Evidence map›Paper›PMID 37102206›Full record

ArticleClinical & experimental ophthalmology2023

Efficacy and accuracy of artificial intelligence to overlay multimodal images from different optical instruments in patients with retinitis pigmentosa.

Shaden H Yassin, Yiqian Wang, William R Freeman, Anna Heinke, Evan Walker, Truong Nguyen, Dirk-Uwe G Bartsch, Cheolhong An, Shyamanga Borooah

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical & experimental ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
3.7field-weighted citation impact, top 7% of its field
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

3 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
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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

9 authors at 1 institution in 1 country.

Shaden H YassinJacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, USA.ORCID 0000-0003-3156-4573
Yiqian WangDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA.
William R FreemanJacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, USA.
Anna HeinkeJacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, USA.
Evan WalkerJacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, USA.
Truong NguyenDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA.
Dirk-Uwe G BartschJacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, USA.
Cheolhong AnDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA.
Shyamanga BorooahJacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, USA.
University of California San Diego · US

Funding

Vision BiostatisticsP30EY022589 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Radha Ayyagari · 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
Foundation Fighting BlindnessNEI NIH HHS P30 EY022589NEI NIH HHS P30EY022589NEI NIH HHS R01 EY016323NEI NIH HHS R01EY016323NEI NIH HHS R01 EY033847NEI NIH HHS R01EY033847
6 · The paper itself

Abstract

backgroundRetinitis pigmentosa (RP) represents a group of progressive, genetically heterogenous blinding diseases. Recently, relationships between measures of retinal function and structure are needed to help identify outcome measures or biomarkers for clinical trials. The ability to align retinal multimodal images, taken on different platforms, will allow better understanding of this relationship. We investigate the efficacy of artificial intelligence (AI) in overlaying different multimodal retinal images in RP patients.

methodsWe overlayed infrared images from microperimetry on near-infra-red images from scanning laser ophthalmoscope and spectral domain optical coherence tomography in RP patients using manual alignment and AI. The AI adopted a two-step framework and was trained on a separate dataset. Manual alignment was performed using in-house software that allowed labelling of six key points located at vessel bifurcations. Manual overlay was considered successful if the distance between same key points on the overlayed images was ≤1/2°.

resultsFifty-seven eyes of 32 patients were included in the analysis. AI was significantly more accurate and successful in aligning images compared to manual alignment as confirmed by linear mixed-effects modelling (p < 0.001). A receiver operating characteristic analysis, used to compute the area under the curve of the AI (0.991) and manual (0.835) Dice coefficients in relation to their respective 'truth' values, found AI significantly more accurate in the overlay (p < 0.001).

conclusionAI was significantly more accurate than manual alignment in overlaying multimodal retinal imaging in RP patients and showed the potential to use AI algorithms for future multimodal clinical and research applications.

Indexed as

Artificial IntelligenceRetinitis PigmentosaHumansRetinaTomography, Optical CoherenceVisual Acuityartificial intelligencemicroperimetryretinitis pigmentosaspectral domain optical coherence tomographystructure-function

Identifiers

PMID37102206
PMCPMC10330794
OpenAlexW4367173948

What OpenQuestion 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 OpenQuestion graph.