{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T23:05:26Z","timestamp":1774652726802,"version":"3.50.1"},"reference-count":59,"publisher":"IOP Publishing","issue":"6","license":[{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2025,11,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Current infusion monitoring methods primarily rely on two technological approaches: non-visual sensor technology and visual sensor technology, for real-time monitoring of the remaining liquid volume in infusion bottles within infusion scenarios. However, non-visual sensor-based methods often suffer from complex installation procedures and are prone to external interference, while visual sensor-based methods tend to exhibit low detection accuracy in complex infusion environments involving small targets, low contrast, tilted objects, and partial occlusions, making it difficult to accurately monitor the remaining liquid. To address these challenges, we propose a high-precision and lightweight object detection algorithm\u2014SDM-YOLO11n\u2014based on an improved version of YOLO11n. Specifically, a lightweight Split-Parallel Convolution (SPConv) module is introduced to enhance the backbone network\u2019s spatial modeling capabilities and improve feature extraction efficiency; the traditional upsampling operation is replaced with a Dynamic Sampling (DySample) module for more adaptive feature reconstruction and multi-scale information fusion; and a Mixed Local Channel Attention (MLCA) mechanism is incorporated to strengthen attention to key regions of infusion bottles and their internal liquids, thereby further improving detection accuracy. In addition, a method based on the ratio of geometric parameters of oriented bounding boxes is proposed to precisely estimate the remaining liquid volume in infusion bottles. Experimental results show that SDM-YOLO11n improves mAP@0.5:0.95 by 0.6 percentage points compared to YOLO11n, with a model size of only 5.1 MB. The proposed algorithm achieves high-precision detection of infusion bottles and their internal liquids in complex scenarios and enables real-time monitoring of the remaining liquid volume in multiple infusion bottles.<\/jats:p>","DOI":"10.1088\/2057-1976\/ae103b","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T22:47:58Z","timestamp":1759877278000},"page":"065020","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["SDM-YOLO11n: a lightweight and high-precision infusion monitoring method"],"prefix":"10.1088","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5007-6797","authenticated-orcid":false,"given":"Xiangyu","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1405-1458","authenticated-orcid":true,"given":"Wenbo","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2214-188X","authenticated-orcid":true,"given":"Zhecong","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"key":"bpexae103bbib1","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1038\/s41591-024-02970-3","type":"journal-article","article-title":"Artificial intelligence in surgery","volume":"30","author":"Varghese","year":"2024","journal-title":"Nat. Med."},{"key":"bpexae103bbib2","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/s41586-023-06221-2","type":"journal-article","article-title":"Scientific discovery in the age of artificial intelligence","volume":"620","author":"Wang","year":"2023","journal-title":"Nature"},{"key":"bpexae103bbib3","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1097\/NAN.0000000000000532","type":"journal-article","article-title":"Infusion therapy standards of practice","volume":"47","author":"Nickel","year":"2024","journal-title":"Journal of Infusion Nursing"},{"key":"bpexae103bbib4","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1097\/NAN.0000000000000396","type":"journal-article","article-title":"Infusion therapy standards of practice","volume":"44","author":"Gorski","year":"2021","journal-title":"Journal of Infusion Nursing"},{"key":"bpexae103bbib5","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMhpr035470","type":"journal-article","article-title":"Medical malpractice","volume":"350","author":"Studdert","year":"2004","journal-title":"New Engl. J. Med."},{"key":"bpexae103bbib6","doi-asserted-by":"publisher","first-page":"9265","DOI":"10.1038\/s41598-025-93911-8","type":"journal-article","article-title":"Design research on a smart infusion device to reduce medical workload and enhance patient safety","volume":"15","author":"Mu","year":"2025","journal-title":"Sci. Rep."},{"key":"bpexae103bbib7","doi-asserted-by":"publisher","first-page":"29-38","DOI":"10.1007\/s40264-023-01365-6","type":"journal-article","article-title":"Intravenous Medication Administration Safety with Smart Infusion Pumps in the Neonatal Intensive Care Unit: An Observational Study","volume":"47","author":"Schnock","year":"2023","journal-title":"Drug Safety"},{"key":"bpexae103bbib8","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1007\/s13534-023-00292-w","type":"journal-article","article-title":"Deep learning-based monitoring technique for real-time intravenous medication bag status","volume":"13","author":"Hwang","year":"2023","journal-title":"Biomedical Engineering Letters"},{"key":"bpexae103bbib9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3102681","type":"journal-article","article-title":"High-precision capacitive sensors for intravenous fluid monitoring in hospitals","volume":"70","author":"Salmaz","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"bpexae103bbib10","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1002\/jbm.b.34412","type":"journal-article","article-title":"Miniaturized piezoresistive flow sensor for real-time monitoring of intravenous infusion","volume":"108","author":"Haghighi","year":"2020","journal-title":"Journal of Biomedical Materials Research Part B: Applied Biomaterials"},{"key":"bpexae103bbib11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ohx.2022.e00345","type":"journal-article","article-title":"DripOMeter: an open-source opto-electronic system for intravenous (IV) infusion monitoring","volume":"12","author":"Venkatesh","year":"2022","journal-title":"HardwareX"},{"key":"bpexae103bbib12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3463496","type":"journal-article","article-title":"Dropmonitor: millimeter-level sensing for RFID-based infusion drip rate monitoring","volume":"5","author":"Lin","year":"2021","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"bpexae103bbib13","doi-asserted-by":"publisher","first-page":"3491","DOI":"10.3934\/mbe.2021175","type":"journal-article","article-title":"Infusion port level detection for intravenous infusion based on Yolo v3 neural network","volume":"18","author":"Huang","year":"2021","journal-title":"Mathematical Biosciences and Engineering"},{"key":"bpexae103bbib14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/MeMeA49120.2020.9137359","type":"conference-proceedings","article-title":"Real-time drip infusion monitoring through a computer vision system","author":"Giaquinto","year":"2020"},{"key":"bpexae103bbib15","doi-asserted-by":"publisher","first-page":"2037, 012109","DOI":"10.1088\/1742-6596\/2037\/1\/012109","type":"conference-proceedings","article-title":"Design of an infusion monitoring system based on image processing","author":"Song","year":"2021"},{"key":"bpexae103bbib16","doi-asserted-by":"publisher","first-page":"3082","DOI":"10.3390\/s25103082","type":"journal-article","article-title":"A Software-defined sensor system using semantic segmentation for monitoring remaining intravenous fluids","volume":"25","author":"Sunwoo","year":"2025","journal-title":"Sensors"},{"key":"bpexae103bbib17","doi-asserted-by":"publisher","first-page":"269","DOI":"10.16208\/j.issn1000-7024.2023.01.036","type":"journal-article","article-title":"Real-time infusion monitoring based on improved YOLOv5s network","volume":"44","author":"Chen","year":"2023","journal-title":"Computer Engineering and Design"},{"key":"bpexae103bbib18","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-5702977\/v1","type":"other","article-title":"High precision intravenous drip infusion monitoring method based on improved YOLOv8n","author":"Ma","year":"2025"},{"key":"bpexae103bbib19","doi-asserted-by":"publisher","first-page":"14148","DOI":"10.1109\/JSEN.2020.3039009","type":"journal-article","article-title":"Deep learning-based computer vision for real-time intravenous drip infusion monitoring","volume":"21","author":"Giaquinto","year":"2020","journal-title":"IEEE Sens. J."},{"key":"bpexae103bbib20","doi-asserted-by":"publisher","DOI":"10.15199\/48.2024.05.49","type":"journal-article","article-title":"Capacitive measurement of infusion fluid volume","volume":"100","author":"Dziadak","year":"2024","journal-title":"Przegl\u0105d Elektrotechniczny"},{"key":"bpexae103bbib21","doi-asserted-by":"publisher","first-page":"9243","DOI":"10.1007\/s11042-022-13644-y","type":"journal-article","article-title":"Object detection using YOLO: challenges, architectural successors, datasets and applications","volume":"82","author":"Diwan","year":"2023","journal-title":"Multimedia Tools and Applications"},{"key":"bpexae103bbib22","doi-asserted-by":"publisher","first-page":"12052","DOI":"10.1038\/s41598-024-62933-z","type":"journal-article","article-title":"YOLOv8-PD: an improved road damage detection algorithm based on YOLOv8n model","volume":"14","author":"Zeng","year":"2024","journal-title":"Scientific Reports"},{"key":"bpexae103bbib23","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","type":"journal-article","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"bpexae103bbib24","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","type":"journal-article","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vision"},{"key":"bpexae103bbib25","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.3390\/electronics13061111","type":"journal-article","article-title":"A Novel Low-Cost Capacitance Sensor Solution for Real-Time Bubble Monitoring in Medical Infusion Devices","volume":"13","author":"Kok","year":"2024","journal-title":"Electronics"},{"key":"bpexae103bbib26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/EIIS.2017.8298632","type":"conference-proceedings","article-title":"Liquid surface location of transparent container based on visual analysis","author":"Feng","year":"2017"},{"key":"bpexae103bbib27","doi-asserted-by":"publisher","first-page":"7126","DOI":"10.3390\/s23167126","type":"journal-article","article-title":"Image-Based Sensor for Liquid Level Monitoring during Bottling with Application to Craft and Home-Brewing","volume":"23","author":"Musi\u0107","year":"2024","journal-title":"Sensors"},{"key":"bpexae103bbib28","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/ITCEM57303.2022.00018","type":"conference-proceedings","article-title":"Automatic hospital inspection system based on convolutional neural network","author":"Ao","year":"2022"},{"key":"bpexae103bbib29","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1109\/CISAT62382.2024.10695377","type":"conference-proceedings","article-title":"Develop a liquid level detection algorithm for infusion bottles based on Ghost-YOLOv4","author":"Jianfeng","year":"2024"},{"key":"bpexae103bbib30","doi-asserted-by":"publisher","first-page":"6434","DOI":"10.1109\/tip.2021.3093795","type":"journal-article","article-title":"SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Features Recombining","volume":"30","author":"Qiu","year":"2021","journal-title":"IEEE Transactions on Image Processing"},{"key":"bpexae103bbib31","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.3390\/f15091630","type":"journal-article","article-title":"LD-YOLO: a lightweight dynamic forest fire and smoke detection model with dysample and spatial context awareness module","volume":"15","author":"Lin","year":"2024","journal-title":"Forests"},{"key":"bpexae103bbib32","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106442","type":"journal-article","article-title":"Mixed local channel attention for object detection","volume":"123","author":"Wan","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"bpexae103bbib33","doi-asserted-by":"publisher","first-page":"989","DOI":"10.3390\/s24030989","type":"journal-article","article-title":"YOLOv7-TS: a traffic sign detection model based on sub-pixel convolution and feature fusion","volume":"24","author":"Zhao","year":"2024","journal-title":"Sensors"},{"key":"bpexae103bbib34","doi-asserted-by":"publisher","first-page":"22367","DOI":"10.1109\/CVPR52729.2023.02142","type":"conference-proceedings","article-title":"Activating more pixels in image super-resolution transformer","author":"Chen","year":"2023"},{"key":"bpexae103bbib35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICASSP49357.2023.10096516","type":"conference-proceedings","article-title":"Efficient multi-scale attention module with cross-spatial learning","author":"Ouyang","year":"2023"},{"key":"bpexae103bbib36","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121352","type":"journal-article","article-title":"Large separable kernel attention: rethinking the large kernel attention design in cnn","volume":"236","author":"Lau","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"bpexae103bbib37","doi-asserted-by":"publisher","first-page":"8906","DOI":"10.1109\/TMM.2023.3243616","type":"journal-article","article-title":"Dilateformer: multi-scale dilated transformer for visual recognition","volume":"25","author":"Jiao","year":"2023","journal-title":"IEEE Trans. Multimedia"},{"key":"bpexae103bbib38","doi-asserted-by":"publisher","first-page":"45986","DOI":"10.1109\/access.2024.3381539","type":"journal-article","article-title":"MFCANet: Multiscale Feature Context Aggregation Network for Oriented Object Detection in Remote-Sensing Images","volume":"12","author":"Jiang","year":"2024","journal-title":"IEEE Access"},{"key":"bpexae103bbib39","doi-asserted-by":"publisher","first-page":"13713","DOI":"10.1109\/CVPR46437.2021.01350","type":"conference-proceedings","article-title":"Coordinate attention for efficient mobile network design","author":"Hou","year":"2021"},{"key":"bpexae103bbib40","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1109\/TPAMI.2022.3164083","type":"journal-article","article-title":"Contextual transformer networks for visual recognition","volume":"45","author":"Li","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"bpexae103bbib41","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108784","type":"journal-article","article-title":"Channel prior convolutional attention for medical image segmentation","volume":"178","author":"Huang","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"bpexae103bbib42","doi-asserted-by":"publisher","first-page":"e0328223","DOI":"10.1371\/journal.pone.0328223","type":"journal-article","article-title":"Optimized small object detection in low resolution infrared images using super resolution and attention based feature fusion","volume":"20","author":"Wang","year":"2025","journal-title":"PLoS One"},{"key":"bpexae103bbib43","doi-asserted-by":"publisher","first-page":"10323","DOI":"10.1109\/CVPR52729.2023.00995","type":"conference-proceedings","article-title":"Biformer: vision transformer with bi-level routing attention","author":"Zhu","year":"2023"},{"key":"bpexae103bbib44","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1109\/TMI.2020.3035253","type":"journal-article","article-title":"CA-net: comprehensive attention convolutional neural networks for explainable medical image segmentation","volume":"40","author":"Ran","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"bpexae103bbib45","doi-asserted-by":"publisher","first-page":"10886","DOI":"10.48550\/arXiv.2103.13425","type":"conference-proceedings","article-title":"Diverse branch block: building a convolution as an inception-like unit","author":"Ding","year":"2021"},{"key":"bpexae103bbib46","doi-asserted-by":"publisher","first-page":"9528","DOI":"10.1109\/TNLS.2022.3151138","type":"journal-article","article-title":"DualConv: dual convolutional kernels for lightweight deep neural networks","volume":"34","author":"Zhong","year":"2022","journal-title":"IEEE Trans Neural Netw. Learn. Syst."},{"key":"bpexae103bbib47","doi-asserted-by":"publisher","first-page":"11030","DOI":"10.1109\/CVPR42600.2020.01104","type":"conference-proceedings","article-title":"Dynamic convolution: attention over convolution kernels","author":"Chen","year":"2020"},{"key":"bpexae103bbib48","doi-asserted-by":"publisher","DOI":"10.1177\/00368504251336695","type":"journal-article","article-title":"Omni-dimensional dynamic convolution with coordinate attention detection scheme","volume":"108","author":"Bai","year":"2025","journal-title":"Sci. Prog."},{"key":"bpexae103bbib49","doi-asserted-by":"publisher","first-page":"83867","DOI":"10.1007\/s11042-024-18941-2","type":"journal-article","article-title":"Highly efficient gaze estimation method using online convolutional re-parameterization","volume":"83","author":"Gu","year":"2024","journal-title":"Multimedia Tools Appl."},{"key":"bpexae103bbib50","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-20411-4","type":"journal-article","article-title":"A lightweight convolutional neural network model with receptive field block for C-shaped root canal detection in mandibular second molars","volume":"12","author":"Zhang","year":"2022","journal-title":"Sci. Rep."},{"key":"bpexae103bbib51","doi-asserted-by":"publisher","first-page":"6434","DOI":"10.1109\/TIP.2021.3093795","type":"journal-article","article-title":"Slimconv: reducing channel redundancy in convolutional neural networks by features recombining","volume":"30","author":"Qiu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"bpexae103bbib52","doi-asserted-by":"publisher","first-page":"3744","DOI":"10.3390\/electronics12183744","type":"journal-article","article-title":"Yolo-ssfs: a method combining spd-conv\/stdl\/im-fpn\/siou for outdoor small target vehicle detection","volume":"12","author":"Gu","year":"2023","journal-title":"Electronics"},{"key":"bpexae103bbib53","doi-asserted-by":"publisher","first-page":"48925","DOI":"10.1007\/s11042-023-17472-6","type":"journal-article","article-title":"Res4net-CBAM: a deep cnn with convolution block attention module for tea leaf disease diagnosis","volume":"83","author":"Bhuyan","year":"2024","journal-title":"Multimedia Tools Appl."},{"key":"bpexae103bbib54","doi-asserted-by":"publisher","first-page":"11187","DOI":"10.1007\/s10586-024-04532-1","type":"journal-article","article-title":"Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification","volume":"27","author":"Pacal","year":"2024","journal-title":"Cluster Computing"},{"key":"bpexae103bbib55","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2212.01173","type":"journal-article","article-title":"DWRSeg: rethinking efficient acquisition of multi-scale contextual information for real-time semantic segmentation","author":"Wei","year":"2022","journal-title":"arXiv preprint"},{"key":"bpexae103bbib56","doi-asserted-by":"publisher","first-page":"11624","DOI":"10.1609\/aaai.v38i10.29045","type":"journal-article","article-title":"Make RepVGG Greater Again: A Quantization-Aware Approach","volume":"38","author":"Chu","year":"2024","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"bpexae103bbib57","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105057","type":"journal-article","article-title":"ASF-YOLO: a novel YOLO model with attentional scale sequence fusion for cell instance segmentation","volume":"147","author":"Kang","year":"2024","journal-title":"Image Vision Comput."},{"key":"bpexae103bbib58","doi-asserted-by":"publisher","DOI":"10.1016\/j.cropro.2024.106665","type":"journal-article","article-title":"Rice disease segmentation method based on CBAM-CARAFE-DeepLabv3+","volume":"180","author":"Zeng","year":"2024","journal-title":"Crop Prot."},{"key":"bpexae103bbib59","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109630","type":"journal-article","article-title":"Enhanced YOLOv8 algorithm for leaf disease detection with lightweight GOCR-ELAN module and loss function: WSIoU","volume":"186","author":"Wen","year":"2025","journal-title":"Comput. Biol. Med."}],"container-title":["Biomedical Physics &amp; Engineering Express"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae103b","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae103b\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae103b","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae103b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae103b\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae103b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae103b\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae103b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T18:33:55Z","timestamp":1761330835000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ae103b"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,24]]},"references-count":59,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,10,24]]},"published-print":{"date-parts":[[2025,11,28]]}},"URL":"https:\/\/doi.org\/10.1088\/2057-1976\/ae103b","relation":{},"ISSN":["2057-1976"],"issn-type":[{"value":"2057-1976","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,24]]},"assertion":[{"value":"SDM-YOLO11n: a lightweight and high-precision infusion monitoring method","name":"article_title","label":"Article Title"},{"value":"Biomedical Physics & Engineering Express","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-07-23","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-10-07","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-10-24","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}