Evidence map›Paper›PMID 35084208›Full record

ArticleThe British journal of radiology2022

Artificial intelligence study on left ventricular function among normal individuals, hypertrophic cardiomyopathy and dilated cardiomyopathy patients using 1.5T cardiac cine MR images obtained by SSFP sequence.

Jiajun Guo, HongFei Lu, Yinyin Chen, Mengsu Zeng, Hang Jin

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

Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

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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

2 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Jiajun GuoDepartment of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Shanghai, China.
HongFei LuDepartment of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Shanghai, China.
Yinyin ChenDepartment of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Shanghai, China.
Mengsu ZengDepartment of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Shanghai, China.
Hang JinDepartment of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Shanghai, China.
Zhongshan Hospital · CNFudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate the performance of a deep learning-based method to automatically quantify left ventricular (LV) function from MR images in different cardiomyopathy.

methodsThis retrospective study included MRI data sets from 2013 to 2020. Data on left ventricular function from patients with hypertrophic cardiomyopathy (HCM), patients with dilated cardiomyopathy (DCM), and healthy participants were analyzed. MRI data from a total of 388 patients were measured manually and automatically.The performance of Convolutional Neural Networks (CNNs) was evaluated based on the manual notes of two experienced observers: (a) LV segmentation accuracy, and (b) LV functional parameter accuracy. Bland-Altman analysis, Receiver operating Characteristic (ROC) curve analysis and Pearson correlation analysis were used to evaluate the consistency between fully automatic and manual diagnosis of HCM and DCM.

resultsThe deep-learning CNN performed best in HCM in evaluating LV function and worst in DCM. Compared with manual analysis, four parameters of LV function in the HCM group showed high correlation (r at least >0.901), and the correlation of DCM in all parameters was weaker than that of HCM, especially EF (r

conclusionAmong different heart diseases, the analysis of cardiac function based on deep-learning CNN may have different performances, with DCM performing the worst and HCM the best and thus, special attention should be paid to DCM patients when assessing LV function through artificial intelligence method. LV function parameter obtained by artificial intelligence method may play an important role in the future AI diagnosis of HCM and DCM. ADVANCES IN KNOWLEDGE: These data for the first time objectively evaluate the performance of a commercially available deep learning-based method in cardiac function evaluation of different cardiomyopathy and point out its advantages and disadvantages in different cardiomyopathy. This work did not attempt to design the algorithm itself, but rather applied an already existing method to a test dataset of clinical data and evaluated the results for a limited number of cardiomyopathy.

Indexed as

CardiomyopathiesCardiomyopathy, DilatedCardiomyopathy, HypertrophicArtificial IntelligenceHumansMagnetic Resonance ImagingRetrospective StudiesVentricular Function, Left

Identifiers

PMID35084208
PMCPMC10993976
OpenAlexW4210389921

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.