Evidence map›Paper›PMID 28393769›Full record

ArticleAnnals of cardiac anaesthesia

Artificial intelligence in mitral valve analysis.

Jelliffe Jeganathan, Ziyad Knio, Yannis Amador, Ting Hai, Arash Khamooshian, Robina Matyal, Kamal R Khabbaz, Feroze Mahmood

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of cardiac anaesthesia. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Observational
  2. Article
  3. Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Routine Echocardiography and Artificial Intelligence Solutions.Frontiers in cardiovascular medicine · 2021
    Review
  13. Article
  14. Review
  15. Review
  16. Review
  17. Article
  18. Recursive multiresolution convolutional neural networks for 3D aortic valve annulus planimetry.International journal of computer assisted radiology and surgery · 2020
    Article
  19. Artificial Intelligence in Cardiovascular Medicine.Current treatment options in cardiovascular medicine · 2019
    Review
  20. Review
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

8 authors.

Jelliffe JeganathanDepartment of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Ziyad KnioDepartment of Surgery, Division of Cardiothoracic Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Yannis AmadorDepartment of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Department of Anesthesia, Hospital México, University of Costa Rica, San José, Costa Rica.
Ting HaiDepartment of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Department of Anesthesiology, Peking University People's Hospital, Beijing, China.
Arash KhamooshianDepartment of Cardio-Thoracic Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Robina MatyalDepartment of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Kamal R KhabbazDepartment of Surgery, Division of Cardiothoracic Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Feroze MahmoodDepartment of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEchocardiographic analysis of mitral valve (MV) has become essential for diagnosis and management of patients with MV disease. Currently, the various software used for MV analysis require manual input and are prone to interobserver variability in the measurements.

aimThe aim of this study is to determine the interobserver variability in an automated software that uses artificial intelligence for MV analysis. SETTINGS AND

designRetrospective analysis of intraoperative three-dimensional transesophageal echocardiography data acquired from four patients with normal MV undergoing coronary artery bypass graft surgery in a tertiary hospital. MATERIALS AND

methodsEchocardiographic data were analyzed using the eSie Valve Software (Siemens Healthcare, Mountain View, CA, USA). Three examiners analyzed three end-systolic (ES) frames from each of the four patients. A total of 36 ES frames were analyzed and included in the study. STATISTICAL ANALYSIS: A multiple mixed-effects ANOVA model was constructed to determine if the examiner, the patient, and the loop had a significant effect on the average value of each parameter. A Bonferroni correction was used to correct for multiple comparisons, and P = 0.0083 was considered to be significant.

resultsExaminers did not have an effect on any of the six parameters tested. Patient and loop had an effect on the average parameter value for each of the six parameters as expected (P < 0.0083 for both).

conclusionWe were able to conclude that using automated analysis, it is possible to obtain results with good reproducibility, which only requires minimal user intervention.

Indexed as

Artificial IntelligenceCoronary Artery BypassEchocardiography, Three-DimensionalEchocardiography, TransesophagealHumansImage Processing, Computer-AssistedMitral ValveReproducibility of ResultsRetrospective Studies

Identifiers

PMID28393769
PMCPMC5408514

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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.