LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,262
تعداد مقالات11,503
تعداد مشاهده مقاله81,855,803
تعداد دریافت فایل اصل مقاله120,988,738
Esmaely, Farid, Moradnejad, Pardis, Boudagh, Shabnam, Zamani-Aliabadi, Seyed Mohammad, Pasha, Hamid Reza, Bitarafan- Rajabi, Ahmad, Ansari, Leyla. (1405). Detection of Valve Vegetations in Native and Prosthetic Valves using Echocardiographic Radiomics and Deep Learning on Transesophageal Echocardiography Images. سامانه مدیریت نشریات علمی, 16(2), 139-152. doi: 10.31661/jbpe.v0i0.2505-1915
Farid Esmaely; Pardis Moradnejad; Shabnam Boudagh; Seyed Mohammad Zamani-Aliabadi; Hamid Reza Pasha; Ahmad Bitarafan- Rajabi; Leyla Ansari. "Detection of Valve Vegetations in Native and Prosthetic Valves using Echocardiographic Radiomics and Deep Learning on Transesophageal Echocardiography Images". سامانه مدیریت نشریات علمی, 16, 2, 1405, 139-152. doi: 10.31661/jbpe.v0i0.2505-1915
Esmaely, Farid, Moradnejad, Pardis, Boudagh, Shabnam, Zamani-Aliabadi, Seyed Mohammad, Pasha, Hamid Reza, Bitarafan- Rajabi, Ahmad, Ansari, Leyla. (1405). 'Detection of Valve Vegetations in Native and Prosthetic Valves using Echocardiographic Radiomics and Deep Learning on Transesophageal Echocardiography Images', سامانه مدیریت نشریات علمی, 16(2), pp. 139-152. doi: 10.31661/jbpe.v0i0.2505-1915
Esmaely, Farid, Moradnejad, Pardis, Boudagh, Shabnam, Zamani-Aliabadi, Seyed Mohammad, Pasha, Hamid Reza, Bitarafan- Rajabi, Ahmad, Ansari, Leyla. Detection of Valve Vegetations in Native and Prosthetic Valves using Echocardiographic Radiomics and Deep Learning on Transesophageal Echocardiography Images. سامانه مدیریت نشریات علمی, 1405; 16(2): 139-152. doi: 10.31661/jbpe.v0i0.2505-1915

Detection of Valve Vegetations in Native and Prosthetic Valves using Echocardiographic Radiomics and Deep Learning on Transesophageal Echocardiography Images

Journal of Biomedical Physics and Engineering
دوره 16، شماره 2 - شماره پیاپی 1، تیر 2026، صفحه 139-152    اصل مقاله (1.71 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2505-1915
نویسندگان
Farid Esmaely1؛ Pardis Moradnejad2؛ Shabnam Boudagh3؛ Seyed Mohammad Zamani-Aliabadi1؛ Hamid Reza Pasha2، 3؛ Ahmad Bitarafan- Rajabi* 1، 2؛ Leyla Ansari1، 4
1Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
2Cardiovascular Research Center, Rajaie Cardiovascular Institute, Tehran, Iran
3Echocardiography Research Center, Rajaie Cardiovascular Institute, Tehran, Iran
4Department of Nulear Medicine, Imam Sajjad Hospital, Yasuj university of medical science, Yasuj, Iran
چکیده
Background: Infective Endocarditis (IE) is a life-threatening condition that requires rapid and accurate diagnosis. Transesophageal Echocardiography (TEE) is the gold standard for detecting valve vegetations; however, its interpretation is highly operator-dependent and particularly challenging in patients with prosthetic valves. Recent advances in artificial intelligence, especially deep learning, offer opportunities to improve diagnostic accuracy and reduce observer variability.
Objective: This study aimed to evaluate the performance of deep learning–based models for detecting vegetations in TEE images to support the diagnostic workflow of IE.
Material and Methods: In this retrospective experimental study, a Faster Region-based Convolutional Neural Network (Faster R-CNN) was implemented to localize valve vegetations in TEE images. Four model configurations were developed using DenseNet121 and ResNet50 backbones, each trained in frozen and fine-tuned modes. All models were pretrained on RadImageNet. The dataset consisted of 1,000 annotated TEE frames acquired from both native and prosthetic heart valves.
Results: The fine-tuned DenseNet121 model achieved the best performance, with a mean Average Precision (mAP) of 0.653 and an Area Under the Curve (AUC) of 0.858. Its frozen version demonstrated lower performance (mAP=0.416, AUC=0.640). The fine-tuned ResNet50 model reached a mAP of 0.593 and an AUC of 0.789, while the frozen ResNet50 showed the lowest performance (mAP=0.403, AUC=0.601). 
Conclusion: Both fine-tuned DenseNet121 and ResNet50 models demonstrated effective localization of valve vegetations in TEE images, with comparable IoU performance. Although DenseNet121 showed superior classification accuracy, the similar localization results highlight the potential of both models as physician-assistive tools for enhancing IE diagnostic workflows.

تازه های تحقیق

Farid Esmaely (Google Scholar)

Ahmad Bitarafan- Rajabi (Google Scholar)

کلیدواژه‌ها
Infective Endocarditis؛ Transesophageal Echocardiography؛ Deep Learning؛ Faster R-CNN؛ Valve Vegetations؛ Computer-Assisted Image Interpretation؛ Heart Valves
مراجع
  1. Galeone A, Gardellini J, Di Nicola V, Perrone F, Menzione MS, Di Gaetano R, Luciani GB. Early and Long-Term Outcomes of Patients Undergoing Surgery for Native and Prosthetic Valve Endocarditis: The Role of Preoperative Neutrophil-to-Lymphocyte Ratio, Neutrophil-to-Platelet Ratio, and Monocyte-to Lymphocyte Ratio. J Clin Med. 2025;14(2):533. doi: 10.3390/jcm14020533. PubMed PMID: 39860541. PubMed PMCID: PMC11766379.
  2. Fukuchi T, Iwata K, Ohji G. Failure of early diagnosis of infective endocarditis in Japan--a retrospective descriptive analysis. Medicine (Baltimore). 2014;93(27):e237. doi: 10.1097/MD.0000000000000237. PubMed PMID: 25501088. PubMed PMCID: PMC4602777.
  3. Abadie BQ, Wang TKM. Contemporary Multi-modality Imaging of Prosthetic Aortic Valves. Rev Cardiovasc Med. 2025;26(1):25339. doi: 10.31083/RCM25339. PubMed PMID: 39867176. PubMed PMCID: PMC11759978.
  4. Li JS, Sexton DJ, Mick N, Nettles R, Fowler VG Jr, Ryan T, et al. Proposed modifications to the Duke criteria for the diagnosis of infective endocarditis. Clin Infect Dis. 2000;30(4):633-8. doi: 10.1086/313753. PubMed PMID: 10770721.
  5. d’Almeida S, Reischmann K, Andreß S, Felbel D, Stephan T, Hay B, et al. Evaluating the Duke Criteria for infectious endocarditis in a single-center retrospective study. Sci Rep. 2024;14(1):19524. doi: 10.1038/s41598-024-70196-x. PubMed PMID: 39174590. PubMed PMCID: PMC11341858.
  6. Carević V, Mladenović Z, Perković-Avelini R, Bečić T, Radić M, Fabijanić D. Three-Dimensional Transesophageal Echocardiography in the Diagnosis and Treatment of Mitral Prosthetic Valve Endocarditis-A Narrative Review. Medicina (Kaunas). 2021;58(1):23. doi: 10.3390/medicina58010023. PubMed PMID: 35056331. PubMed PMCID: PMC8779064.
  7. Ten Hove D, Slart RHJA, Glaudemans AWJM, Postma DF, Gomes A, Swart LE, et al. Using machine learning to improve the diagnostic accuracy of the modified Duke/ESC 2015 criteria in patients with suspected prosthetic valve endocarditis - a proof of concept study. Eur J Nucl Med Mol Imaging. 2024;51(13):3924-33. doi: 10.1007/s00259-024-06774-y. PubMed PMID: 38904778. PubMed PMCID: PMC11527948.
  8. Pamnani A, Skubas NJ. Imaging artifacts during transesophageal echocardiography. Anesth Analg. 2014;118(3):516-20. doi: 10.1213/ANE.0000000000000084. PubMed PMID: 24557099.
  9. Habib G, Lancellotti P, Antunes MJ, Bongiorni MG, Casalta JP, Del Zotti F, et al. 2015 ESC Guidelines for the management of infective endocarditis: The Task Force for the Management of Infective Endocarditis of the European Society of Cardiology (ESC). Endorsed by: European Association for Cardio-Thoracic Surgery (EACTS), the European Association of Nuclear Medicine (EANM). Eur Heart J. 2015;36(44):3075-128. doi: 10.1093/eurheartj/ehv319. PubMed PMID: 26320109.
  10. Saby L, Laas O, Habib G, Cammilleri S, Mancini J, Tessonnier L, et al. Positron emission tomography/computed tomography for diagnosis of prosthetic valve endocarditis: increased valvular 18F-fluorodeoxyglucose uptake as a novel major criterion. J Am Coll Cardiol. 2013;61(23):2374-82. doi: 10.1016/j.jacc.2013.01.092. PubMed PMID: 23583251.
  11. Stamate E, Piraianu AI, Ciobotaru OR, Crassas R, Duca O, Fulga A, et al. Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years. Diagnostics (Basel). 2024;14(11):1103. doi: 10.3390/diagnostics14111103. PubMed PMID: 38893630. PubMed PMCID: PMC11172021.
  12. Delgado V, Ajmone Marsan N, De Waha S, Bonaros N, Brida M, Burri H, et al. 2023 ESC guidelines for the management of endocarditis: developed by the task force on the management of endocarditis of the European Society of Cardiology (ESC) endorsed by the European Association for Cardio-Thoracic Surgery (EACTS) and the European Association of Nuclear Medicine (EANM). Eur Heart J. 2023;44(39):3948-4042. doi:10.1093/eurheartj/ehad193. PubMed PMID: 37622656.
  13. Sammartino AM, Bonfioli GB, Dondi F, Riccardi M, Bertagna F, Metra M, Vizzardi E. Contemporary Role of Positron Emission Tomography (PET) in Endocarditis: A Narrative Review. J Clin Med. 2024;13(14):4124. doi: 10.3390/jcm13144124. PubMed PMID: 39064164. PubMed PMCID: PMC11277723.
  14. Pizzi MN, Roque A, Fernández-Hidalgo N, Cuéllar-Calabria H, Ferreira-González I, Gonzàlez-Alujas MT, et al. Improving the Diagnosis of Infective Endocarditis in Prosthetic Valves and Intracardiac Devices With 18F-Fluordeoxyglucose Positron Emission Tomography/Computed Tomography Angiography: Initial Results at an Infective Endocarditis Referral Center. 2015;132(12):1113-26. doi: 10.1161/CIRCULATIONAHA.115.015316. PubMed PMID: 26276890.
  15. Papadimitriou-Olivgeris M, Ledergerber B, Siedentop B, Monney P, Frank M, Tzimas G, et al. Beyond the Timeline: 1-Year Mortality Trends in Early Versus Late Prosthetic Valve Endocarditis. Clin Infect Dis. 2025;80(4):804-6. doi: 10.1093/cid/ciae392. PubMed PMID: 39067056. PubMed PMCID: PMC12043058.
  16. Kusunose K. Transforming Echocardiography: The Role of Artificial Intelligence in Enhancing Diagnostic Accuracy and Accessibility. Intern Med. 2025;64(3):331-6. doi: 10.2169/internalmedicine.4171-24. PubMed PMID: 39048361. PubMed PMCID: PMC11867741.
  17. Kusunose K. Radiomics in Echocardiography: Deep Learning and Echocardiographic Analysis. Curr Cardiol Rep. 2020;22(9):89. doi: 10.1007/s11886-020-01348-4. PubMed PMID: 32648059.
  18. Tsampras T, Karamanidou T, Papanastasiou G, Stavropoulos TG. Deep learning for cardiac imaging: focus on myocardial diseases, a narrative review. Hellenic J Cardiol. 2025;81:18-24. doi: 10.1016/j.hjc.2024.12.002. PubMed PMID: 39662734.
  19. Baeßler B, Engelhardt S, Hekalo A, Hennemuth A, Hüllebrand M, Laube A, et al. Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging. Circ Cardiovasc Imaging. 2024;17(6):e015490. doi: 10.1161/CIRCIMAGING.123.015490. PubMed PMID: 38889216.
  20. Chen J, Liu Y, Wei S, Bian Z, Subramanian S, Carass A, et al. A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond. Med Image Anal. 2025;100:103385. doi: 10.1016/j.media.2024.103385. PubMed PMID: 39612808. PubMed PMCID: PMC11730935.
  21. Kim HE, Cosa-Linan A, Santhanam N, Jannesari M, Maros ME, Ganslandt T. Transfer learning for medical image classification: a literature review. BMC Med Imaging. 2022;22(1):69. doi: 10.1186/s12880-022-00793-7. PubMed PMID: 35418051. PubMed PMCID: PMC9007400.
  22. Li Z, Zheng Y, Shan D, Yang S, Li Q, Wang B, et al. ScribFormer: Transformer Makes CNN Work Better for Scribble-Based Medical Image Segmentation. IEEE Trans Med Imaging. 2024;43(6):2254-65. doi: 10.1109/TMI.2024.3363190. PubMed PMID: 38324425.
  23. Afifi S, Kaur R, GholamHosseini H, Monsef A, Ullah E, Baig MM, Salama Abdelhady A. Detection of Metastatic Tissues in Histopathologic Images using DenseNet-121 with Data Augmentation. Annu Int Conf IEEE Eng Med Biol Soc. 2025;2025:1-7. doi: 10.1109/EMBC58623.2025.11251669. PubMed PMID: 41336055.
  24. Xu W, Fu YL, Zhu D. ResNet and its application to medical image processing: Research progress and challenges. Comput Methods Programs Biomed. 2023;240:107660. doi: 10.1016/j.cmpb.2023.107660. PubMed PMID: 37320940.
  25. Alruwaili M, Mohamed M. An Integrated Deep Learning Model with EfficientNet and ResNet for Accurate Multi-Class Skin Disease Classification. Diagnostics (Basel). 2025;15(5):551. doi: 10.3390/diagnostics15050551. PubMed PMID: 40075797. PubMed PMCID: PMC11898587.
  26. Cao Z, Duan L, Yang G, Yue T, Chen Q. An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures. BMC Med Imaging. 2019;19(1):51. doi: 10.1186/s12880-019-0349-x. PubMed PMID: 31262255. PubMed PMCID: PMC6604293.
  27. Nurmaini S, Sapitri AI, Tutuko B, Rachmatullah MN, Rini DP, Darmawahyuni A, et al. Automatic echocardiographic anomalies interpretation using a stacked residual-dense network model. BMC Bioinformatics. 2023;24(1):365. doi: 10.1186/s12859-023-05493-9. PubMed PMID: 37759158. PubMed PMCID: PMC10536702.
  28. DYap MH, Goyal M, Osman F, Martí R, Denton E, Juette A, Zwiggelaar R. Breast ultrasound region of interest detection and lesion localisation. Artif Intell Med. 2020;107:101880. doi: 10.1016/j.artmed.2020.101880. PubMed PMID: 32828439.
  29. Zhang L, Wang X, Yang D, Sanford T, Harmon S, Turkbey B, et al. Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation. IEEE Trans Med Imaging. 2020;39(7):2531-40. doi: 10.1109/TMI.2020.2973595. PubMed PMID: 32070947. PubMed PMCID: PMC7393676.
  30. Snider EJ, Hernandez-Torres SI, Hennessey R. Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting. Diagnostics (Basel). 2023;13(3):417. doi: 10.3390/diagnostics13030417. PubMed PMID: 36766522. PubMed PMCID: PMC9914871.
  31. Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A. A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol. 2021;65(5):545-63. doi: 10.1111/1754-9485.13261. PubMed PMID: 34145766.
  32. Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, et al. Microsoft COCO: Common Objects in Context. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T, editors. Computer Vision – ECCV 2014. Lecture Notes in Computer Science. Cham: Springer; 2014. p. 740-55.
  33. Everingham M, Eslami SA, Van Gool L, Williams CK, Winn J, Zisserman A. The pascal visual object classes challenge: A retrospective. Int J Comput Vis. 2015;111(1):98-136. doi: 10.1007/s11263-014-0733-5.
  34. Moon KB, Tattersall MC, Adoe M, Osman F, Rahko PS. Evaluation of inter-observer variability regarding aortic and mitral valve findings on transesophageal echocardiograms ordered for suspected endocarditis. 2022;39(7):906-17. doi: 10.1111/echo.15400. PubMed PMID: 35733298. PubMed PMCID: PMC9541542.
  35. Lau V, Priestap F, Landry Y, Ball I, Arntfield R. Diagnostic Accuracy of Critical Care Transesophageal Echocardiography vs Cardiology-Led Echocardiography in ICU Patients. 2019;155(3):491-501. doi: 10.1016/j.chest.2018.11.025. PubMed. PMID: 30543807.
  36. Mei X, Liu Z, Robson PM, Marinelli B, Huang M, Doshi A, et al. RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning. Radiol Artif Intell. 2022;4(5):e210315. doi: 10.1148/ryai.210315. PubMed PMID: 36204533. PubMed PMCID: PMC9530758.
  37. Boice EN, Hernandez-Torres SI, Snider EJ. Comparison of Ultrasound Image Classifier Deep Learning Algorithms for Shrapnel Detection. J Imaging. 2022;8(5):140. doi: 10.3390/jimaging8050140. PubMed PMID: 35621904. PubMed PMCID: PMC9144026.
  38. Takahashi S, Sakaguchi Y, Kouno N, Takasawa K, Ishizu K, Akagi Y, et al. Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review. J Med Syst. 2024;48(1):84. doi: 10.1007/s10916-024-02105-8. PubMed PMID: 39264388. PubMed PMCID: PMC11393140.
  39. Huang G, Liu Z, Pleiss G, Maaten LV, Weinberger KQ. Convolutional Networks with Dense Connectivity. IEEE Trans Pattern Anal Mach Intell. 2022;44(12):8704-16. doi: 10.1109/TPAMI.2019.2918284. PubMed PMID: 31135351.
  40. Zhu M, Kuang Y, Jiang Z, Liu J, Zhang H, Zhao H, et al. Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ. Gland Surg. 2024;13(4):512-27. doi: 10.21037/gs-23-417. PubMed PMID: 38720675. PubMed PMCID: PMC11074652.
  41. He X, Bai XH, Chen H, Feng WW. Machine learning models in evaluating the malignancy risk of ovarian tumors: a comparative study. J Ovarian Res. 2024;17(1):219. doi: 10.1186/s13048-024-01544-8. PubMed PMID: 39506832. PubMed PMCID: PMC11539702.
  42. Kim J, Kim MH, Lim DJ, Lee H, Lee JJ, Kwon HS, et al. Deep Learning Technology for Classification of Thyroid Nodules Using Multi-View Ultrasound Images: Potential Benefits and Challenges in Clinical Application. Endocrinol Metab (Seoul). 2025;40(2):216-24. doi: 10.3803/EnM.2024.2058. PubMed PMID: 39805576. PubMed PMCID: PMC12061742.
  43. Okazaki S, Mine Y, Yoshimi Y, Iwamoto Y, Ito S, Peng TY, et al. RadImageNet and ImageNet as Datasets for Transfer Learning in the Assessment of Dental Radiographs: A Comparative Study. J Imaging Inform Med. 2025;38(1):534-44. doi: 10.1007/s10278-024-01204-9. PubMed PMID: 39048809. PubMed PMCID: PMC11811346.
  44. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Honolulu, HI: IEEE; 2017. p. 4700-8.
  45. Zhang YD, Satapathy SC, Zhang X, Wang SH. COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting. Cognit Comput. 2021:1-17. doi: 10.1007/s12559-020-09776-8. PubMed PMID: 33488837. PubMed PMCID: PMC7812362.
  46. Hou Y, Wu Z, Cai X, Zhu T. The application of improved densenet algorithm in accurate image recognition. Sci Rep. 2024;14(1):8645. doi: 10.1038/s41598-024-58421-z. PubMed PMID: 38622153. PubMed PMCID: PMC11018628.
  47. Senior R, Becher H, Monaghan M, Agati L, Zamorano J, Vanoverschelde JL, et al. Clinical practice of contrast echocardiography: recommendation by the European Association of Cardiovascular Imaging (EACVI) 2017. Eur Heart J Cardiovasc Imaging. 2017;18(11):1205-1205af. doi: 10.1093/ehjci/jex182. PubMed PMID: 28950366.
  48. Okonta KE, Adamu YB. What size of vegetation is an indication for surgery in endocarditis? Interact Cardiovasc Thorac Surg. 2012;15(6):1052-6. doi: 10.1093/icvts/ivs365. PubMed PMID: 22962320. PubMed PMCID: PMC3501298.
  49. Philip M, Tessonier L, Mancini J, Mainardi JL, Fernandez-Gerlinger MP, Lussato D, et al. Comparison Between ESC and Duke Criteria for the Diagnosis of Prosthetic Valve Infective Endocarditis. JACC Cardiovasc Imaging. 2020;13(12):2605-15. doi: 10.1016/j.jcmg.2020.04.011. PubMed PMID: 32563658.
آمار
تعداد مشاهده مقاله: 12
تعداد دریافت فایل اصل مقاله: 5


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب