Evidence map›Paper›PMID 41264403›Full record

ArticleAmerican journal of physiology. Renal physiology2026

AutoGlom: software tool for segmentation and analysis of magnetic resonance images of the kidney.

Teng Li, Adam Cochran, Yanzhe Xu, Jennifer R Charlton, Kevin M Bennett, Sage M Timberline, Rachel K Dailey, Syeda Y Jannath, Edwin J Baldelomar, Matthew R Hoch and 1 more

Abstract read
In one paragraph

Article in American journal of physiology. Renal physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Are noninvasive measurements of nephron number achievable in humans?Current opinion in nephrology and hypertension · 2026
    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

11 authors.

Teng LiSchool of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, United States.ORCID 0009-0006-3807-4226
Adam CochranAmazon.com, Inc., Seattle, Washington, United States.
Yanzhe XuMeta Platforms, Inc., Menlo Park, California, United States.
Jennifer R CharltonDepartment of Pediatrics, University of Virginia Children's Hospital, Charlottesville, Virginia, United States.
Kevin M BennettMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States.ORCID 0000-0003-1706-4660
Sage M TimberlineDepartment of Pediatrics, University of Virginia Children's Hospital, Charlottesville, Virginia, United States.
Rachel K DaileyDepartment of Pediatrics, University of Virginia Children's Hospital, Charlottesville, Virginia, United States.
Syeda Y JannathMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States.
Edwin J BaldelomarMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States.ORCID 0000-0002-3482-373X
Matthew R HochDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States.
Teresa WuSchool of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, United States.ORCID 0000-0002-0529-7048

Funding

Single-Cell Epigenomics, Transcriptomics, and Bioinformatics CoreP50DK096373 · NIDDK · UNIVERSITY OF VIRGINIA · PI ROBERTO Ariel GOMEZ · 2012 to 2026
$13.8M
Investigating the effect of dysregulated vitamin D metabolism on kidney development following preterm birthR01DK136989 · NIDDK · UNIVERSITY OF VIRGINIA · PI Jennifer R Charlton, Kimberly Jean Reidy · 2024 to 2026
$2.1M
Small-Animal 9.4T MRI for Biomedical ResearchS10OD025024 · OD · UNIVERSITY OF VIRGINIA · PI BERR, STUART S. · 2019 to 2019
$2.0M
Comprehensive MRI-based evaluation of human renal microstructureR01DK111861 · NIDDK · UNIVERSITY OF VIRGINIA · PI BENNETT, KEVIN M, CHARLTON, JENNIFER R · 2017 to 2020
$1.8M
Noninvasive MRI techniques to detect pathology in murine models of renal diseaseR01DK110622 · NIDDK · WASHINGTON UNIVERSITY · PI BENNETT, KEVIN M, CHARLTON, JENNIFER R · 2017 to 2019
$1.0M
Investigating the role of nephron mass in the progression of CKD using MRIR56DK110622 · NIDDK · WASHINGTON UNIVERSITY · PI BENNETT, KEVIN M, CHARLTON, JENNIFER R · 2021 to 2021
$100k
HHS | National Institutes of Health (NIH) P50DK096373-11HHS | National Institutes of Health (NIH) S10OD025024HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) R01DK110622HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) R01DK111861HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) R01DK136989NIDDK NIH HHS P50 DK096373NIDDK NIH HHS R01 DK110622NIDDK NIH HHS R01 DK111861NIDDK NIH HHS R01 DK136989NIDDK NIH HHS R56 DK110622
6 · The paper itself

Abstract

Magnetic resonance imaging (MRI) is increasingly important in preclinical and clinical investigations of the kidney. However, there are few user-friendly, flexible, and standardized tools for evaluating MR images for quantitative imaging analysis. Here, we develop AutoGlom, an open-source, modular, and expandable imaging software tool that incorporates artificial intelligence (AI) for segmentation, analysis, and visualization of three-dimensional (3-D) MR images of the kidney. This initial version of AutoGlom focuses on morphological segmentation and quantification. We describe kidney segmentation from MR images, followed by the use of the graphical user interface of AutoGlom. Using AutoGlom, we measure glomerular number and volume from ex vivo cationic ferritin-enhanced MRI (CFE-MRI) in mice. We further demonstrate a 3-D-printed holder to allow for simultaneous imaging of up to 16 mouse kidneys at high resolution (50 μm) within several hours. The streamlined workflow facilitates rapid image analysis and accelerates optimization of cationic ferritin dosing and imaging parameters. These tools are a resource for the kidney community that may accelerate the identification of candidate imaging biomarkers from 3-D MRI of the kidney and have the potential to be extended to in vivo studies and other imaging modalities.

Indexed as

Image Interpretation, Computer-AssistedImaging, Three-DimensionalKidneyKidney GlomerulusMagnetic Resonance ImagingSoftwareAnimalsArtificial IntelligenceMiceMice, Inbred C57BLPredictive Value of Testsdeep learningimage segmentationkidneymagnetic resonance imagingmedical image analysis

Identifiers

PMID41264403
PMCPMC13008130

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.