Publication

Artificial Intelligence in Mitral Valve Analysis

Jeganathan, J., Knio, Z., Amador, Y., Hai, T., Khamooshian, A., Matyal, R., Khabbaz, K. R. & Mahmood, F., 2017, In : Annals of cardiac anaesthesia. 20, 2, p. 129-134 6 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Jeganathan, J., Knio, Z., Amador, Y., Hai, T., Khamooshian, A., Matyal, R., ... Mahmood, F. (2017). Artificial Intelligence in Mitral Valve Analysis. Annals of cardiac anaesthesia, 20(2), 129-134. https://doi.org/10.4103/aca.ACA_243_16

Author

Jeganathan, Jelliffe ; Knio, Ziyad ; Amador, Yannis ; Hai, Ting ; Khamooshian, Arash ; Matyal, Robina ; Khabbaz, Kamal R. ; Mahmood, Feroze. / Artificial Intelligence in Mitral Valve Analysis. In: Annals of cardiac anaesthesia. 2017 ; Vol. 20, No. 2. pp. 129-134.

Harvard

Jeganathan, J, Knio, Z, Amador, Y, Hai, T, Khamooshian, A, Matyal, R, Khabbaz, KR & Mahmood, F 2017, 'Artificial Intelligence in Mitral Valve Analysis', Annals of cardiac anaesthesia, vol. 20, no. 2, pp. 129-134. https://doi.org/10.4103/aca.ACA_243_16

Standard

Artificial Intelligence in Mitral Valve Analysis. / Jeganathan, Jelliffe; Knio, Ziyad; Amador, Yannis; Hai, Ting; Khamooshian, Arash; Matyal, Robina; Khabbaz, Kamal R.; Mahmood, Feroze.

In: Annals of cardiac anaesthesia, Vol. 20, No. 2, 2017, p. 129-134.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Jeganathan J, Knio Z, Amador Y, Hai T, Khamooshian A, Matyal R et al. Artificial Intelligence in Mitral Valve Analysis. Annals of cardiac anaesthesia. 2017;20(2):129-134. https://doi.org/10.4103/aca.ACA_243_16


BibTeX

@article{8d8540ee28eb46cd91a04082376df3a8,
title = "Artificial Intelligence in Mitral Valve Analysis",
abstract = "Background: Echocardiographic 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. Aim: The aim of this study is to determine the interobserver variability in an automated software that uses artificial intelligence for MV analysis. Settings and Design: Retrospective 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 Methods: Echocardiographic 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. Results: Examiners 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). Conclusion: We were able to conclude that using automated analysis, it is possible to obtain results with good reproducibility, which only requires minimal user intervention.",
keywords = "Artificial intelligence, eSie Valve Software, interobserver variability, mitral valve, mitral valve analysis, ALGORITHMS",
author = "Jelliffe Jeganathan and Ziyad Knio and Yannis Amador and Ting Hai and Arash Khamooshian and Robina Matyal and Khabbaz, {Kamal R.} and Feroze Mahmood",
year = "2017",
doi = "10.4103/aca.ACA_243_16",
language = "English",
volume = "20",
pages = "129--134",
journal = "Annals of cardiac anaesthesia",
issn = "0971-9784",
publisher = "MEDKNOW PUBLICATIONS & MEDIA PVT LTD",
number = "2",

}

RIS

TY - JOUR

T1 - Artificial Intelligence in Mitral Valve Analysis

AU - Jeganathan, Jelliffe

AU - Knio, Ziyad

AU - Amador, Yannis

AU - Hai, Ting

AU - Khamooshian, Arash

AU - Matyal, Robina

AU - Khabbaz, Kamal R.

AU - Mahmood, Feroze

PY - 2017

Y1 - 2017

N2 - Background: Echocardiographic 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. Aim: The aim of this study is to determine the interobserver variability in an automated software that uses artificial intelligence for MV analysis. Settings and Design: Retrospective 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 Methods: Echocardiographic 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. Results: Examiners 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). Conclusion: We were able to conclude that using automated analysis, it is possible to obtain results with good reproducibility, which only requires minimal user intervention.

AB - Background: Echocardiographic 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. Aim: The aim of this study is to determine the interobserver variability in an automated software that uses artificial intelligence for MV analysis. Settings and Design: Retrospective 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 Methods: Echocardiographic 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. Results: Examiners 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). Conclusion: We were able to conclude that using automated analysis, it is possible to obtain results with good reproducibility, which only requires minimal user intervention.

KW - Artificial intelligence

KW - eSie Valve Software

KW - interobserver variability

KW - mitral valve

KW - mitral valve analysis

KW - ALGORITHMS

U2 - 10.4103/aca.ACA_243_16

DO - 10.4103/aca.ACA_243_16

M3 - Article

VL - 20

SP - 129

EP - 134

JO - Annals of cardiac anaesthesia

JF - Annals of cardiac anaesthesia

SN - 0971-9784

IS - 2

ER -

ID: 67291417