCMC48092.2020.ICCMC-00059.
  8. C. Lin et al., "Robust Fetal Heart Beat Detection via R-Peak Intervals Distribution," in IEEE Transactions on Biomedical Engineering, vol. 66, no. 12, pp. 3310-3319, Dec. 2019, doi: 10.1109/TBME.2019.2904014.
  9. M. W. Rivolta, T. Stampalija, M. G. Frasch and R. Sassi, "Theoretical Value of Deceleration Capacity Points to Deceleration Reserve of Fetal Heart Rate," in IEEE Transactions on Biomedical Engineering, vol. 67, no. 4, pp. 1176-1185, April 2020, doi: 10.1109/TBME.2019.293280
  10. R. Vullings, "Fetal Electrocardiography and Deep Learning for Prenatal Detection of Congenital Heart D i s e a s e , " 2 0 1 9 C o m p u t i n g i n C a r d i o l o g y ( C i n C ) , 2 0 1 9 , p p . P a g e 1 - P a g e 4 , d o i : 10.23919/CinC49843.2019.9005870.
  11. L. Ji, Y. Gu, K. Sun, J. Yang and Y. Qiao, "Congenital heart disease (CHD) discrimination in fetal echocardiogram based on 3D feature fusion," 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 3419-3423, doi: 10.1109/ICIP.2016.7532994.
  12. C. Stoean, R. Stoean, M. Hotoleanu, D. Iliescu, C. Patru and R. Nagy, "An assessment of the usefulness of image pre-processing for the classification of first trimester fetal heart ultrasound using convolutional neural networks," 2021 25th International Conference on System Theory, Control and Computing (ICSTCC), 2021, pp. 242-248, doi: 10.1109/ICSTCC52150.2021.9606852.
X