{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T13:08:58Z","timestamp":1780319338984,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T00:00:00Z","timestamp":1753747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Taichung Veterans General Hospital","award":["TCVGH-FCU1128202"],"award-info":[{"award-number":["TCVGH-FCU1128202"]}]},{"name":"Taichung Veterans General Hospital","award":["TCVGH-FC U1138204"],"award-info":[{"award-number":["TCVGH-FC U1138204"]}]},{"name":"Taichung Veterans General Hospital","award":["TCVGH-FCU1148208"],"award-info":[{"award-number":["TCVGH-FCU1148208"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>This research aims to offer a deep learning-based diagnostic approach for hemorrhagic complications linked to patent ductus arteriosus (PDA) in preterm infants. Utilizing the You Only Look Once (YOLO) algorithm, this research analyzed five key cardiac parameters derived from echocardiographic ultrasonic waves: the left ventricular ejection time (LVET), left ventricular internal dimension at diastole (LVIDd), left ventricular internal dimension at systole (LVIDs), posterior wall thickness at end-systole (HES), and RR interval between two successive R-waves. The proposed ensemble model achieved best-in-class detection accuracies for each parameter, with rates of 97.56% (LVET), 88.69% (LVIDd), 99.50% (LVIDs), 82.29% (HES), and 81.15% (RR interval). Furthermore, assessment of cardiac function using derived indices\u2014end-systolic wall stress (ESWS) and rate-corrected mean velocity of circumferential fiber shortening (mVcfc)\u2014achieved mean accuracy rates of 82.33% and 90.16%, respectively. This approach enables physicians to accurately evaluate cardiac function in preterm infants and facilitates the diagnosis of PDA-related hemorrhagic complications.<\/jats:p>","DOI":"10.3390\/bioengineering12080815","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T09:31:45Z","timestamp":1753781505000},"page":"815","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluating Hemodynamic Changes in Preterm Infants Using Recent YOLO Models"],"prefix":"10.3390","volume":"12","author":[{"given":"Li-Cheng","family":"Huang","sequence":"first","affiliation":[{"name":"Master\u2019s Program of Electroacoustics, Feng Chia University, Taichung 40724, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi-Wei","family":"Zheng","sequence":"additional","affiliation":[{"name":"Program of Mechanical and Aeronautical Engineering, Feng Chia University, Taichung 40724, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3562-0694","authenticated-orcid":false,"given":"Ming-Chih","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 402, Taiwan"},{"name":"Children\u2019s Medical Center, Taichung Veterans General Hospital, Taichung 40705, Taiwan"},{"name":"School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan"},{"name":"School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0877-664X","authenticated-orcid":false,"given":"Yu-Ting","family":"Tsai","sequence":"additional","affiliation":[{"name":"Master\u2019s Program of Electroacoustics, Feng Chia University, Taichung 40724, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.earlhumdev.2008.12.008","article-title":"Clinical and echocardiographic diagnosis of patent ductus arteriosus in premature neonates","volume":"85","author":"Chiruvolu","year":"2009","journal-title":"Early Hum. Dev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.jfma.2013.02.011","article-title":"Tailor-made circulatory management based on the stress\u2013velocity relationship in preterm infants","volume":"112","author":"Toyoshima","year":"2013","journal-title":"J. Formos. Med. Assoc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1053\/j.semperi.2011.09.022","article-title":"Patent ductus arteriosus: Are current neonatal treatment options better or worse than no treatment at all?","volume":"36","author":"Clyman","year":"2012","journal-title":"Semin. Perinatol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1007\/s002469900127","article-title":"Postnatal left ventricular contractility in very low birth weight infants","volume":"18","author":"Takahashi","year":"1997","journal-title":"Pediatr. Cardiol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Huang, W.-H., Li, D.-M., Hsu, C.-T., Lin, Y.-H., Hsu, Y.-C., Wang, T.-M., and Lin, M.-C. (2021). Decreased incidence of intraventricular hemorrhage in extremely low birth weight infants using customized circulatory management to evaluate the hemodynamic change of patent ductus arteriosus. Front. Pediatr., 9.","DOI":"10.3389\/fped.2021.711871"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.21037\/qims-20-745","article-title":"Automatic segmentation of the left ventricle in echocardiographic images using convolutional neural networks","volume":"11","author":"Kim","year":"2021","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ejmp.2019.10.001","article-title":"MFP-Unet: A novel deep learning based approach for left ventricle segmentation in echocardiography","volume":"67","author":"Moradi","year":"2019","journal-title":"Phys. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101873","DOI":"10.1016\/j.media.2020.101873","article-title":"Deep pyramid local attention neural network for cardiac structure segmentation in two-dimensional echocardiography","volume":"67","author":"Liu","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3772129","DOI":"10.1155\/2021\/3772129","article-title":"Automatic segmentation of left ventricle in echocardiography based on YOLOv3 model to achieve constraint and positioning","volume":"2021","author":"Zhuang","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mortada, M.J., Tomassini, S., Anbar, H., Morettini, M., Burattini, L., and Sbrollini, A. (2023). Segmentation of anatomical structures of the left heart from echocardiographic images using deep learning. Diagnostics, 13.","DOI":"10.3390\/diagnostics13101683"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. arXiv.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2016). Faster R-CNN: Towards real-time object detection with region proposal networks. arXiv.","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017). Aggregated residual transformations for deep neural networks. arXiv.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_20","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2017). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. arXiv.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_22","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv."},{"key":"ref_23","unstructured":"Kingma, D.P., and Ba, J. (2017). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_24","unstructured":"Liu, L., and Luo, X. (2020). A new accelerated stochastic gradient method with momentum. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.gltp.2022.04.020","article-title":"A review: Data pre-processing and data augmentation techniques","volume":"3","author":"Maharana","year":"2022","journal-title":"Glob. Transit. Proc."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Misra, D. (2020). Mish: A self regularized non-monotonic activation function. arXiv.","DOI":"10.5244\/C.34.191"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2018). Focal loss for dense object detection. arXiv.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","article-title":"SSD: Single shot multibox detector","volume":"Volume 9905","author":"Liu","year":"2016","journal-title":"Computer Vision\u2013ECCV 2016"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. arXiv.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2016). YOLO9000: Better, faster, stronger. arXiv.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_31","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOV4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Liao, H.-Y.M., Yeh, I.-H., Wu, Y.-H., Chen, P.-Y., and Hsieh, J.-W. (2019). CSPNet: A new backbone that can enhance learning capability of CNN. arXiv.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, Z.-Q., Zheng, P., Xu, S., and Wu, X. (2019). Object detection with deep learning: A review. arXiv.","DOI":"10.1109\/TNNLS.2018.2876865"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1093\/ejechocard\/jeq084","article-title":"Systolic time intervals as simple echocardiographic parameters of left ventricular systolic performance: Correlation with ejection fraction and longitudinal two-dimensional strain","volume":"11","author":"Reant","year":"2010","journal-title":"Eur. J. Echocardiogr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1016\/0002-9149(92)90772-Q","article-title":"Intra- and interobserver reproducibility of Doppler-assessed indexes of left ventricular diastolic function in a population-based study (the Framingham Heart Study)","volume":"70","author":"Galderisi","year":"1992","journal-title":"Am. J. Cardiol."},{"key":"ref_36","unstructured":"Reis, D., Kupec, J., Hong, J., and Daoudi, A. (2023). Real-Time Flying Object Detection with YOLOV8. arXiv."},{"key":"ref_37","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv."},{"key":"ref_38","unstructured":"Khanam, R., and Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancements. arXiv."},{"key":"ref_39","unstructured":"Tian, Y., Ye, Q., and Doermann, D. (2025). YOLOv12: Attention-Centric Real-Time Object Detectors. arXiv."},{"key":"ref_40","unstructured":"(2011). Studio Encoding Parameters of Digital Television for Standard 4:3 and Wide-Screen 16:9 Aspect Ratios (Standard No. ITU-R BT.601-7)."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/echo.13853","article-title":"Early modifications of cardiac function in preterm neonates using speckle tracking echocardiography","volume":"35","author":"Castaldi","year":"2018","journal-title":"Echocardiography"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1007\/s00592-022-01906-y","article-title":"Comprehensive assessment of biventricular myocardial function by two-dimensional speckle tracking echocardiography in infants of gestational diabetic mothers","volume":"59","author":"Sonaglioni","year":"2022","journal-title":"Acta Diabetol."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/8\/815\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:17:49Z","timestamp":1760033869000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/8\/815"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,29]]},"references-count":42,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["bioengineering12080815"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12080815","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,29]]}}}