ArticleFrontiers in artificial intelligence2022
Fetal Organ Anomaly Classification Network for Identifying Organ Anomalies in Fetal MRI.
Article in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Deep learning-based detection of murine congenital heart defects from µCT scans.Communications biology · 2025Article
- Fetal Assessment Suite (FetAS): a web-based platform for automatic fetal MRI analysis using AI.Scientific reports · 2025Article
- ISUOG Practice Guidelines (updated): performance of fetal magnetic resonance imaging.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2023Article
- Fet-Net Algorithm for Automatic Detection of Fetal Orientation in Fetal MRI.Bioengineering (Basel, Switzerland) · 2023Article
- PANDA: Patch-based unsupervised deep learning for brain anomaly detection via age prediction in fetal MRI.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid development in Magnetic Resonance Imaging (MRI) has played a key role in prenatal diagnosis over the last few years. Deep learning (DL) architectures can facilitate the process of anomaly detection and affected-organ classification, making diagnosis more accurate and observer-independent. We propose a novel DL image classification architecture, Fetal Organ Anomaly Classification Network (FOAC-Net), which uses squeeze-and-excitation (SE) and naïve inception (NI) modules to automatically identify anomalies in fetal organs. This architecture can identify normal fetal anatomy, as well as detect anomalies present in the (1) brain, (2) spinal cord, and (3) heart. In this retrospective study, we included fetal 3-dimensional (3D) SSFP sequences of 36 participants. We classified the images on a slice-by-slice basis. FOAC-Net achieved a classification accuracy of 85.06, 85.27, 89.29, and 82.20% when predicting brain anomalies, no anomalies (normal), spinal cord anomalies, and heart anomalies, respectively. In a comparison study, FOAC-Net outperformed other state-of-the-art classification architectures in terms of class-average F1 and accuracy. This work aims to develop a novel classification architecture identifying the affected organs in fetal MRI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.