Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images

Alzubaidi, Mahmood; Agus, Marco; Alyafei, Khalid; Al-Thelaya, Khaled A.; Shah, Uzair; Abd‐Alrazaq, Alaa; Anbar, Mohammed; Makhlouf, Michel; Househ, Mowafa

doi:10.1016/j.isci.2022.104713

Cited by 18 publications

(9 citation statements)

References 169 publications

(475 reference statements)

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“…These methods have shown promise in identifying a variety of anomalies, such as skeletal anomalies, abdominal deformities, heart defects, and neural tube defects. The ability of machine learning and deep learning models to learn from large datasets allows them to capture subtle patterns and complex relationships that human observers may find difficult to recognize [108,109]. As these models are trained on a variety of datasets, they may be able to spot anomalies that less skilled or outdated sonographers might miss, increasing diagnostic precision and lowering the likelihood of missed diagnoses.…”

Section: Machine Learning and Deep Learning In Us Analysismentioning

confidence: 99%

Ultrasound imaging based recognition of prenatal anomalies: a systematic clinical engineering review

Sriraam,

Chinta,

Suresh

et al. 2024

Prog. Biomed. Eng.

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For prenatal screening, ultrasound imaging allows for real-time observation of developing fetal anatomy. Understanding normal and aberrant forms through extensive fetal structural assessment enables for early detection and intervention. However, the reliability of anomaly diagnosis varies depending on operator expertise and device limits. First trimester scans in conjunction with circulating biochemical markers are critical in identifying high-risk pregnancies, but they also pose technical challenges. Recent engineering advancements in automated diagnosis, such as AI-based ultrasound image processing and multimodal data fusion, are developing to improve screening efficiency, accuracy, and consistency. Still, creating trust in these data-driven solutions is necessary for integration and acceptability in clinical settings. Transparency can be promoted by explainable AI (XAI) technologies that provide visual interpretations and illustrate the underlying diagnostic decision making process. An explanatory framework based on deeplearning is suggested to construct charts depicting anomaly screening results from ultrasound video feeds. AI modeling can then be applied to these charts to connect defects with probable deformations. Overall, engineering approaches that increaseimaging, automation, and interpretability hold enormous promise for altering traditional workflows and expanding diagnostic capabilities for better prenatal care.

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Section: Machine Learning and Deep Learning In Us Analysismentioning

confidence: 99%

Ultrasound imaging based recognition of prenatal anomalies: a systematic clinical engineering review

Sriraam,

Chinta,

Suresh

et al. 2024

Prog. Biomed. Eng.

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“…It is impossible to provide here an extensive overview of the literature related to these topics. We refer readers to the survey and review [ 4 , 25 , 26 , 27 ]. In the following, we discuss the methods that are most closely related to our work.…”

Section: Related Workmentioning

confidence: 99%

“…This method had significantly better skills in image processing tasks due to their promising capabilities. In particular, CNN has emerged as a top choice for medical image classification, segmentation, and object detection [ 4 ]. UNet [ 22 ] is a network often used for biomedical image segmentation because of the symmetric structure observed in the images, allowing for the efficient use of skip connection layers and the reduced computing complexity.…”

Section: Related Workmentioning

confidence: 99%

“…Ultrasound may be utilized in various prenatal diagnostic situations, including: confirming the pregnancy and the position of the fetus, calculating the gestational age of the fetal baby, verifying the number of fetal bodies, examining fetal development, examining the amounts of the placenta and amniotic fluid, identifying congenital disabilities, looking into complications, and other prenatal tests [ 3 ]. When ultrasound is routinely used in early pregnancy, it will result in an earlier detection of problems and an improved management of pregnancy complications, which is better than relying on clinical indicators such as bleeding in early pregnancy [ 4 ]. Halle et al [ 5 ] reported that 1111 women received prenatal treatment at primary care health centers in their health cohort.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction

et al. 2022

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Ultrasound is one of the most commonly used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. Specifically, ultrasound images are routinely utilized to gather fetal information, including body measurements, anatomy structure, fetal movements, and pregnancy complications. Recent developments in artificial intelligence and computer vision provide new methods for the automated analysis of medical images in many domains, including ultrasound images. We present a full end-to-end framework for segmenting, measuring, and estimating fetal gestational age and weight based on two-dimensional ultrasound images of the fetal head. Our segmentation framework is based on the following components: (i) eight segmentation architectures (UNet, UNet Plus, Attention UNet, UNet 3+, TransUNet, FPN, LinkNet, and Deeplabv3) were fine-tuned using lightweight network EffientNetB0, and (ii) a weighted voting method for building an optimized ensemble transfer learning model (ETLM). On top of that, ETLM was used to segment the fetal head and to perform analytic and accurate measurements of circumference and seven other values of the fetal head, which we incorporated into a multiple regression model for predicting the week of gestational age and the estimated fetal weight (EFW). We finally validated the regression model by comparing our result with expert physician and longitudinal references. We evaluated the performance of our framework on the public domain dataset HC18: we obtained 98.53% mean intersection over union (mIoU) as the segmentation accuracy, overcoming the state-of-the-art methods; as measurement accuracy, we obtained a 1.87 mm mean absolute difference (MAD). Finally we obtained a 0.03% mean square error (MSE) in predicting the week of gestational age and 0.05% MSE in predicting EFW.

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“…During pregnancy, women typically have one to three ultrasounds; however, if the woman is pregnant with twins, ultrasounds may be required more regularly [2].Ultrasound is used in a variety of prenatal diagnostic situations, including confirming pregnancy and fetus position, calculating the gestational age of the fetus, verifying the number of fetuses, examining fetus development, determining the amount of placenta and amniotic fluid, identifying congenital disabilities, and investigating complications, among others [3]. When ultrasonography is used routinely throughout early pregnancy, it results in earlier detection of issues and improved management of pregnancy difficulties, as opposed to depending on clinical signs such as bleeding during early pregnancy [4].…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Framework for Fetus Head Analysis Based on Ultrasound Images

Alzubaidi

Agus

Shah

et al. 2022

Studies in Health Technology and Informatics

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Ultrasound images are the most used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. In particular, the obstetrician uses fetus head images to monitor the growth state and identify essential features such as Gestational age (GA), estimated fetus weight (EFW), and brain anatomical structures. However, this work requires an expert obstetrician, and it is time-consuming and costly. Therefore, we proposed an automatic framework by adopting a hybrid approach that combines three components i) automatic segmentation to segment the region of interest (ROI) in the fetus head, ii) measurement extraction to measure the segmented ROI, and iii) anomaly and features detection to predict fetus GA, EFW, and abnormality status.

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Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images

Cited by 18 publications

References 169 publications

Ultrasound imaging based recognition of prenatal anomalies: a systematic clinical engineering review

Ultrasound imaging based recognition of prenatal anomalies: a systematic clinical engineering review

Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction

A Conceptual Framework for Fetus Head Analysis Based on Ultrasound Images

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