Evidence map›Paper›PMID 32424211›Full record

ArticleNature medicine2020

Predicting conversion to wet age-related macular degeneration using deep learning.

Jason Yim, Reena Chopra, Terry Spitz, Jim Winkens, Annette Obika, Christopher Kelly, Harry Askham, Marko Lukic, Josef Huemer, Katrin Fasler and 15 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 139 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
139citing papers in PubMed, 6 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

139 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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79 more citing papers are in PubMed but not listed here.

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

25 authors.

Jason Yim *DeepMind, London, UK.
Reena Chopra *DeepMind, London, UK.ORCID http://orcid.org/0000-0002-4264-8329
Terry SpitzGoogle Health, London, UK.ORCID http://orcid.org/0000-0002-9791-3767
Jim WinkensGoogle Health, London, UK.
Annette ObikaDeepMind, London, UK.
Christopher KellyGoogle Health, London, UK.ORCID http://orcid.org/0000-0002-1246-844X
Harry AskhamGoogle Health, London, UK.
Marko LukicNIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK.ORCID http://orcid.org/0000-0002-7636-8368
Josef HuemerNIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK.
Katrin FaslerNIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK.
Gabriella MoraesNIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK.
Clemens MeyerDeepMind, London, UK.
Marc WilsonGoogle Health, London, UK.
Jonathan DixonGoogle Health, London, UK.
Cian HughesGoogle Health, London, UK.ORCID http://orcid.org/0000-0001-6901-0985
Geraint ReesUniversity College London, London, UK.ORCID http://orcid.org/0000-0002-9623-7007
Peng T KhawNIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK.ORCID http://orcid.org/0000-0002-8087-2268
Alan KarthikesalingamGoogle Health, London, UK.
Dominic KingGoogle Health, London, UK.
Demis HassabisDeepMind, London, UK.
Mustafa SuleymanDeepMind, London, UK.
Trevor BackDeepMind, London, UK.
Joseph R LedsamDeepMind, London, UK. jledsam@google.com.ORCID http://orcid.org/0000-0001-9917-7196
Pearse A KeaneNIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, UK. pearse.keane1@nhs.net.ORCID http://orcid.org/0000-0002-9239-745X
Jeffrey De FauwDeepMind, London, UK. defauw@google.com.ORCID http://orcid.org/0000-0001-6971-5678

Funding

Department of Health CS-2014-14-023Department of Health II-C2-0713-20004Department of Health NIHR-CS-2014-14-023Medical Research Council MC_PC_19005Medical Research Council MR/T019050/1
6 · The paper itself

Abstract

Progression to exudative 'wet' age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the second eye. By combining models based on three-dimensional (3D) optical coherence tomography images and corresponding automatic tissue maps, our system predicts conversion to exAMD within a clinically actionable 6-month time window, achieving a per-volumetric-scan sensitivity of 80% at 55% specificity, and 34% sensitivity at 90% specificity. This level of performance corresponds to true positives in 78% and 41% of individual eyes, and false positives in 56% and 17% of individual eyes at the high sensitivity and high specificity points, respectively. Moreover, we show that automatic tissue segmentation can identify anatomical changes before conversion and high-risk subgroups. This AI system overcomes substantial interobserver variability in expert predictions, performing better than five out of six experts, and demonstrates the potential of using AI to predict disease progression.

Indexed as

Deep LearningTomography, Optical CoherenceAgedAged, 80 and overDisease ProgressionEarly DiagnosisEarly Medical InterventionFemaleGeographic AtrophyHumansImaging, Three-DimensionalMacular DegenerationMalePrognosisWet Macular Degeneration

Identifiers

PMID32424211

What OpenQuestion holds

Textmetadata
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.