Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance
Background Global longitudinal shortening (GL‐Shortening) and the mitral annular plane systolic excursion (MAPSE) are known markers in heart failure patients, but measurement may be subjective and less frequently reported because of the lack of automated analysis. Therefore, a validated, automated a...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-02-01
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| Series: | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| Subjects: | |
| Online Access: | https://www.ahajournals.org/doi/10.1161/JAHA.121.023849 |
| _version_ | 1797859018023108608 |
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| author | Hui Xue Jessica Artico Rhodri H. Davies Robert Adam Abhishek Shetye João B. Augusto Anish Bhuva Fredrika Fröjdh Timothy C. Wong Miho Fukui João L. Cavalcante Thomas A. Treibel Charlotte Manisty Marianna Fontana Martin Ugander James C. Moon Erik B. Schelbert Peter Kellman |
| author_facet | Hui Xue Jessica Artico Rhodri H. Davies Robert Adam Abhishek Shetye João B. Augusto Anish Bhuva Fredrika Fröjdh Timothy C. Wong Miho Fukui João L. Cavalcante Thomas A. Treibel Charlotte Manisty Marianna Fontana Martin Ugander James C. Moon Erik B. Schelbert Peter Kellman |
| author_sort | Hui Xue |
| collection | DOAJ |
| description | Background Global longitudinal shortening (GL‐Shortening) and the mitral annular plane systolic excursion (MAPSE) are known markers in heart failure patients, but measurement may be subjective and less frequently reported because of the lack of automated analysis. Therefore, a validated, automated artificial intelligence (AI) solution can be of strong clinical interest. Methods and Results The model was implemented on cardiac magnetic resonance scanners with automated in‐line processing. Reproducibility was evaluated in a scan–rescan data set (n=160 patients). The prognostic association with adverse events (death or hospitalization for heart failure) was evaluated in a large patient cohort (n=1572) and compared with feature tracking global longitudinal strain measured manually by experts. Automated processing took ≈1.1 seconds for a typical case. On the scan–rescan data set, the model exceeded the precision of human expert (coefficient of variation 7.2% versus 11.1% for GL‐Shortening, P=0.0024; 6.5% versus 9.1% for MAPSE, P=0.0124). The minimal detectable change at 90% power was 2.53 percentage points for GL‐Shortening and 1.84 mm for MAPSE. AI GL‐Shortening correlated well with manual global longitudinal strain (R2=0.85). AI MAPSE had the strongest association with outcomes (χ2, 255; hazard ratio [HR], 2.5 [95% CI, 2.2–2.8]), compared with AI GL‐Shortening (χ2, 197; HR, 2.1 [95% CI,1.9–2.4]), manual global longitudinal strain (χ2, 192; HR, 2.1 [95% CI, 1.9–2.3]), and left ventricular ejection fraction (χ2, 147; HR, 1.8 [95% CI, 1.6–1.9]), with P<0.001 for all. Conclusions Automated in‐line AI‐measured MAPSE and GL‐Shortening can deliver immediate and highly reproducible results during cardiac magnetic resonance scanning. These results have strong associations with adverse outcomes that exceed those of global longitudinal strain and left ventricular ejection fraction. |
| first_indexed | 2024-04-09T21:22:42Z |
| format | Article |
| id | doaj.art-6691a60d3ae84678a9f6720c72f51bd0 |
| institution | Directory Open Access Journal |
| issn | 2047-9980 |
| language | English |
| last_indexed | 2024-04-09T21:22:42Z |
| publishDate | 2022-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| spelling | doaj.art-6691a60d3ae84678a9f6720c72f51bd02023-03-28T04:20:06ZengWileyJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease2047-99802022-02-0111410.1161/JAHA.121.023849Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic SignificanceHui Xue0Jessica Artico1Rhodri H. Davies2Robert Adam3Abhishek Shetye4João B. Augusto5Anish Bhuva6Fredrika Fröjdh7Timothy C. Wong8Miho Fukui9João L. Cavalcante10Thomas A. Treibel11Charlotte Manisty12Marianna Fontana13Martin Ugander14James C. Moon15Erik B. Schelbert16Peter Kellman17National Heart, Lung, and Blood InstituteNational Institutes of Health Bethesda MDBarts Heart CentreBarts Health NHS Trust London United KingdomBarts Heart CentreBarts Health NHS Trust London United KingdomBarts Heart CentreBarts Health NHS Trust London United KingdomBarts Heart CentreBarts Health NHS Trust London United KingdomBarts Heart CentreBarts Health NHS Trust London United KingdomBarts Heart CentreBarts Health NHS Trust London United KingdomDepartment of Clinical Physiology Karolinska University Hospital, and Karolinska Institute Stockholm SwedenUPMC Cardiovascular Magnetic Resonance CenterUPMC Pittsburgh PAMinneapolis Heart InstituteAbbott Northwestern Hospital Minneapolis MNMinneapolis Heart InstituteAbbott Northwestern Hospital Minneapolis MNBarts Heart CentreBarts Health NHS Trust London United KingdomBarts Heart CentreBarts Health NHS Trust London United KingdomUniversity College London London United KingdomDepartment of Clinical Physiology Karolinska University Hospital, and Karolinska Institute Stockholm SwedenBarts Heart CentreBarts Health NHS Trust London United KingdomMinneapolis Heart Institute, United HospitalSt. Paul, Minnesota and Abbott Northwestern Hospital Minneapolis MNNational Heart, Lung, and Blood InstituteNational Institutes of Health Bethesda MDBackground Global longitudinal shortening (GL‐Shortening) and the mitral annular plane systolic excursion (MAPSE) are known markers in heart failure patients, but measurement may be subjective and less frequently reported because of the lack of automated analysis. Therefore, a validated, automated artificial intelligence (AI) solution can be of strong clinical interest. Methods and Results The model was implemented on cardiac magnetic resonance scanners with automated in‐line processing. Reproducibility was evaluated in a scan–rescan data set (n=160 patients). The prognostic association with adverse events (death or hospitalization for heart failure) was evaluated in a large patient cohort (n=1572) and compared with feature tracking global longitudinal strain measured manually by experts. Automated processing took ≈1.1 seconds for a typical case. On the scan–rescan data set, the model exceeded the precision of human expert (coefficient of variation 7.2% versus 11.1% for GL‐Shortening, P=0.0024; 6.5% versus 9.1% for MAPSE, P=0.0124). The minimal detectable change at 90% power was 2.53 percentage points for GL‐Shortening and 1.84 mm for MAPSE. AI GL‐Shortening correlated well with manual global longitudinal strain (R2=0.85). AI MAPSE had the strongest association with outcomes (χ2, 255; hazard ratio [HR], 2.5 [95% CI, 2.2–2.8]), compared with AI GL‐Shortening (χ2, 197; HR, 2.1 [95% CI,1.9–2.4]), manual global longitudinal strain (χ2, 192; HR, 2.1 [95% CI, 1.9–2.3]), and left ventricular ejection fraction (χ2, 147; HR, 1.8 [95% CI, 1.6–1.9]), with P<0.001 for all. Conclusions Automated in‐line AI‐measured MAPSE and GL‐Shortening can deliver immediate and highly reproducible results during cardiac magnetic resonance scanning. These results have strong associations with adverse outcomes that exceed those of global longitudinal strain and left ventricular ejection fraction.https://www.ahajournals.org/doi/10.1161/JAHA.121.023849artificial intelligencecardiac magnetic resonance imagingglobal longitudinal shortening, reproducibilityimage processingprognosis |
| spellingShingle | Hui Xue Jessica Artico Rhodri H. Davies Robert Adam Abhishek Shetye João B. Augusto Anish Bhuva Fredrika Fröjdh Timothy C. Wong Miho Fukui João L. Cavalcante Thomas A. Treibel Charlotte Manisty Marianna Fontana Martin Ugander James C. Moon Erik B. Schelbert Peter Kellman Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease artificial intelligence cardiac magnetic resonance imaging global longitudinal shortening, reproducibility image processing prognosis |
| title | Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance |
| title_full | Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance |
| title_fullStr | Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance |
| title_full_unstemmed | Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance |
| title_short | Automated In‐Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance |
| title_sort | automated in line artificial intelligence measured global longitudinal shortening and mitral annular plane systolic excursion reproducibility and prognostic significance |
| topic | artificial intelligence cardiac magnetic resonance imaging global longitudinal shortening, reproducibility image processing prognosis |
| url | https://www.ahajournals.org/doi/10.1161/JAHA.121.023849 |
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