Evidence map›Paper›PMID 40563043›Full record

ArticleJournal of imaging informatics in medicine2026

ARANet: Adaptive Resolution Attention Network for Precise MRI-Based Segmentation and Quantification of Fetal Size and Amniotic Fluid Volume.

Adham M Alkhadrawi, Valeria Peña-Trujillo, Sebastian Gallo-Bernal, Michael S Gee, Camilo E Jaimes Cobos, Kyungsu Kim, Saul Langarica, Young-Tak Kim, Teresa Victoria, Synho Do

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Adham M Alkhadrawi *Department of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, 96822, USA.
Valeria Peña-Trujillo *Department of Radiology, Harvard Medical School, Boston, MA, 02114, USA.
Sebastian Gallo-BernalDepartment of Radiology, Harvard Medical School, Boston, MA, 02114, USA.
Michael S GeeDepartment of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, 96822, USA.
Camilo E Jaimes CobosDepartment of Radiology, Harvard Medical School, Boston, MA, 02114, USA.
Kyungsu KimSchool of Transdisciplinary Innovations, Seoul National University, Seoul, South Korea.
Saul LangaricaDepartment of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, 96822, USA.
Young-Tak KimDepartment of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, 96822, USA.
Teresa VictoriaDepartment of Radiology, Harvard Medical School, Boston, MA, 02114, USA.
Synho DoDepartment of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, 96822, USA. sdo@mgh.harvard.edu.ORCID http://orcid.org/0000-0001-6211-7050

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amniotic fluid volume (AFV) is a critical indicator of fetal health, traditionally assessed using ultrasound-based methods, which are limited by operator dependency and 2D measurements. While MRI offers superior tissue characterization, it remains underutilized for AFV assessment due to labor-intensive manual segmentations. Pulse sequence variations in MRI can significantly influence image contrast through T1 and T2 weighting and potentially cause signal dropout in certain regions, making automated analysis challenging. To address these challenges, we present ARANet (Adaptive Resolution Attention Network), featuring a novel adaptive resolution attention module that uniquely combines adaptive resolution processing with channel-wise attention mechanisms for precise MRI-based segmentation. In extensive evaluations, ARANet demonstrates superior performance with a Dice score of 0.961 at full brightness, maintaining robust performance (0.909) even at 30% brightness, significantly outperforming existing models in challenging low-contrast conditions. We introduce FAFO

Indexed as

Amniotic FluidFetusImage Processing, Computer-AssistedMagnetic Resonance ImagingFemaleHumansPregnancyAFV classificationAFV segmentationAmniotic fluid volumeFetal developmentFetal MRIFetal volume

Identifiers

PMID40563043
PMCPMC13103092

What OpenQuestion holds

Textmetadata
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.