Evidence map›Paper›PMID 33173612›Full record

ArticleTranslational vision science & technology2020

Artificial Intelligence for Automated Overlay of Fundus Camera and Scanning Laser Ophthalmoscope Images.

Melina Cavichini, Cheolhong An, Dirk-Uwe G Bartsch, Mahima Jhingan, Manuel J Amador-Patarroyo, Christopher P Long, Junkang Zhang, Yiqian Wang, Alison X Chan, Samantha Madala and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. 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.

Melina CavichiniJacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, CA, USA.
Cheolhong AnDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
Dirk-Uwe G BartschJacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, CA, USA.
Mahima JhinganJacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, CA, USA.
Manuel J Amador-PatarroyoJacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, CA, USA.
Christopher P LongUniversity of California San Diego School of Medicine, La Jolla, CA, USA.
Junkang ZhangDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
Yiqian WangDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
Alison X ChanUniversity of California San Diego School of Medicine, La Jolla, CA, USA.
Samantha MadalaUniversity of California San Diego School of Medicine, La Jolla, CA, USA.
Truong NguyenDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
William R FreemanJacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, CA, USA.

Funding

Vision BiostatisticsP30EY022589 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Derek Stuart Welsbie · 2012 to 2026
$10.3M
Mechanistic-Based Non-Invasive Assessment of Retinal Damage in HAART EraR01EY016323 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BARTSCH, DIRK-UWE G · 2006 to 2020
$5.0M
SCH: Multimodal Retina Image Alignment and ApplicationsR01EY033847 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI AN, CHEOLHONG, BARTSCH, DIRK-UWE G · 2021 to 2024
$1.2M
NEI NIH HHS P30 EY022589NEI NIH HHS R01 EY016323NEI NIH HHS R01 EY033847
6 · The paper itself

Abstract

Purpose: The purpose of this study was to evaluate the ability to align two types of retinal images taken on different platforms; color fundus (CF) photographs and infrared scanning laser ophthalmoscope (IR SLO) images using mathematical warping and artificial intelligence (AI). Methods: We collected 109 matched pairs of CF and IR SLO images. An AI algorithm utilizing two separate networks was developed. A style transfer network (STN) was used to segment vessel structures. A registration network was used to align the segmented images to each. Neither network used a ground truth dataset. A conventional image warping algorithm was used as a control. Software displayed image pairs as a 5 × 5 checkerboard grid composed of alternating subimages. This technique permitted vessel alignment determination by human observers and 5 masked graders evaluated alignment by the AI and conventional warping in 25 fields for each image. Results: Our new AI method was superior to conventional warping at generating vessel alignment as judged by masked human graders ( Conclusions: AI permitted a more accurate overlay of CF and IR SLO images than conventional mathematical warping. This is a first step toward developing an AI that could allow overlay of all types of fundus images by utilizing vascular landmarks. Translational Relevance: The ability to align and overlay imaging data from multiple instruments and manufacturers will permit better analysis of this complex data helping understand disease and predict treatment.

Indexed as

Artificial IntelligenceOphthalmoscopesFluorescein AngiographyFundus OculiHumansLasersartificial intelligencediagnosismultimodal imagesretina, imaging

Identifiers

PMID33173612
PMCPMC7594596

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.