A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographers
Abstract Background Speckle tracking echocardiography (STE) provides quantification of left ventricular (LV) deformation and is useful in the assessment of LV function. STE is increasingly being used clinically, and every effort to simplify and standardize STE is important. Manual outlining of regio...
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BMC
2024-11-01
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| Online Access: | https://doi.org/10.1186/s12880-024-01470-7 |
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| author | Magnus Rogstadkjernet Sigurd Z. Zha Lars G. Klæboe Camilla K. Larsen John M. Aalen Esther Scheirlynck Bjørn-Jostein Singstad Steven Droogmans Bernard Cosyns Otto A. Smiseth Kristina H. Haugaa Thor Edvardsen Eigil Samset Pål H. Brekke |
| author_facet | Magnus Rogstadkjernet Sigurd Z. Zha Lars G. Klæboe Camilla K. Larsen John M. Aalen Esther Scheirlynck Bjørn-Jostein Singstad Steven Droogmans Bernard Cosyns Otto A. Smiseth Kristina H. Haugaa Thor Edvardsen Eigil Samset Pål H. Brekke |
| author_sort | Magnus Rogstadkjernet |
| collection | DOAJ |
| description | Abstract Background Speckle tracking echocardiography (STE) provides quantification of left ventricular (LV) deformation and is useful in the assessment of LV function. STE is increasingly being used clinically, and every effort to simplify and standardize STE is important. Manual outlining of regions of interest (ROIs) is labor intensive and may influence assessment of strain values. Purpose We hypothesized that a deep learning (DL) model, trained on clinical echocardiographic exams, can be combined with a readily available echocardiographic analysis software, to automate strain calculation with comparable fidelity to trained cardiologists. Methods Data consisted of still frame echocardiographic images with cardiologist-defined ROIs from 672 clinical echocardiographic exams from a university hospital outpatient clinic. Exams included patients with ischemic heart disease, heart failure, valvular disease, and conduction abnormalities, and some healthy subjects. An EfficientNetB1-based architecture was employed, and different techniques and properties including data set size, data quality, augmentations, and transfer learning were evaluated. DL predicted ROIs were reintroduced into commercially available echocardiographic analysis software to automatically calculate strain values. Results DL-automated strain calculations had an average absolute difference of 0.75 (95% CI 0.58–0.92) for global longitudinal strain (GLS), and 1.16 (95% CI 1.03–1.29) for single-projection longitudinal strain (LS), compared to operators. A Bland–Altman plot revealed no obvious bias, though there were fewer outliers in the lower average LS ranges. Techniques and data properties yielded no significant increase/decrease in performance. Conclusion The study demonstrates that DL-assisted, automated strain measurements are feasible, and provide results within interobserver variation. Employing DL in echocardiographic analyses could further facilitate adoption of STE parameters in clinical practice and research, and improve reproducibility. |
| format | Article |
| id | doaj-art-5d494022ecb341ab8768554221743f05 |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-5d494022ecb341ab8768554221743f052024-11-17T12:53:59ZengBMCBMC Medical Imaging1471-23422024-11-0124111010.1186/s12880-024-01470-7A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographersMagnus Rogstadkjernet0Sigurd Z. Zha1Lars G. Klæboe2Camilla K. Larsen3John M. Aalen4Esther Scheirlynck5Bjørn-Jostein Singstad6Steven Droogmans7Bernard Cosyns8Otto A. Smiseth9Kristina H. Haugaa10Thor Edvardsen11Eigil Samset12Pål H. Brekke13Institute for Clinical Medicine, University of OsloInstitute for Clinical Medicine, University of OsloProCardio Center for Innovation, Department of Cardiology, Oslo University HospitalInstitute for Surgical Research, Oslo University Hospital and University of OsloInstitute for Surgical Research, Oslo University Hospital and University of OsloCentrum Voor Hart-en Vaatziekten, Universitair Ziekenhuis Brussel-Vrije Universiteit BrusselProCardio Center for Innovation, Department of Cardiology, Oslo University HospitalDepartment of Cardiology, Universitair Ziekenhuis BrusselCentrum Voor Hart- en Vaatziekten, Universitair Ziekenhuis BrusselInstitute for Clinical Medicine, University of OsloProCardio Center for Innovation, Department of Cardiology, Oslo University HospitalDepartment of Cardiology, Oslo University HospitalProCardio Center for Innovation, Department of Cardiology, Oslo University HospitalDepartment of Cardiology, Oslo University HospitalAbstract Background Speckle tracking echocardiography (STE) provides quantification of left ventricular (LV) deformation and is useful in the assessment of LV function. STE is increasingly being used clinically, and every effort to simplify and standardize STE is important. Manual outlining of regions of interest (ROIs) is labor intensive and may influence assessment of strain values. Purpose We hypothesized that a deep learning (DL) model, trained on clinical echocardiographic exams, can be combined with a readily available echocardiographic analysis software, to automate strain calculation with comparable fidelity to trained cardiologists. Methods Data consisted of still frame echocardiographic images with cardiologist-defined ROIs from 672 clinical echocardiographic exams from a university hospital outpatient clinic. Exams included patients with ischemic heart disease, heart failure, valvular disease, and conduction abnormalities, and some healthy subjects. An EfficientNetB1-based architecture was employed, and different techniques and properties including data set size, data quality, augmentations, and transfer learning were evaluated. DL predicted ROIs were reintroduced into commercially available echocardiographic analysis software to automatically calculate strain values. Results DL-automated strain calculations had an average absolute difference of 0.75 (95% CI 0.58–0.92) for global longitudinal strain (GLS), and 1.16 (95% CI 1.03–1.29) for single-projection longitudinal strain (LS), compared to operators. A Bland–Altman plot revealed no obvious bias, though there were fewer outliers in the lower average LS ranges. Techniques and data properties yielded no significant increase/decrease in performance. Conclusion The study demonstrates that DL-assisted, automated strain measurements are feasible, and provide results within interobserver variation. Employing DL in echocardiographic analyses could further facilitate adoption of STE parameters in clinical practice and research, and improve reproducibility.https://doi.org/10.1186/s12880-024-01470-7Speckle-tracking echocardiographyStrain rate imagingDeep learningArtificial intelligenceAutomation |
| spellingShingle | Magnus Rogstadkjernet Sigurd Z. Zha Lars G. Klæboe Camilla K. Larsen John M. Aalen Esther Scheirlynck Bjørn-Jostein Singstad Steven Droogmans Bernard Cosyns Otto A. Smiseth Kristina H. Haugaa Thor Edvardsen Eigil Samset Pål H. Brekke A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographers BMC Medical Imaging Speckle-tracking echocardiography Strain rate imaging Deep learning Artificial intelligence Automation |
| title | A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographers |
| title_full | A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographers |
| title_fullStr | A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographers |
| title_full_unstemmed | A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographers |
| title_short | A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographers |
| title_sort | deep learning based method for left ventricular strain measurements repeatability and accuracy compared to experienced echocardiographers |
| topic | Speckle-tracking echocardiography Strain rate imaging Deep learning Artificial intelligence Automation |
| url | https://doi.org/10.1186/s12880-024-01470-7 |
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