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Diagnosis and Detection of Congenital Diseases in New-Borns or Fetuses Using Artificial Intelligence Techniques: A Systematic Review

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Abstract

Artificial intelligence, including machine learning and deep learning, play an essential role in the medical industry for predicting various diseases. One such disease or disorder is a congenital disease that affects the newborn infant or unborn foetus by different viruses carried by the mother and passed on to the baby either during the time of pregnancy or delivery. Based on PRISMA guidelines, an extensive survey has been done to predict congenital diseases, including neonatal and postnatal. We have considered 115 articles related to the prediction of congenital diseases such as Zika virus, congenital heart disease, chromosome anomalies, sepsis, hypertension, cytomegalovirus, and many more using artificial intelligence published from 2008 to 2022 on different databases, journals, and conferences. In addition, the review also depicts the current work done by several researchers in the field of congenital disease prediction, along with their datasets and limitations. For complete work, we have designed four investigations and, in the end, explored solutions for the same. From the survey, it has been found that irrespective of various approaches used in the reported work, they can achieve predicted outcomes, but still, several problems need to be resolved. Thus, there is a need for more extensive research to deal with the challenges in the area of predicting various congenital diseases in the early stages.

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  1. Department of Electronics and Communication Punjabi University, Patiala, India

    Komalpreet Kaur & Charanjit Singh

  2. Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

    Yogesh Kumar

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Kaur, K., Singh, C. & Kumar, Y. Diagnosis and Detection of Congenital Diseases in New-Borns or Fetuses Using Artificial Intelligence Techniques: A Systematic Review. Arch Computat Methods Eng 30, 3031–3058 (2023). https://doi.org/10.1007/s11831-023-09892-2

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