Convolution Neural Network based Next Generation Neonatal Surgery: Innovations and Interventions in Precision surgery for New-borns
Keywords:
Convolution Neural Network (CNN), Deep Learning, Congenital Diaphragmatic Hernia (CDH), Congenital Lung Malformations (CLM)Abstract
Surgical operations carried out on newborns, usually during the first 28 days of their lives, are referred to as neonatal surgery. These operations are frequently required to treat life-threatening illnesses or congenital abnormalities (birth defects) that are discovered either before or soon after delivery. Using machine learning and deep learning, artificial intelligence can revolutionize conventional surgical techniques. One kind of artificial neural network that is particularly good at identifying patterns in visual data is the Convolutional Neural Network (CNN). It does this by employing a unique layer known as a convolutional layer, which uses a process known as convolution to learn how to extract features from the input data. Congenital lung malformations, congenital diaphragmatic hernia (CDH), intestinal atresia, necrotizing enterocolitis (NEC), and esophageal tresia /tracheoesophageal fistula (EA/TEF) are among the critical conditions that can be identified in newborns using a variety of diagnostic techniques, including clinical cases examined using plain radiographs, CT scans, prenatal ultrasounds, and magnetic resonance imaging (MR) images. CNN can accurately identify and categories the kind and degree of a newborn's condition. The evaluation of several CNN-based advanced procedures for newborns, together with their difficulties and potential developments, is the main emphasis of this study.
Downloads
Metrics
References
Yildirim A.E., Canayaz M. (2023), A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images, Biocybernetics and Biomedical Engineering, Volume 43, Issue 4, October–December 2023, Pages 635-655.
Ghosh A., Sufian A., Sultana F., Chakrabarti A., De D. (2020), Fundamental Concepts of Convolutional Neural Network, Recent Trends and Advances in Artificial Intelligence and Internet of Things, pp.519-567.
Amodeo I., Nunzio G., Raffaeli G., Borzani I., Griggio A., Conte L., Macchini F., Condò V., Persico N., Fabietti I., Ghirardello S., Pierro M., Tafuri B., Como G., Cascio D., Colnaghi M., Mosca F., Cavallaro G., (2021), A machine and deep Learning Approach to predict pulmonary hypertension in new-born with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study, PLOSONE, 16(11).
Ervural S., Ceylan M. (2021), Convolutional Neural Networks-Based Approach to Detect Neonatal Respiratory System Anomalies with Limited Thermal Image, Traitement du Signal, Vol. 38, No. 2, pp. 437-442.
Plut D., Bauer M., Mikić A., Winant A.J., Park H., Lee E.Y. (2024), Paediatric Congenital Lung Malformations: Contemporary Perspectives on Imaging Characteristics, Seminars in Roentgenology, Volume 59, Issue 3, pp. 249-266.
Daodu O., Brindle M., (2017), Predicting outcomes in congenital diaphragmatic hernia, Seminars in Pediatric Surgery 26(3).
Chowdhury M.M., Chakraborty S. (2015), Imaging of congenital lung malformations, Seminars in Pediatric Surgery, Volume 24, Issue 4, pp. 168-175.
Mehta P.A., Sharma G. (2023), Congenital Pulmonary Airway Malformation, National Library of Medicine, pp.12-16.
Obuchowicz R., Strzelecki M., Piórkowski A. (2024), Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing-A Review, Cancers 2024, 16, 1870.
Ramalingam A., Karunamurthy A., Victoire T.A, Pavithra, B. (2023), Impact of Artificial Intelligence on Healthcare: A Review of Current Applications and Future Possibilities, Quing: International Journal of Innovative Research in Science and Engineering, 2(2), 37-49.
Russo FM, De Coppi P, Allegaert K, Toelen J, Van Der Veeken L, Attilakos G, et al.. Current and future antenatal management of isolated congenital diaphragmatic hernia. Seminars in Fetal and Neonatal Medicine. 2017; 22 (6):383–90.
Keijzer R, Liu J, Deimling J, Tibboel D, Post M. Dual-hit hypothesis explains pulmonary hypoplasia in the nitrofen model of congenital diaphragmatic hernia. The American journal of pathology. 2000; 156 (4):1299–306.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.