Evidence map›Paper›PMID 38114568›Full record

ArticleEye (London, England)2024

Ultra-wide field and new wide field composite retinal image registration with AI-enabled pipeline and 3D distortion correction algorithm.

Fritz Gerald P Kalaw, Melina Cavichini, Junkang Zhang, Bo Wen, Andrew C Lin, Anna Heinke, Truong Nguyen, Cheolhong An, Dirk-Uwe G Bartsch, Lingyun Cheng and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Eye (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

11 authors.

Fritz Gerald P KalawJacobs Retina Center, University of California, San Diego, CA, USA.ORCID http://orcid.org/0000-0002-3940-2272
Melina CavichiniJacobs Retina Center, University of California, San Diego, CA, USA.
Junkang ZhangDepartment of Electrical and Computer Engineering, University of California, San Diego, CA, USA.
Bo WenDepartment of Electrical and Computer Engineering, University of California, San Diego, CA, USA.
Andrew C LinViterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, CA, USA.
Anna HeinkeJacobs Retina Center, University of California, San Diego, CA, USA.
Truong NguyenDepartment of Electrical and Computer Engineering, University of California, San Diego, CA, USA.
Cheolhong AnDepartment of Electrical and Computer Engineering, University of California, San Diego, CA, USA.
Dirk-Uwe G BartschJacobs Retina Center, University of California, San Diego, CA, USA.
Lingyun ChengJacobs Retina Center, University of California, San Diego, CA, USA.
William R FreemanJacobs Retina Center, University of California, San Diego, CA, USA. wrfreeman@health.ucsd.edu.

Funding

Bridge2AI:Salutogenesis Data Generation ProjectOT2OD032644 · OD · WASHINGTON UNIVERSITY · PI BAXTER, SALLY LIU, CHUTE, CHRISTOPHER G · 2022 to 2025
$32.7M
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 R01 EY016323NEI NIH HHS R01 EY033847NIH HHS OT2 OD032644
6 · The paper itself

Abstract

purposeThis study aimed to compare a new Artificial Intelligence (AI) method to conventional mathematical warping in accurately overlaying peripheral retinal vessels from two different imaging devices: confocal scanning laser ophthalmoscope (cSLO) wide-field images and SLO ultra-wide field images.

methodsImages were captured using the Heidelberg Spectralis 55-degree field-of-view and Optos ultra-wide field. The conventional mathematical warping was performed using Random Sample Consensus-Sample and Consensus sets (RANSAC-SC). This was compared to an AI alignment algorithm based on a one-way forward registration procedure consisting of full Convolutional Neural Networks (CNNs) with Outlier Rejection (OR CNN), as well as an iterative 3D camera pose optimization process (OR CNN + Distortion Correction [DC]). Images were provided in a checkerboard pattern, and peripheral vessels were graded in four quadrants based on alignment to the adjacent box.

resultsA total of 660 boxes were analysed from 55 eyes. Dice scores were compared between the three methods (RANSAC-SC/OR CNN/OR CNN + DC): 0.3341/0.4665/4784 for fold 1-2 and 0.3315/0.4494/4596 for fold 2-1 in composite images. The images composed using the OR CNN + DC have a median rating of 4 (out of 5) versus 2 using RANSAC-SC. The odds of getting a higher grading level are 4.8 times higher using our OR CNN + DC than RANSAC-SC (p < 0.0001).

conclusionPeripheral retinal vessel alignment performed better using our AI algorithm than RANSAC-SC. This may help improve co-localizing retinal anatomy and pathology with our algorithm.

Indexed as

Artificial IntelligenceRetinaAlgorithmsHumansNeural Networks, ComputerRetinal Vessels

Identifiers

PMID38114568
PMCPMC11009222

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.