Evidence map›Paper›PMID 41402396›Full record

ArticleScientific reports2025

Fetal Assessment Suite (FetAS): a web-based platform for automatic fetal MRI analysis using AI.

Alejo Costanzo, Adam Lim, Michael Pereira, Yasmin Modarai, Justin Lo, Nika Momeni, Joshua Eisenstat, Matthias W Wagner, Logi Vidarsson, Elka Miller and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Alejo Costanzo *Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
Adam Lim *Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
Michael PereiraDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
Yasmin ModaraiDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
Justin LoDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
Nika MomeniDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
Joshua EisenstatDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
Matthias W WagnerDivision of Neuroradiology, The Hospital for Sick Children, Toronto, ON, Canada.
Logi VidarssonDepartment of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada.
Elka MillerDivision of Neuroradiology, The Hospital for Sick Children, Toronto, ON, Canada.
Birgit Ertl-WagnerDivision of Neuroradiology, The Hospital for Sick Children, Toronto, ON, Canada.
Dafna SussmanDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada. dafna.sussman@torontomu.ca.

Funding

CIHR 202303PJT-496119-MPI-ABAF-192837Government of Ontario ER21-16-130
6 · The paper itself

Abstract

Fetal MRI provides high-resolution, three-dimensional imaging with superior soft tissue contrast, offering critical diagnostic insights that complement ultrasound, particularly in cases of suspected abnormalities. However, its interpretation remains labour-intensive and highly dependent on subspecialized radiological expertise, which limits accessibility and contributes to diagnostic delays. To address these challenges, we present the Fetal Assessment Suite (FetAS), a secure, web-based platform designed to streamline and standardize fetal MRI analysis. FetAS automates key tasks, including artifact detection, motion correction, segmentation of the fetal body, amniotic fluid, and placenta, as well as classification of fetal and placental position and orientation, using AI models developed to reduce reliance on expert radiologists. By consolidating these capabilities into a single, user-friendly interface, FetAS reduces the cognitive and time burden of interpretation, supports timely clinical decision-making, and promotes equitable access to care in regions without local expertise. In addition, FetAS enables research through standardized pipelines, facilitates data sharing, and provides a foundation for future advancements in maternal-fetal imaging. Collectively, these features establish FetAS as both a diagnostic tool and a platform for expanding high-quality prenatal care across diverse healthcare settings.

Indexed as

Artificial IntelligenceFetusMagnetic Resonance ImagingPrenatal DiagnosisFemaleHumansImaging, Three-DimensionalInternetPregnancyDeep learningFetal abnormalitiesFetal biometryFetal MRIWeb-Based softwareWorkflow automation

Identifiers

PMID41402396
PMCPMC12819390

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.