Cargando…
A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound
OBJECTIVE: Echocardiography (ECG) is the most common method used to diagnose heart failure (HF). However, its accuracy relies on the experience of the operator. Additionally, the video format of the data makes it challenging for patients to bring them to referrals and reexaminations. Therefore, this...
Autores principales: | , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
De Gruyter
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10680380/ https://www.ncbi.nlm.nih.gov/pubmed/38025953 http://dx.doi.org/10.2478/jtim-2023-0088 |
_version_ | 1785142379151360000 |
---|---|
author | Liu, Zeye Huang, Yuan Li, Hang Li, Wenchao Zhang, Fengwen Ouyang, Wenbin Wang, Shouzheng Luo, Zhiling Wang, Jinduo Chen, Yan Xia, Ruibing Li, Yakun Pan, Xiangbin |
author_facet | Liu, Zeye Huang, Yuan Li, Hang Li, Wenchao Zhang, Fengwen Ouyang, Wenbin Wang, Shouzheng Luo, Zhiling Wang, Jinduo Chen, Yan Xia, Ruibing Li, Yakun Pan, Xiangbin |
author_sort | Liu, Zeye |
collection | PubMed |
description | OBJECTIVE: Echocardiography (ECG) is the most common method used to diagnose heart failure (HF). However, its accuracy relies on the experience of the operator. Additionally, the video format of the data makes it challenging for patients to bring them to referrals and reexaminations. Therefore, this study used a deep learning approach to assist physicians in assessing cardiac function to promote the standardization of echocardiographic findings and compatibility of dynamic and static ultrasound data. METHODS: A deep spatio-temporal convolutional model r2plus1d-Pan (trained on dynamic data and applied to static data) was improved and trained using the idea of “regression training combined with classification application,” which can be generalized to dynamic ECG and static cardiac ultrasound views to identify HF with a reduced ejection fraction (EF < 40%). Additionally, three independent datasets containing 8976 cardiac ultrasound views and 10085 cardiac ultrasound videos were established. Subsequently, a multinational, multi-center dataset of EF was labeled. Furthermore, model training and independent validation were performed. Finally, 15 registered ultrasonographers and cardiologists with different working years in three regional hospitals specialized in cardiovascular disease were recruited to compare the results. RESULTS: The proposed deep spatio-temporal convolutional model achieved an area under the receiveroperating characteristic curve (AUC) value of 0.95 (95% confidence interval [CI]: 0.947 to 0.953) on the training set of dynamic ultrasound data and an AUC of 1 (95% CI, 1 to 1) on the independent validation set. Subsequently, the model was applied to the static cardiac ultrasound view (validation set) with simultaneous input of 1, 2, 4, and 8 images of the same heart, with classification accuracies of 85%, 81%, 93%, and 92%, respectively. On the static data, the classification accuracy of the artificial intelligence (AI) model was comparable with the best performance of ultrasonographers and cardiologists with more than 3 working years (P = 0.344), but significantly better than the median level (P = 0.0000008). CONCLUSION: A new deep spatio-temporal convolution model was constructed to identify patients with HF with reduced EF accurately (< 40%) using dynamic and static cardiac ultrasound images. The model outperformed the diagnostic performance of most senior specialists. This may be the first HF-related AI diagnostic model compatible with multi-dimensional cardiac ultrasound data, and may thereby contribute to the improvement of HF diagnosis. Additionally, the model enables patients to carry “on-the-go” static ultrasound reports for referral and reexamination, thus saving healthcare resources. |
format | Online Article Text |
id | pubmed-10680380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | De Gruyter |
record_format | MEDLINE/PubMed |
spelling | pubmed-106803802023-07-05 A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound Liu, Zeye Huang, Yuan Li, Hang Li, Wenchao Zhang, Fengwen Ouyang, Wenbin Wang, Shouzheng Luo, Zhiling Wang, Jinduo Chen, Yan Xia, Ruibing Li, Yakun Pan, Xiangbin J Transl Int Med Original Article OBJECTIVE: Echocardiography (ECG) is the most common method used to diagnose heart failure (HF). However, its accuracy relies on the experience of the operator. Additionally, the video format of the data makes it challenging for patients to bring them to referrals and reexaminations. Therefore, this study used a deep learning approach to assist physicians in assessing cardiac function to promote the standardization of echocardiographic findings and compatibility of dynamic and static ultrasound data. METHODS: A deep spatio-temporal convolutional model r2plus1d-Pan (trained on dynamic data and applied to static data) was improved and trained using the idea of “regression training combined with classification application,” which can be generalized to dynamic ECG and static cardiac ultrasound views to identify HF with a reduced ejection fraction (EF < 40%). Additionally, three independent datasets containing 8976 cardiac ultrasound views and 10085 cardiac ultrasound videos were established. Subsequently, a multinational, multi-center dataset of EF was labeled. Furthermore, model training and independent validation were performed. Finally, 15 registered ultrasonographers and cardiologists with different working years in three regional hospitals specialized in cardiovascular disease were recruited to compare the results. RESULTS: The proposed deep spatio-temporal convolutional model achieved an area under the receiveroperating characteristic curve (AUC) value of 0.95 (95% confidence interval [CI]: 0.947 to 0.953) on the training set of dynamic ultrasound data and an AUC of 1 (95% CI, 1 to 1) on the independent validation set. Subsequently, the model was applied to the static cardiac ultrasound view (validation set) with simultaneous input of 1, 2, 4, and 8 images of the same heart, with classification accuracies of 85%, 81%, 93%, and 92%, respectively. On the static data, the classification accuracy of the artificial intelligence (AI) model was comparable with the best performance of ultrasonographers and cardiologists with more than 3 working years (P = 0.344), but significantly better than the median level (P = 0.0000008). CONCLUSION: A new deep spatio-temporal convolution model was constructed to identify patients with HF with reduced EF accurately (< 40%) using dynamic and static cardiac ultrasound images. The model outperformed the diagnostic performance of most senior specialists. This may be the first HF-related AI diagnostic model compatible with multi-dimensional cardiac ultrasound data, and may thereby contribute to the improvement of HF diagnosis. Additionally, the model enables patients to carry “on-the-go” static ultrasound reports for referral and reexamination, thus saving healthcare resources. De Gruyter 2023-07-05 /pmc/articles/PMC10680380/ /pubmed/38025953 http://dx.doi.org/10.2478/jtim-2023-0088 Text en © 2023 Zeye Liu, Yuan Huang, Hang Li, Wenchao Li, Fengwen Zhang, Wenbin Ouyang, Shouzheng Wang, Zhiling Luo, Jinduo Wang, Yan Chen, Ruibing Xia, Yakun Li, Xiangbin Pan, published by De Gruyter on behalf of the SMP https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License. |
spellingShingle | Original Article Liu, Zeye Huang, Yuan Li, Hang Li, Wenchao Zhang, Fengwen Ouyang, Wenbin Wang, Shouzheng Luo, Zhiling Wang, Jinduo Chen, Yan Xia, Ruibing Li, Yakun Pan, Xiangbin A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound |
title | A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound |
title_full | A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound |
title_fullStr | A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound |
title_full_unstemmed | A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound |
title_short | A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound |
title_sort | generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10680380/ https://www.ncbi.nlm.nih.gov/pubmed/38025953 http://dx.doi.org/10.2478/jtim-2023-0088 |
work_keys_str_mv | AT liuzeye ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT huangyuan ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT lihang ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT liwenchao ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT zhangfengwen ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT ouyangwenbin ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT wangshouzheng ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT luozhiling ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT wangjinduo ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT chenyan ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT xiaruibing ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT liyakun ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT panxiangbin ageneralizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT liuzeye generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT huangyuan generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT lihang generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT liwenchao generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT zhangfengwen generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT ouyangwenbin generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT wangshouzheng generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT luozhiling generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT wangjinduo generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT chenyan generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT xiaruibing generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT liyakun generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound AT panxiangbin generalizeddeeplearningmodelforheartfailurediagnosisusingdynamicandstaticultrasound |