Accuracy of automated three‐dimensional ultrasound imaging technique for fetal head biometry

Pluym, Ilina D.; Afshar, Yalda; Holliman, Kerry; Kwan, Lorna; Bolagani, Aswani; Mok, Thalia; Silver, BE; E, Ramirez; Han, Christina S.; Platt, Lawrence D.

doi:10.1002/uog.22171

Cited by 27 publications

(27 citation statements)

References 28 publications

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“…Nevertheless, manual navigation through brain volume datasets to retrieve differently oriented diagnostic planes along the x-, y-, and z-axes necessitates a comprehensive understanding of fetal CNS architecture and a spatial sense of anatomic relations and hence is highly operator dependent, especially when CNS abnormalities are suspected. It has recently been shown that automated volumetric approaches e ciently enable rapid and standardized evaluation of the fetal brain in terms of basic examination [41][42][43] or reconstruction of an entire neurosonogram. [12][13][14] By simplifying the evaluation process of basic and/or advanced CNS examination, these algorithms might aid in earlier detection of abnormal CNS anatomy in utero.…”

Section: Discussionmentioning

confidence: 99%

Applicability of a Semiautomated Volumetric Approach (5D CNS+™) for Detailed Antenatal Reconstruction of Abnormal Fetal CNS Anatomy

Welp

Gembicki

Dracopoulos

et al. 2021

Preprint

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Background. The aim of this study was to evaluate the accuracy and reliability of a semiautomated volumetric approach (5DCNS+) when examining fetuses with an apparent abnormal anatomy of the central nervous system (CNS).Methods. Stored 3D volumes of 91 fetuses with structural cerebral anomalies extracted from a cohort of >1.400 consecutive 2nd and 3rd trimester pregnancies (range 15-36 gestational weeks) were analyzed using the semiautomatic software tool 5DCNS+, enabling detailed reconstruction of nine diagnostic planes of the fetal brain. All 3D data sets were examined and judged for plane accuracy, the need for manual adjustment, and fetal CNS anomalies affecting successful plane reconstruction.Results. Based on our data, we were able to reveal a wide range of CNS anomalies with application of the 5DCNS+ technique. The corresponding anatomical features and consecutive changes of neighboring structures could be clearly demonstrated. Thus, a profound assessment of the entire altered CNS anatomy could be achieved in nearly all cases. The comparison with matched controls showed a significant difference in volume acquisition (p <0.001) and in need for manual adjustment (p < 0.001) but not in the drop-out rates (p = 0.677) of both groups.Conclusion. 5DCNS+ software is applicable in the majority of cases with brain lesions and constitutes a reliable tool even if the integrity of the fetal CNS is compromised by structural anomalies. Using volume data that were acquired in identical cutting sections needed for conventional biometry allows for detailed anatomic surveys grossly independent of the examiner’s experience.

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Section: Discussionmentioning

confidence: 99%

Applicability of a Semiautomated Volumetric Approach (5D CNS+™) for Detailed Antenatal Reconstruction of Abnormal Fetal CNS Anatomy

Welp

Gembicki

Dracopoulos

et al. 2021

Preprint

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show abstract

“…Common measurement items included fetal lateral ventricles, transcerebellar, cisterna magna, and posterior horn of the lateral ventricle, etc. (19,20). The methods were not very different from those of neonates, except for the image quality (the fetal head was further from the ultrasound probe, and sheltered by adjacent tissue or anatomical structures) and the difficulties of location caused by the fetal activity.…”

Section: Automatic Segmentation Of Fetal Head and Its Internal Structurementioning

confidence: 98%

Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications

Chen

Liu

et al. 2021

Front. Med.

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Artificial intelligence (AI) can support clinical decisions and provide quality assurance for images. Although ultrasonography is commonly used in the field of obstetrics and gynecology, the use of AI is still in a stage of infancy. Nevertheless, in repetitive ultrasound examinations, such as those involving automatic positioning and identification of fetal structures, prediction of gestational age (GA), and real-time image quality assurance, AI has great potential. To realize its application, it is necessary to promote interdisciplinary communication between AI developers and sonographers. In this review, we outlined the benefits of AI technology in obstetric ultrasound diagnosis by optimizing image acquisition, quantification, segmentation, and location identification, which can be helpful for obstetric ultrasound diagnosis in different periods of pregnancy.

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“…In other recent research papers in our field, algorithms have been shown to be useful in identifying the fetal occiput position during labor 8 and in the classification of ovarian tumors as benign or malignant 9 . Additionally, major ultrasound machine manufacturers have unveiled applications that are based on algorithms, although their development and validity often remain unreported for commercial reasons [10][11][12] . Therefore, all of these AI systems should remain at Level 2 of automation (Figure 2) until robust clinical evidence is collected via prospective trials.…”

Section: Drukkermentioning

confidence: 99%

Real‐time identification of fetal anomalies on ultrasound using artificial intelligence: what's next?

Drukker

2022

Ultrasound in Obstet & Gyne

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Accuracy of automated three‐dimensional ultrasound imaging technique for fetal head biometry

Cited by 27 publications

References 28 publications

Applicability of a Semiautomated Volumetric Approach (5D CNS+™) for Detailed Antenatal Reconstruction of Abnormal Fetal CNS Anatomy

Applicability of a Semiautomated Volumetric Approach (5D CNS+™) for Detailed Antenatal Reconstruction of Abnormal Fetal CNS Anatomy

Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications

Real‐time identification of fetal anomalies on ultrasound using artificial intelligence: what's next?

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