Evidence map›Paper›PMID 36725098›Full record

ArticleBMJ open2023

Safety and efficacy of an artificial intelligence-enabled decision tool for treatment decisions in neovascular age-related macular degeneration and an exploration of clinical pathway integration and implementation: protocol for a multi-methods validation study.

Henry David Jeffry Hogg, Katie Brittain, Dawn Teare, James Talks, Konstantinos Balaskas, Pearse Keane, Gregory Maniatopoulos

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 11% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Artificial intelligence in ophthalmology.International journal of ophthalmology · 2023
    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

7 authors at 4 institutions in 2 countries.

Henry David Jeffry HoggPopulation Health Sciences Institute, University of Newcastle upon Tyne, Newcastle upon Tyne, UK jeffry.hogg@ncl.ac.uk.ORCID 0000-0001-8044-7790
Katie BrittainPopulation Health Sciences Institute, University of Newcastle upon Tyne, Newcastle upon Tyne, UK.
Dawn TearePopulation Health Sciences Institute, University of Newcastle upon Tyne, Newcastle upon Tyne, UK.
James TalksNewcastle Eye Centre, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK.
Konstantinos BalaskasInstitute of Ophthalmology, University College London, London, UK.
Pearse KeaneInstitute of Ophthalmology, University College London, London, UK.
Gregory ManiatopoulosPopulation Health Sciences Institute, University of Newcastle upon Tyne, Newcastle upon Tyne, UK.
Moorfields Eye Hospital NHS Foundation Trust · GBNewcastle University · GBNewcastle upon Tyne Hospitals NHS Foundation Trust · GBNorthumbria University · GB

Funding

Medical Research Council MC_PC_19005Medical Research Council MR/T019050/1
6 · The paper itself

Abstract

introductionNeovascular age-related macular degeneration (nAMD) management is one of the largest single-disease contributors to hospital outpatient appointments. Partial automation of nAMD treatment decisions could reduce demands on clinician time. Established artificial intelligence (AI)-enabled retinal imaging analysis tools, could be applied to this use-case, but are not yet validated for it. A primary qualitative investigation of stakeholder perceptions of such an AI-enabled decision tool is also absent. This multi-methods study aims to establish the safety and efficacy of an AI-enabled decision tool for nAMD treatment decisions and understand where on the clinical pathway it could sit and what factors are likely to influence its implementation. METHODS AND ANALYSIS: Single-centre retrospective imaging and clinical data will be collected from nAMD clinic visits at a National Health Service (NHS) teaching hospital ophthalmology service, including judgements of nAMD disease stability or activity made in real-world consultant-led-care. Dataset size will be set by a power calculation using the first 127 randomly sampled eligible clinic visits. An AI-enabled retinal segmentation tool and a rule-based decision tree will independently analyse imaging data to report nAMD stability or activity for each of these clinic visits. Independently, an external reading centre will receive both clinical and imaging data to generate an enhanced reference standard for each clinic visit. The non-inferiority of the relative negative predictive value of AI-enabled reports on disease activity relative to consultant-led-care judgements will then be tested. In parallel, approximately 40 semi-structured interviews will be conducted with key nAMD service stakeholders, including patients. Transcripts will be coded using a theoretical framework and thematic analysis will follow. ETHICS AND DISSEMINATION: NHS Research Ethics Committee and UK Health Research Authority approvals are in place (21/NW/0138). Informed consent is planned for interview participants only. Written and oral dissemination is planned to public, clinical, academic and commercial stakeholders.

Indexed as

Angiogenesis InhibitorsMacular DegenerationArtificial IntelligenceCritical PathwaysHumansRetrospective StudiesState MedicineAngiogenesis InhibitorsHealth informaticsMedical retinaOrganisation of health servicesQUALITATIVE RESEARCHSTATISTICS & RESEARCH METHODS

Identifiers

PMID36725098
PMCPMC9896175
OpenAlexW4318833964

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.