Evidence map›Paper›PMID 40917265›Full record

ArticleOphthalmology science

Development and Validation of a Semiautomated Tool for Measuring Periorbital Distances.

Jeffrey C Peterson, George R Nahass, Claudia Lasalle, Deanna C Bradley, David Wu, Isabella Zorra, Alvin Nguyen, Akriti Choudhary, Kevin Heinze, Chad A Purnell and 3 more

Abstract read
In one paragraph

Article in Ophthalmology science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

13 authors.

Jeffrey C PetersonIllinois Eye and Ear Infirmary, Department of Ophthalmology, University of Illinois at Chicago, Chicago, Illinois.
George R NahassIllinois Eye and Ear Infirmary, Department of Ophthalmology, University of Illinois at Chicago, Chicago, Illinois.
Claudia LasalleCollege of Medicine, University of Illinois at Chicago, Chicago, Illinois.
Deanna C BradleyCollege of Medicine, University of Illinois at Chicago, Chicago, Illinois.
David WuCollege of Medicine, University of Illinois at Chicago, Chicago, Illinois.
Isabella ZorraCollege of Medicine, University of Illinois at Chicago, Chicago, Illinois.
Alvin NguyenIllinois Eye and Ear Infirmary, Department of Ophthalmology, University of Illinois at Chicago, Chicago, Illinois.
Akriti ChoudharyDepartment of Surgery, University of Illinois at Chicago, Chicago, Illinois.
Kevin HeinzeIsrael Englander Department of Ophthalmology, Weill Cornell University, New York, New York.
Chad A PurnellDepartment of Surgery, University of Illinois at Chicago, Chicago, Illinois.
Pete SetabutrIllinois Eye and Ear Infirmary, Department of Ophthalmology, University of Illinois at Chicago, Chicago, Illinois.
Darvin YiIllinois Eye and Ear Infirmary, Department of Ophthalmology, University of Illinois at Chicago, Chicago, Illinois.
Ann Q TranIllinois Eye and Ear Infirmary, Department of Ophthalmology, University of Illinois at Chicago, Chicago, Illinois.

Funding

Translational Core for Therapeutic and Diagnostic DevelopmentP30EY001792 · NEI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI AAKALU, VINAY · 1985 to 2025
$14.8M
NEI NIH HHS P30 EY001792
6 · The paper itself

Abstract

Purpose: To validate a custom FIJI (ImageJ) program for more reproducible, faster curvilinear periorbital measurements, as compared with 2 custom artificial intelligence-based tools. Design: Combined technical validation and method comparison study. Subjects: Front-facing photographs of 45 cleft palate syndromic patients. Methods: A FIJI (ImageJ)-based macro script tool, OrbitJ (semiautomated), was developed, requiring 15 user input steps to generate 38 measurements per photo. The user outlines the irises to set image scale and then selects points along the lid margins and brow lines. Linear interpolation and fourth-degree polynomial fit lines were used to generate periorbital measurements. This tool was compared against our previously developed deep learning algorithm for periorbital measurements, OrbitMap (automated), another open-source algorithm PeriOrbitAI (automated), and against manual measurements. Four human graders measured 45 photos once both manually and with OrbitJ. Intrarater and interrater measurements were performed with 10 photos manually, 3 in triplicate manually and 5 in triplicate with OrbitJ. Fourteen manual measurements were performed: time per image, iris diameter, margin reflex distance (MRD) 1 and 2, inferior scleral show (ISS), medial, central, and lateral brow height, canthal tilt, vertical dystopia, interpupillary distance, and inner and outer canthal distance (OCD). Main Outcome Measures: The mean absolute error, reliability, bias, and Pearson correlation of periorbital measurements. Results: Analysis was successful in all 45 images for all methods except PeriOrbitAI, which failed on 6 images. For manual and semiautomated intra- and interrater measurements, reliability was considered moderate or better (intraclass correlation coefficient [ICC] >0.5) for all measurements excluding elapsed time. Manual interrater mean absolute error was <1 mm all measures except OCD. Reliability and correlation were high (ICC, Pearson correlation coefficient >0.8) between all OrbitJ and manual measurements. Comparing OrbitMap to manual measurements, ICC and Pearson correlation coefficient were >0.5 except for ISS, borderline for OCD (ICC = 0.51). Reliability between PeriOrbitAI and manual measurements was low (ICC <0.5) except for MRD2 and OCD, and correlation was moderate (Pearson correlation coefficient = 0.49-0.75). The mean analysis time per image was 13.4 ± 4 minutes (manual measurements), 5.4 ± 1.9 minutes (semiautomated OrbitJ), 10.71 ± 1.65 seconds (automated PeriOrbitAI), and 1.45 ± 0.15 seconds (automated OrbitMap) ( Conclusions: Compared with manual measurements, all semiautomated OrbitJ measurements and most automated OrbitMap measurements were reliable. Notably, only 2 PeriOrbitAI measurements were reliable. All methods demonstrated significant time savings over manual measurements. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Indexed as

Artificial intelligenceBrow heightEyelid positionOculoplasticsPeriorbital measurements

Identifiers

PMID40917265
PMCPMC12410191

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.