Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging

Vahedifard, Farzan; Adepoju, Jubril O; Supanich, Mark; Ai, Hua Asher; Liu, Xuchu; Kocak, Mehmet; Marathu, Kranthi K; Byrd, Sharon E

doi:10.12998/wjcc.v11.i16.3725

Cited by 5 publications

(5 citation statements)

References 53 publications

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“…A simple system-based classification based on a fetal MRI midline sagittal image is the first step in the evaluation of CCA; however, future studies may use advanced imaging techniques like functional MRI or fractional anisotropy for precise anatomic subclassification [20]. The use of machine learning models in conjunction with genetic and clinical data to analyze prenatal brain multi-parametric MRI data may improve diagnosis and postnatal outcome monitoring [53]. The deep learning-facilitated pipeline helps radiologists select good-quality fetal brain images in a shorter time frame and facilitates anatomical measurements [54].…”

Section: Discussionmentioning

confidence: 99%

Fetal MRI Analysis of Corpus Callosal Abnormalities: Classification, and Associated Anomalies

Marathu,

Vahedifard,

Kocak

et al. 2024

Diagnostics

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Background. Corpus callosal abnormalities (CCA) are midline developmental brain malformations and are usually associated with a wide spectrum of other neurological and non-neurological abnormalities. The study aims to highlight the diagnostic role of fetal MRI to characterize heterogeneous corpus callosal abnormalities using the latest classification system. It also helps to identify associated anomalies, which have prognostic implications for the postnatal outcome. Methods. In this study, retrospective data from antenatal women who underwent fetal MRI between January 2014 and July 2023 at Rush University Medical Center were evaluated for CCA and classified based on structural morphology. Patients were further assessed for associated neurological and non-neurological anomalies. Results. The most frequent class of CCA was complete agenesis (79.1%), followed by hypoplasia (12.5%), dysplasia (4.2%), and hypoplasia with dysplasia (4.2%). Among them, 17% had isolated CCA, while the majority (83%) had complex forms of CCA associated with other CNS and non-CNS anomalies. Out of the complex CCA cases, 58% were associated with other CNS anomalies, while 8% were associated with non-CNS anomalies. 17% of cases had both. Conclusion. The use of fetal MRI is valuable in the classification of abnormalities of the corpus callosum after the confirmation of a suspected diagnosis on prenatal ultrasound. This technique is an invaluable method for distinguishing between isolated and complex forms of CCA, especially in cases of apparent isolated CCA. The use of diffusion-weighted imaging or diffusion tensor imaging in fetal neuroimaging is expected to provide further insights into white matter abnormalities in fetuses diagnosed with CCA in the future.

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Section: Discussionmentioning

confidence: 99%

Fetal MRI Analysis of Corpus Callosal Abnormalities: Classification, and Associated Anomalies

Marathu,

Vahedifard,

Kocak

et al. 2024

Diagnostics

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“…Manual measurements have several disadvantages, including clinician training requirements, time commitment, and inter-and intra-observer variability [22,23]. AI and deep learning have several capabilities in the automatization of fetal brain MRI tasks, which we review in a separate paper [8].…”

Section: Similar Studiesmentioning

confidence: 99%

“…The fetal MRI measures ventricles the same way the US does in the axial plane [8,9]. During pregnancy, the size of the atrium and lateral ventricles stays mostly the same, with ventricular width less than 10 mm considered normal [9].…”

Section: Introductionmentioning

confidence: 99%

“…Manual measurement of the ventricle size requires specialized training of the physician and can be very time-consuming, given that a fetal MRI exam can contain a large number of 2D series due to repeated acquisitions [4,6]. In addition, because manual measurements are performed on 2D slices, they can potentially contain errors if the 2D slices The fetal MRI measures ventricles the same way the US does in the axial plane [8,9]. During pregnancy, the size of the atrium and lateral ventricles stays mostly the same, with ventricular width less than 10 mm considered normal [9].…”

Section: Introductionmentioning

confidence: 99%

“…Automated segmentation could save time and manual labor [7]. Automated brain tissue segmentation in prenatal MRI is difficult due to increased intensity inhomogeneity and spontaneous fetal movements [8]. In recent years, convolutional neural networks (CNNs) have become the most popular medical imaging segmentation method [9].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Ventriculomegaly Detection in Fetal Brain MRI: A Step-by-Step Deep Learning Model for Novel 2D-3D Linear Measurements

Vahedifard,

Ai,

Supanich

et al. 2023

Diagnostics

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In this study, we developed an automated workflow using a deep learning model (DL) to measure the lateral ventricle linearly in fetal brain MRI, which are subsequently classified into normal or ventriculomegaly, defined as a diameter wider than 10 mm at the level of the thalamus and choroid plexus. To accomplish this, we first trained a UNet-based deep learning model to segment the brain of a fetus into seven different tissue categories using a public dataset (FeTA 2022) consisting of fetal T2-weighted images. Then, an automatic workflow was developed to perform lateral ventricle measurement at the level of the thalamus and choroid plexus. The test dataset included 22 cases of normal and abnormal T2-weighted fetal brain MRIs. Measurements performed by our AI model were compared with manual measurements performed by a general radiologist and a neuroradiologist. The AI model correctly classified 95% of fetal brain MRI cases into normal or ventriculomegaly. It could measure the lateral ventricle diameter in 95% of cases with less than a 1.7 mm error. The average difference between measurements was 0.90 mm in AI vs. general radiologists and 0.82 mm in AI vs. neuroradiologists, which are comparable to the difference between the two radiologists, 0.51 mm. In addition, the AI model also enabled the researchers to create 3D-reconstructed images, which better represent real anatomy than 2D images. When a manual measurement is performed, it could also provide both the right and left ventricles in just one cut, instead of two. The measurement difference between the general radiologist and the algorithm (p = 0.9827), and between the neuroradiologist and the algorithm (p = 0.2378), was not statistically significant. In contrast, the difference between general radiologists vs. neuroradiologists was statistically significant (p = 0.0043). To the best of our knowledge, this is the first study that performs 2D linear measurement of ventriculomegaly with a 3D model based on an artificial intelligence approach. The paper presents a step-by-step approach for designing an AI model based on several radiological criteria. Overall, this study showed that AI can automatically calculate the lateral ventricle in fetal brain MRIs and accurately classify them as abnormal or normal.

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Fetal MRI Analysis of Corpus Callosum Abnormalities: Classification and Associated Anomalies

Marathu,

Vahedifard,

Kocak

et al. 2024

Preprint

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Background Corpus callosum abnormalities (CCA) are midline developmental brain malformations and are usually associated with a wide spectrum of other neurological and non-neurological abnormalities. The study aims to highlight the diagnostic role of fetal MRI to characterize heterogeneous corpus callosum abnormalities using the latest classification system. It also helps to identify associated anomalies, which have prognostic implications on the postnatal outcome. Methods In this study, retrospective data from antenatal women who underwent fetal MRI between January 2014 and July 2023 at Rush University Medical Center were evaluated for CCA and classified based on structural morphology. Patients were further assessed for associated neurological and non-neurological anomalies. Results The most frequent class of CCA was complete agenesis (79.1%), followed by hypoplasia (12.5%), dysplasia (4.2%) and hypoplasia with dysplasia (4.2%). Amongst them, 17% had isolated CCA, while the majority (83%) had complex forms of CCA associated with other CNS and non-CNS anomalies. Out of the complex CCA cases, 58% were associated with other CNS anomalies, while 8% were associated with non-CNS anomalies. 17% of cases had both. Conclusion The use of fetal MRI is valuable in the classification of abnormalities of the corpus callosum after the confirmation of a diagnosis suspected on prenatal ultrasound. This technique is an invaluable method for distinguishing between isolated and complex forms of CCA, especially in the cases of apparent isolated CCA. The use of diffusion-weighted imaging or diffusion tensor imaging in fetal neuroimaging is expected to provide further insights into white matter abnormalities in fetuses diagnosed with CCA in the future.

show abstract

Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging

Cited by 5 publications

References 53 publications

Fetal MRI Analysis of Corpus Callosal Abnormalities: Classification, and Associated Anomalies

Fetal MRI Analysis of Corpus Callosal Abnormalities: Classification, and Associated Anomalies

Automatic Ventriculomegaly Detection in Fetal Brain MRI: A Step-by-Step Deep Learning Model for Novel 2D-3D Linear Measurements

Fetal MRI Analysis of Corpus Callosum Abnormalities: Classification and Associated Anomalies

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