دورية أكاديمية
Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography.
| العنوان: | Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography. |
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| المؤلفون: | Zha SZ; University of Oslo, Oslo, Norway. sigzha@gmail.com., Rogstadkjernet M; University of Oslo, Oslo, Norway., Klæboe LG; Oslo University Hospital, Rikshospitalet, Oslo, Norway., Skulstad H; University of Oslo, Oslo, Norway.; Oslo University Hospital, Rikshospitalet, Oslo, Norway., Singstad BJ; Oslo University Hospital, Rikshospitalet, Oslo, Norway., Gilbert A; GE HealthCare, Oslo, Norway., Edvardsen T; University of Oslo, Oslo, Norway.; Oslo University Hospital, Rikshospitalet, Oslo, Norway., Samset E; University of Oslo, Oslo, Norway.; GE HealthCare, Oslo, Norway., Brekke PH; Oslo University Hospital, Rikshospitalet, Oslo, Norway. |
| المصدر: | Cardiovascular ultrasound [Cardiovasc Ultrasound] 2023 Oct 13; Vol. 21 (1), pp. 19. Date of Electronic Publication: 2023 Oct 13. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: BioMed Central Country of Publication: England NLM ID: 101159952 Publication Model: Electronic Cited Medium: Internet ISSN: 1476-7120 (Electronic) Linking ISSN: 14767120 NLM ISO Abbreviation: Cardiovasc Ultrasound Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: London, UK : BioMed Central, 2003- |
| مواضيع طبية MeSH: | Deep Learning*, Humans ; Echocardiography ; Heart ; Stroke Volume |
| مستخلص: | Background: Measurement of the left ventricular outflow tract diameter (LVOTd) in echocardiography is a common source of error when used to calculate the stroke volume. The aim of this study is to assess whether a deep learning (DL) model, trained on a clinical echocardiographic dataset, can perform automatic LVOTd measurements on par with expert cardiologists. Methods: Data consisted of 649 consecutive transthoracic echocardiographic examinations of patients with coronary artery disease admitted to a university hospital. 1304 LVOTd measurements in the parasternal long axis (PLAX) and zoomed parasternal long axis views (ZPLAX) were collected, with each patient having 1-6 measurements per examination. Data quality control was performed by an expert cardiologist, and spatial geometry data was preserved for each LVOTd measurement to convert DL predictions into metric units. A convolutional neural network based on the U-Net was used as the DL model. Results: The mean absolute LVOTd error was 1.04 (95% confidence interval [CI] 0.90-1.19) mm for DL predictions on the test set. The mean relative LVOTd errors across all data subgroups ranged from 3.8 to 5.1% for the test set. Generally, the DL model had superior performance on the ZPLAX view compared to the PLAX view. DL model precision for patients with repeated LVOTd measurements had a mean coefficient of variation of 2.2 (95% CI 1.6-2.7) %, which was comparable to the clinicians for the test set. Conclusion: DL for automatic LVOTd measurements in PLAX and ZPLAX views is feasible when trained on a limited clinical dataset. While the DL predicted LVOTd measurements were within the expected range of clinical inter-observer variability, the robustness of the DL model requires validation on independent datasets. Future experiments using temporal information and anatomical constraints could improve valvular identification and reduce outliers, which are challenges that must be addressed before clinical utilization. (© 2023. BioMed Central Ltd., part of Springer Nature.) |
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| معلومات مُعتمدة: | Project number 271555/F20 The Research Council of Norway |
| فهرسة مساهمة: | Keywords: Automated measurements; Deep learning; Left ventricular outflow tract; Machine learning; Transthoracic echocardiography |
| المشرفين على المادة: | 0 (Plax) |
| تواريخ الأحداث: | Date Created: 20231013 Date Completed: 20231101 Latest Revision: 20231119 |
| رمز التحديث: | 20240628 |
| مُعرف محوري في PubMed: | PMC10571406 |
| DOI: | 10.1186/s12947-023-00317-5 |
| PMID: | 37833731 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1476-7120 |
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| DOI: | 10.1186/s12947-023-00317-5 |