{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T22:44:03Z","timestamp":1783032243579,"version":"3.54.6"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The wrist is a high-incidence site for traumatic injuries in the skeletal system, where rapid and accurate diagnosis of fractures is critically important in clinical practice. However, the complex multi-scale variations of fractures impede precise detection of wrist fractures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To address this issue, this study proposes a novel wrist fracture detection model, PEYOLO, based on multi-level receptive field feature extraction and cross-scale feature fusion. First, we design a Parallel Dilated Multi-head Attention Module (PDMAM), which performs sparse sampling under different receptive fields to simultaneously obtain receptive fields of varying scales within the same layer. Second, we introduce an Efficient Multi-Scale Attention module, which enhances the perception of multi-scale fracture features by fusing multi-scale spatial information with cross-dimensional dependencies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental results on the GRAZPEDWRI-DX-fracture dataset demonstrate that PEYOLO improves mAP by 1.4% over the baseline and outperforms several state-of-the-art object detection models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>PEYOLO has demonstrated highly advantageous high precision and inference speed, making it an important tool for clinical diagnosis and treatment planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1850116","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T04:27:26Z","timestamp":1778473646000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["PEYOLO: a wrist fracture detection network based on multi-level receptive field feature extraction and cross-scale fusion"],"prefix":"10.3389","volume":"13","author":[{"given":"Shuwei","family":"Zhang","sequence":"first","affiliation":[{"name":"Orthopedic Department, Handan First Hospital","place":["Handan, China"]},{"name":"Graduate School, Chengde Medical College","place":["Chengde, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University","place":["Ya\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiong","family":"Mu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University","place":["Ya\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaohai","family":"Ren","sequence":"additional","affiliation":[{"name":"Orthopedic Department, Handan First Hospital","place":["Handan, China"]},{"name":"Graduate School, Chengde Medical College","place":["Chengde, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"ref1","first-page":"141","article-title":"Athletic injuries of the wrist","volume-title":"Clin Orthop Relat Res","author":"Linscheid","year":"1976"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1186\/s12891-025-08733-6","article-title":"Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images","volume":"26","author":"Saadh","year":"2025","journal-title":"BMC Musculoskelet Disord"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1007\/s11831-025-10330-8","article-title":"Deep learning techniques for the diagnosis and detection of orthopedic conditions: a systematic review of recent advances and challenges","volume":"33","author":"Shah","year":"2025","journal-title":"Arch Comput Methods Eng"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"288","DOI":"10.3390\/bioengineering12030288","article-title":"Progress in the application of artificial intelligence in ultrasound-assisted medical diagnosis","volume":"12","author":"Yan","year":"2025","journal-title":"Bioengineering"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3404922","article-title":"RSProtoSeg: high spatial resolution remote sensing images segmentation based on non-learnable prototypes","volume":"62","author":"Sun","year":"2024","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.3390\/healthcare11091222","article-title":"Yolo-based deep learning model for pressure ulcer detection and classification[C]\/\/healthcare","volume":"11","author":"Aldughayfiq","year":"2023","journal-title":"MDPI"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"108874","DOI":"10.1016\/j.bspc.2025.108874","article-title":"X-YOLO: a method for detecting wrist fractures in children based on dynamic feature enhancement and lightweight design","volume":"112","author":"Qiu","year":"2026","journal-title":"Biomed Signal Process Control"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"52461","DOI":"10.1109\/ACCESS.2025.3549839","article-title":"YOLOv8-AM: YOLOv8 based on effective attention mechanisms for pediatric wrist fracture detection. 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