1
|
Machine learning based quantification of ejection and filling parameters by fully automated dynamic measurement of left ventricular volumes from cardiac magnetic resonance images. Magn Reson Imaging 2019; 67:28-32. [PMID: 31838116 DOI: 10.1016/j.mri.2019.12.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 11/13/2019] [Accepted: 12/07/2019] [Indexed: 12/19/2022]
Abstract
BACKGROUND Although analysis of cardiac magnetic resonance (CMR) images provides accurate and reproducible measurements of left ventricular (LV) volumes, these measurements are usually not performed throughout the cardiac cycle because of lack of tools that would allow such analysis within a reasonable timeframe. A fully-automated machine-learning (ML) algorithm was recently developed to automatically generate LV volume-time curves. Our aim was to validate ejection and filling parameters calculated from these curves using conventional analysis as a reference. METHODS We studied 21 patients undergoing clinical CMR examinations. LV volume-time curves were obtained using the ML-based algorithm (Neosoft), and independently using slice-by-slice, frame-by-frame manual tracing of the endocardial boundaries. Ejection and filling parameters derived from these curves were compared between the two techniques. For each parameter, Bland-Altman bias and limits of agreement (LOA) were expressed in percent of the mean measured value. RESULTS Time-volume curves were generated using the automated ML analysis within 2.5 ± 0.5 min, considerably faster than the manual analysis (43 ± 14 min per patient, including ~10 slices with 25-32 frames per slice). Time-volume curves were similar between the two techniques in magnitude and shape. Size and function parameters extracted from these curves showed no significant inter-technique differences, reflected by high correlations, small biases (<10%) and mostly reasonably narrow LOA. CONCLUSION ML software for dynamic LV volume measurement allows fast and accurate, fully automated analysis of ejection and filling parameters, compared to manual tracing based analysis. The ability to quickly evaluate time-volume curves is important for a more comprehensive evaluation of the patient's cardiac function.
Collapse
|
2
|
Narang A, Mor-Avi V, Prado A, Volpato V, Prater D, Tamborini G, Fusini L, Pepi M, Goyal N, Addetia K, Gonçalves A, Patel AR, Lang RM. Machine learning based automated dynamic quantification of left heart chamber volumes. Eur Heart J Cardiovasc Imaging 2019; 20:541-549. [PMID: 30304500 PMCID: PMC6933871 DOI: 10.1093/ehjci/jey137] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 09/13/2018] [Indexed: 12/19/2022] Open
Abstract
AIMS Studies have demonstrated the ability of a new automated algorithm for volumetric analysis of 3D echocardiographic (3DE) datasets to provide accurate and reproducible measurements of left ventricular and left atrial (LV, LA) volumes at end-systole and end-diastole. Recently, this methodology was expanded using a machine learning (ML) approach to automatically measure chamber volumes throughout the cardiac cycle, resulting in LV and LA volume-time curves. We aimed to validate ejection and filling parameters obtained from these curves by comparing them to independent well-validated reference techniques. METHODS AND RESULTS We studied 20 patients referred for cardiac magnetic resonance (CMR) examinations, who underwent 3DE imaging the same day. Volume-time curves were obtained for both LV and LA chambers using the ML algorithm (Philips HeartModel), and independently conventional 3DE volumetric analysis (TomTec), and CMR images (slice-by-slice, frame-by-frame manual tracing). Automatically derived LV and LA volumes and ejection/filling parameters were compared against both reference techniques. Minor manual correction of the automatically detected LV and LA borders was needed in 4/20 and 5/20 cases, respectively. Time required to generate volume-time curves was 35 ± 17 s using ML algorithm, 3.6 ± 0.9 min using conventional 3DE analysis, and 96 ± 14 min using CMR. Volume-time curves obtained by all three techniques were similar in shape and magnitude. In both comparisons, ejection/filling parameters showed no significant inter-technique differences. Bland-Altman analysis confirmed small biases, despite wide limits of agreement. CONCLUSION The automated ML algorithm can quickly measure dynamic LV and LA volumes and accurately analyse ejection/filling parameters. Incorporation of this algorithm into the clinical workflow may increase the utilization of 3DE imaging.
Collapse
Affiliation(s)
- Akhil Narang
- Department of Medicine, University of Chicago Medical Center, 5758 South Maryland Ave, MC 9067 Room 5513, Chicago, IL, USA
| | - Victor Mor-Avi
- Department of Medicine, University of Chicago Medical Center, 5758 South Maryland Ave, MC 9067 Room 5513, Chicago, IL, USA
| | - Aldo Prado
- Centro Privado de Cardiologia, Yerba Buena, Virgen de la Merced 550, Tucumán, Argentina
| | - Valentina Volpato
- Department of Medicine, University of Chicago Medical Center, 5758 South Maryland Ave, MC 9067 Room 5513, Chicago, IL, USA
- Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Via Parea 4, Milan, Italy
| | - David Prater
- Philips Healthcare, 3000 Minuteman Road, Andover, MA, USA
| | - Gloria Tamborini
- Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Via Parea 4, Milan, Italy
| | - Laura Fusini
- Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Via Parea 4, Milan, Italy
| | - Mauro Pepi
- Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Via Parea 4, Milan, Italy
| | - Neha Goyal
- Department of Medicine, University of Chicago Medical Center, 5758 South Maryland Ave, MC 9067 Room 5513, Chicago, IL, USA
| | - Karima Addetia
- Department of Medicine, University of Chicago Medical Center, 5758 South Maryland Ave, MC 9067 Room 5513, Chicago, IL, USA
| | | | - Amit R Patel
- Department of Medicine, University of Chicago Medical Center, 5758 South Maryland Ave, MC 9067 Room 5513, Chicago, IL, USA
| | - Roberto M Lang
- Department of Medicine, University of Chicago Medical Center, 5758 South Maryland Ave, MC 9067 Room 5513, Chicago, IL, USA
| |
Collapse
|