Evidence map›Paper›PMID 36721410›Full record

ArticleRadiology. Artificial intelligence2023

Automated Inline Myocardial Segmentation of Joint T1 and T2 Mapping Using Deep Learning.

James P Howard, Kelvin Chow, Liza Chacko, Mariana Fontana, Graham D Cole, Peter Kellman, Hui Xue

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

7 authors.

James P HowardNational Heart and Lung Institute, Imperial College London, Du Cane Rd, B Block, 2nd Floor, Hammersmith Hospital, London W12 0HS, England (J.P.H., G.D.C.); National Amyloidosis Centre, Division of Medicine, University College London, London, England (L.C., M.F.); Cardiovascular MR R&D, Siemens Medical Solutions USA, Chicago, Ill (K.C., L.C., M.F.); and Medical Signal and Image Processing Program, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (P.K., H.X.).ORCID https://orcid.org/0000-0002-9989-6331
Kelvin ChowNational Heart and Lung Institute, Imperial College London, Du Cane Rd, B Block, 2nd Floor, Hammersmith Hospital, London W12 0HS, England (J.P.H., G.D.C.); National Amyloidosis Centre, Division of Medicine, University College London, London, England (L.C., M.F.); Cardiovascular MR R&D, Siemens Medical Solutions USA, Chicago, Ill (K.C., L.C., M.F.); and Medical Signal and Image Processing Program, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (P.K., H.X.).ORCID https://orcid.org/0000-0003-0698-1746
Liza ChackoNational Heart and Lung Institute, Imperial College London, Du Cane Rd, B Block, 2nd Floor, Hammersmith Hospital, London W12 0HS, England (J.P.H., G.D.C.); National Amyloidosis Centre, Division of Medicine, University College London, London, England (L.C., M.F.); Cardiovascular MR R&D, Siemens Medical Solutions USA, Chicago, Ill (K.C., L.C., M.F.); and Medical Signal and Image Processing Program, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (P.K., H.X.).
Mariana FontanaNational Heart and Lung Institute, Imperial College London, Du Cane Rd, B Block, 2nd Floor, Hammersmith Hospital, London W12 0HS, England (J.P.H., G.D.C.); National Amyloidosis Centre, Division of Medicine, University College London, London, England (L.C., M.F.); Cardiovascular MR R&D, Siemens Medical Solutions USA, Chicago, Ill (K.C., L.C., M.F.); and Medical Signal and Image Processing Program, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (P.K., H.X.).
Graham D ColeNational Heart and Lung Institute, Imperial College London, Du Cane Rd, B Block, 2nd Floor, Hammersmith Hospital, London W12 0HS, England (J.P.H., G.D.C.); National Amyloidosis Centre, Division of Medicine, University College London, London, England (L.C., M.F.); Cardiovascular MR R&D, Siemens Medical Solutions USA, Chicago, Ill (K.C., L.C., M.F.); and Medical Signal and Image Processing Program, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (P.K., H.X.).ORCID https://orcid.org/0000-0002-3157-0335
Peter KellmanNational Heart and Lung Institute, Imperial College London, Du Cane Rd, B Block, 2nd Floor, Hammersmith Hospital, London W12 0HS, England (J.P.H., G.D.C.); National Amyloidosis Centre, Division of Medicine, University College London, London, England (L.C., M.F.); Cardiovascular MR R&D, Siemens Medical Solutions USA, Chicago, Ill (K.C., L.C., M.F.); and Medical Signal and Image Processing Program, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (P.K., H.X.).ORCID https://orcid.org/0000-0002-9875-6070
Hui XueNational Heart and Lung Institute, Imperial College London, Du Cane Rd, B Block, 2nd Floor, Hammersmith Hospital, London W12 0HS, England (J.P.H., G.D.C.); National Amyloidosis Centre, Division of Medicine, University College London, London, England (L.C., M.F.); Cardiovascular MR R&D, Siemens Medical Solutions USA, Chicago, Ill (K.C., L.C., M.F.); and Medical Signal and Image Processing Program, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (P.K., H.X.).ORCID https://orcid.org/0000-0002-4561-5530

Funding

Gadgetron Global Network and Artificial Intelligence Powered Cardiac ImagingZIAHL006242 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI KELLMAN, PETER · 2019 to 2023
$2.2M
British Heart Foundation FS/ICRF/22/26039
6 · The paper itself

Abstract

Purpose: To develop an artificial intelligence (AI) solution for automated segmentation and analysis of joint cardiac MRI short-axis T1 and T2 mapping. Materials and Methods: In this retrospective study, a joint T1 and T2 mapping sequence was used to acquire 4240 maps from 807 patients across two hospitals between March and November 2020. Five hundred nine maps from 94 consecutive patients were assigned to a holdout testing set. A convolutional neural network was trained to segment the endocardial and epicardial contours with use of an edge probability estimation approach. Training labels were segmented by an expert cardiologist. Predicted contours were processed to yield mapping values for each of the 16 American Heart Association segments. Network segmentation performance and segment-wise measurements on the testing set were compared with those of two experts on the holdout testing set. The AI model was fully integrated using open-source software to run on MRI scanners. Results: A total of 3899 maps (92%) were deemed artifact-free and suitable for human segmentation. AI segmentation closely matched that of each expert (mean Dice coefficient, 0.82 ± 0.07 [SD] vs expert 1 and 0.86 ± 0.06 vs expert 2) and compared favorably with interexpert agreement (Dice coefficient, 0.84 ± 0.06 for expert 1 vs expert 2). AI-derived segment-wise values for native T1, postcontrast T1, and T2 mapping correlated with expert-derived values ( Conclusion: Automated inline analysis of joint T1 and T2 mapping allows accurate segment-wise tissue characterization, with performance equivalent to that of human experts.

Indexed as

CardiacHeartMRINeural Networks

Identifiers

PMID36721410
PMCPMC9885378

What OpenQuestion holds

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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.