{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:49:29Z","timestamp":1785408569329,"version":"3.56.0"},"reference-count":32,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:00:00Z","timestamp":1776124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82470803"],"award-info":[{"award-number":["82470803"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82200858"],"award-info":[{"award-number":["82200858"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82300870"],"award-info":[{"award-number":["82300870"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["82470803"],"award-info":[{"award-number":["82470803"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["82200858"],"award-info":[{"award-number":["82200858"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["82300870"],"award-info":[{"award-number":["82300870"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Objective: To enhance the information modeling capacity of large-kernel convolutional neural networks and to build a ureteral orifice detection framework for ureteroscopic imaging. Methods: A retrospective dataset of ureteroscopic images from 222 patients was collected. The patients were randomly divided into training and testing sets at a ratio of 7:3. Initially, video files were converted into image frames, and feature-relevant images were manually labeled by physicians. Subsequently, a ConvNeXt-based backbone augmented with squeeze-and-excitation (SE) modules was employed to extract diverse deep features. SCConv modules were incorporated across stages to strengthen the network\u2019s feature extraction performance. Lastly, enhanced spatial excitation attention mechanisms were cascaded to achieve superior feature fusion and detection accuracy. Comparative experiments were conducted against baseline models, including ConvNeXt, assessing accuracy, computational overhead, and inference latency. Results: On a test set of 491 ureteroscopic images, all models achieved mAP@50 values above 0.75, whereas the proposed network achieved 0.890, markedly exceeding baseline performance. The model operated at 20 ms per frame, achieving a frame rate of 50 FPS. Conclusions: We developed an improved deep learning framework based on large-kernel convolutional networks for real-time ureteral orifice detection in endoscopic scenarios. This system achieves a favorable balance between detection accuracy and real-time efficiency. The method demonstrates significant potential as a training and feedback tool for residents and junior urologists in clinical environments.<\/jats:p>","DOI":"10.3390\/bioengineering13040459","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T10:29:08Z","timestamp":1776162548000},"page":"459","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ureteral Orifice Detection in Ureteroscopic Images Based on Large-Kernel Convolutional Neural Networks and Attention-Based Feature Fusion"],"prefix":"10.3390","volume":"13","author":[{"given":"Liang","family":"Li","sequence":"first","affiliation":[{"name":"School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen-Yi","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing-Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan-Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhuo","sequence":"additional","affiliation":[{"name":"Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,14]]},"reference":[{"key":"ref_1","unstructured":"Engelsgjerd, J.S., and Deibert, C.M. (2018). Cystoscopy. StatPearls, StatPearls Publishing."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e42069","DOI":"10.1097\/MD.0000000000042069","article-title":"Ureteral orifice edema and stenting challenges in diabetic patients: Lessons from a case report","volume":"104","author":"Guo","year":"2025","journal-title":"Medicine"},{"key":"ref_3","unstructured":"Leslie, S., Chargui, S., and Stormont, G. (2023). Transurethral resection of the prostate. StatPearls, StatPearls Publishing."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106956","DOI":"10.1016\/j.crad.2025.106956","article-title":"The perils of Double-J stent placement: What radiologists must know","volume":"87","author":"Lucas","year":"2025","journal-title":"Clin. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3395","DOI":"10.1007\/s00345-023-04530-9","article-title":"Consultation on UTUC II Stockholm 2022: Diagnostics, prognostication, and follow-up\u2014Where are we today?","volume":"41","author":"Baard","year":"2023","journal-title":"World J. Urol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mantica, G., Ambrosini, F., Riccardi, N., Vecchio, E., Rigatti, L., De Rose, A.F., Van der Merwe, A., Terrone, C., Bartoletti, R., and Bonkat, G. (2021). Genitourinary tuberculosis: A comprehensive review of a neglected manifestation in low-endemic countries. Antibiotics, 10.","DOI":"10.3390\/antibiotics10111399"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1989","DOI":"10.1016\/j.ejso.2020.06.024","article-title":"Diagnostic accuracy of ureteroscopic biopsy in predicting stage and grade at final pathology in upper tract urothelial carcinoma: Systematic review and meta-analysis","volume":"46","author":"Subiela","year":"2020","journal-title":"Eur. J. Surg. Oncol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1111\/bju.16533","article-title":"Ureteric stenting outside of the operation theatre: Challenges and opportunities","volume":"135","author":"Gordon","year":"2025","journal-title":"BJU Int."},{"key":"ref_9","first-page":"CD014887","article-title":"Narrow band imaging versus white light cystoscopy alone for transurethral resection of non-muscle invasive bladder cancer","volume":"4","author":"Lai","year":"2022","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_10","first-page":"e67","article-title":"Comparison of methods for ureteral patency visualization","volume":"23","author":"Boytim","year":"2024","journal-title":"Rev. Urol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1097\/JU.0000000000003480","article-title":"Diagnosis and management of non-metastatic upper tract urothelial carcinoma: AUA\/SUO guideline","volume":"209","author":"Coleman","year":"2023","journal-title":"J. Urol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"221","DOI":"10.4103\/iju.iju_39_24","article-title":"Can artificial intelligence aid the urologists in detecting bladder cancer?","volume":"40","author":"Hengky","year":"2024","journal-title":"Indian J. Urol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100322","DOI":"10.1016\/j.sipas.2025.100322","article-title":"The Role of Artificial Intelligence in Advancing Urologic Care: From Diagnostics to Therapeutics","volume":"24","author":"Samenezhad","year":"2025","journal-title":"Surg. Pract. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1007\/s11548-021-02376-3","article-title":"Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy","volume":"16","author":"Lazo","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1038\/s41746-025-02109-9","article-title":"Clinical validation of an AI-assisted system for real-time kidney stone detection during flexible ureteroscopic surgery","volume":"8","author":"Wang","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guduguntla, A., Al-Khanaty, A., Davey, C.E., Patel, O., Ta, A., and Ischia, J. (2025). A Review of the Intraoperative Use of Artificial Intelligence in Urologic Surgery. Soci\u00e9t\u00e9 Int. D\u2019urologie J., 6.","DOI":"10.3390\/siuj6010005"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Peng, X., Liu, D., Li, Y., Xue, W., and Qian, D. (2019). Real-time detection of ureteral orifice in urinary endoscopy videos based on deep learning. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC.2019.8856484"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104104","DOI":"10.1016\/j.compbiomed.2020.104104","article-title":"A real-time system using deep learning to detect and track ureteral orifices during urinary endoscopy","volume":"128","author":"Liu","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nikouei, M., Baroutian, B., Nabavi, S., Taraghi, F., Aghaei, A., Sajedi, A., and Moghaddam, M.E. (2025). Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications. arXiv.","DOI":"10.1016\/j.iswa.2025.200561"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nie, C., Xu, C., Li, Z., Chu, L., and Hu, Y. (2023). Specular reflections detection and removal for endoscopic images based on brightness classification. Sensors, 23.","DOI":"10.3390\/s23020974"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.1038\/s41591-024-03113-4","article-title":"The limits of fair medical imaging AI in real-world generalization","volume":"30","author":"Yang","year":"2024","journal-title":"Nat. Med."},{"key":"ref_22","unstructured":"Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016). Ssd: Single shot multibox detector. European Conference on Computer Vision, Springer International Publishing.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017). Focal loss for dense object detection. IEEE International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (2019). Fcos: Fully convolutional one-stage object detection. IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. European Conference on Computer Vision, Springer International Publishing.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_29","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Han, J., Zhou, Y., Ding, G., and Sun, J. (2022). Scaling up your kernels to 31 \u00d7 31: Revisiting large kernel design in cnns. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52688.2022.01166"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., Kweon, I.S., and Xie, S. (2023). Convnext v2: Co-designing and scaling convnets with masked autoencoders. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/4\/459\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T04:12:42Z","timestamp":1776312762000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/4\/459"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,14]]},"references-count":32,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["bioengineering13040459"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13040459","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,14]]}}}