Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches

Yousefpour Shahrivar, Ramin; Karami, Fatemeh; Karami, Ebrahim

doi:10.3390/biomimetics8070519

Cited by 3 publications

(2 citation statements)

References 170 publications

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“…Undoubtedly, an integral part of prenatal examination is ultrasound diagnostics, which is highly informative and safe, and every year not only the technical capabilities of ultrasound machines, but also the system for assessing the development of the fetus are improved. R. Yousefpour Shahrivar et al [28] emphasize the need for further research in this field, which will contribute to more effective detection of fetal abnormalities based on ultrasound.…”

Section: Discussionmentioning

confidence: 99%

Fetal ultrasound anatomy and morphometric parameters of the tibia

Komar,

Khmara,

Protsak

et al. 2024

Rep. of Morph.

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Ultrasound screening is one of the most informative methods of visualizing fetal development during pregnancy. During standard ultrasound examinations, the length of the femur of the fetus is measured. However, if any skeletal dysplasia or intrauterine growth retardation is suspected, additional measurement of other long tubular bones should be performed. The aim of the work was to investigate the morphometric parameters of the lengths of the right and left tibia bones and to establish correlations between them and the lengths of the corresponding fibula, lower limb and crown rump length of the fetus during the fetal period of human ontogenesis. Intravital ultrasound examination of the leg bones of 38 human fetuses was performed at the medical center "YUZKO MEDICAL CENTER" in accordance with the cooperation agreement. Built-in capabilities of MS Excel were used for statistical calculations. The established data on the fetal morphometry of tibial lengths indicate relatively uniform growth of the right and left tibias during the fetal period of human ontogenesis: the length of the right tibia increases from 24.77±2.25 mm to 65.32±2.20 mm, and the length of the left tibia increases from 25.19±1.63 mm to 65.78±1.81 mm. The analysis of correlations between the length of the right and left tibia bones, the length of the fibula bones, the length of the lower limbs and the crown rump length in human fetuses aged from 4 to 10 months of intrauterine development allowed to reveal reliable strong and medium strength reliable and unreliable, mostly direct, connections in all age groups. The obtained morphometric parameters of tibia bones in fetuses of different gestational periods are important additional dimensions of fetobiometry, which can be used if a detailed assessment of the state of fetal development is necessary and in case of suspicion of congenital anomalies.

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Section: Discussionmentioning

confidence: 99%

Fetal ultrasound anatomy and morphometric parameters of the tibia

Komar,

Khmara,

Protsak

et al. 2024

Rep. of Morph.

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“…Beyond GA estimation, AI algorithms have a potential role in advancing obstetric ultrasonography. For example, AI-enabled tools can analyze obstetric ultrasonography images to identify ultrasonography markers that are predictive of anomalies but may be missed by the human eye, helping in the early detection of congenital anomalies . In addition, by providing automated measurements and growth curve analyses, AI can facilitate the detection of fetal growth anomalies, such as intrauterine growth restriction or macrosomia .…”

mentioning

confidence: 99%

Enhancing Obstetric Ultrasonography With Artificial Intelligence in Resource-Limited Settings

Gimovsky,

Eke,

Tuuli

2024

JAMA

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Ensemble machine learning framework for predicting maternal health risk during pregnancy

Khadidos,

Saleem,

Selvarajan

et al. 2024

Sci Rep

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Maternal health risks can cause a range of complications for women during pregnancy. High blood pressure, abnormal glucose levels, depression, anxiety, and other maternal health conditions can all lead to pregnancy complications. Proper identification and monitoring of risk factors can assist to reduce pregnancy complications. The primary goal of this research is to use real-world datasets to identify and predict Maternal Health Risk (MHR) factors. As a result, we developed and implemented the Quad-Ensemble Machine Learning framework to predict Maternal Health Risk Classification (QEML-MHRC). The methodology used a vacxsriety of Machine Learning (ML) models, which then integrated with four ensemble ML techniques to improve prediction. The dataset collected from various maternity hospitals and clinics subjected to nineteen training and testing tests. According to the exploratory data analysis, the most significant risk factors for pregnant women include high blood pressure, low blood pressure, and high blood sugar levels. The study proposed a novel approach to dealing with high-risk factors linked to maternal health. Dealing with class-specific performance elaborated further to properly understand the distinction between high, low, and medium risks. All tests yielded outstanding results when predicting the amount of risk during pregnancy. In terms of class performance, the dataset associated with the “HR” class outperformed the others, predicting 90% correctly. GBT with ensemble stacking outperformed and demonstrated remarkable performance for all evaluation measure (0.86) across all classes in the dataset. The key success of the models used in this work is the ability to measure model performance using a class-wise distribution. The proposed approach can help medical experts assess maternal health risks, saving lives and preventing complications throughout pregnancy. The prediction approach presented in this study can detect high-risk pregnancies early on, allowing for timely intervention and treatment. This study’s development and findings have the potential to raise public awareness of maternal health issues.

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Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches

Cited by 3 publications

References 170 publications

Fetal ultrasound anatomy and morphometric parameters of the tibia

Fetal ultrasound anatomy and morphometric parameters of the tibia

Enhancing Obstetric Ultrasonography With Artificial Intelligence in Resource-Limited Settings

Ensemble machine learning framework for predicting maternal health risk during pregnancy

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