Evidence map›Paper›PMID 42621025›Full record

ArticleFrontiers in bioengineering and biotechnology2026

3D foot and ankle radiographic measurement toolbox.

Andrew C Peterson, Elana Renae Lapins, Melissa R Requist, Karen M Kruger, Amy L Lenz

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2026. 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. 3D foot and ankle radiographic measurement toolbox.Frontiers in bioengineering and biotechnology · 2026
    Article
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

5 authors.

Andrew C PetersonDepartment of Mechanical Engineering, University of Utah, Salt Lake City, UT, United States.
Elana Renae LapinsDepartment of Mechanical Engineering, University of Utah, Salt Lake City, UT, United States.
Melissa R RequistDepartment of Mechanical Engineering, University of Utah, Salt Lake City, UT, United States.
Karen M KrugerJoint Department of Biomedical Engineering, Marquette University, Milwaukee, WI, United States.
Amy L LenzDepartment of Mechanical Engineering, University of Utah, Salt Lake City, UT, United States.

Funding

Classification of Ankle Osteoarthritis Severity from Weightbearing Computed Tomography Using Statistical Shape Modeling and Machine LearningK01AR080221 · NIAMS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Amy L. Lenz · 2022 to 2026
$707k
NIAMS NIH HHS K01 AR080221
6 · The paper itself

Abstract

Two-dimensional radiographic measurements remain the clinical standard for evaluating foot and ankle deformities, despite increasing use of three-dimensional weightbearing computed tomography (WBCT). While WBCT enables improved visualization of osseous relationships under static load, translation of familiar two-dimensional radiographic metrics into three-dimensional imaging environments has been limited by manual workflows and a lack of standardized computational frameworks. To address this gap, we developed the three-dimensional Foot and Ankle Radiographic Measurements (3D FARM) toolbox to automatically compute clinically relevant radiographic measurements from bone models. WBCT scans from 180 adult feet across six clinical groups (rectus, cavus, planus, Charcot-Marie-Tooth, progressive collapsing foot deformity, and tibiotalar/subtalar osteoarthritis) were analyzed. Fourteen bones per foot were segmented using both semi-automatic and manual methods. The 3D FARM toolbox automatically computes 20 commonly used radiographic measurements using anatomically defined coordinate systems. Agreement between segmentation methods was evaluated using intraclass correlation coefficients and mean bias. Measurements from reference manual segmentations were used to generate group-specific reference values. Agreement between semi-automatic and manual segmentations was excellent for 18 of 20 measurements, with the remaining measurements demonstrating good to moderate agreement. Mean bias across measurements was minimal. Group-specific reference values demonstrated distinct distributions across pathologies and aligned qualitatively with published radiographic measurements. The 3D FARM toolbox enables reproducible, automated extraction of clinically familiar radiographic measurements from WBCT data. Strong agreement with manual segmentation and establishment of adult pathology-specific reference values support its use as a standardized framework for three-dimensional foot and ankle deformity assessment.

Indexed as

computational automatic toolboxfoot and anklefoot deformitiesradiographic measurementsthree-dimensional imaging

Identifiers

PMID42621025
PMCPMC13486229

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.