Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities
A fetal ultrasound (US) is a technique to examine a baby’s maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health....
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/12/4570 |
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| author | Irfan Ullah Khan Nida Aslam Fatima M. Anis Samiha Mirza Alanoud AlOwayed Reef M. Aljuaid Razan M. Bakr |
| author_facet | Irfan Ullah Khan Nida Aslam Fatima M. Anis Samiha Mirza Alanoud AlOwayed Reef M. Aljuaid Razan M. Bakr |
| author_sort | Irfan Ullah Khan |
| collection | DOAJ |
| description | A fetal ultrasound (US) is a technique to examine a baby’s maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health. Disorders resulting from abnormal AFV levels, commonly referred to as oligohydramnios or polyhydramnios, may pose a serious threat to a mother’s or child’s health. This paper attempts to accumulate and compare the most recent advancements in Artificial Intelligence (AI)-based techniques for the diagnosis and classification of AFV levels. Additionally, we provide a thorough and highly inclusive breakdown of other relevant factors that may cause abnormal AFV levels, including, but not limited to, abnormalities in the placenta, kidneys, or central nervous system, as well as other contributors, such as preterm birth or twin-to-twin transfusion syndrome. Furthermore, we bring forth a concise overview of all the Machine Learning (ML) and Deep Learning (DL) techniques, along with the datasets supplied by various researchers. This study also provides a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to further explore. |
| first_indexed | 2024-03-09T22:32:14Z |
| format | Article |
| id | doaj.art-c2df21b14aa34f899262f554268df44d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:32:14Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c2df21b14aa34f899262f554268df44d2023-11-23T18:55:29ZengMDPI AGSensors1424-82202022-06-012212457010.3390/s22124570Amniotic Fluid Classification and Artificial Intelligence: Challenges and OpportunitiesIrfan Ullah Khan0Nida Aslam1Fatima M. Anis2Samiha Mirza3Alanoud AlOwayed4Reef M. Aljuaid5Razan M. Bakr6Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaA fetal ultrasound (US) is a technique to examine a baby’s maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health. Disorders resulting from abnormal AFV levels, commonly referred to as oligohydramnios or polyhydramnios, may pose a serious threat to a mother’s or child’s health. This paper attempts to accumulate and compare the most recent advancements in Artificial Intelligence (AI)-based techniques for the diagnosis and classification of AFV levels. Additionally, we provide a thorough and highly inclusive breakdown of other relevant factors that may cause abnormal AFV levels, including, but not limited to, abnormalities in the placenta, kidneys, or central nervous system, as well as other contributors, such as preterm birth or twin-to-twin transfusion syndrome. Furthermore, we bring forth a concise overview of all the Machine Learning (ML) and Deep Learning (DL) techniques, along with the datasets supplied by various researchers. This study also provides a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to further explore.https://www.mdpi.com/1424-8220/22/12/4570amniotic fluid (AF)artificial intelligencedeep learningmachine learningoligohydramniospolyhydramnios |
| spellingShingle | Irfan Ullah Khan Nida Aslam Fatima M. Anis Samiha Mirza Alanoud AlOwayed Reef M. Aljuaid Razan M. Bakr Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities Sensors amniotic fluid (AF) artificial intelligence deep learning machine learning oligohydramnios polyhydramnios |
| title | Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities |
| title_full | Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities |
| title_fullStr | Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities |
| title_full_unstemmed | Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities |
| title_short | Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities |
| title_sort | amniotic fluid classification and artificial intelligence challenges and opportunities |
| topic | amniotic fluid (AF) artificial intelligence deep learning machine learning oligohydramnios polyhydramnios |
| url | https://www.mdpi.com/1424-8220/22/12/4570 |
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