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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    With the increasing frequency of daily physical activities among children and adolescents, the incidence of wrist fractures has been rising annually. Without precise and prompt diagnosis, these fractures may remain undetected, potentially leading to complications. Recent advancements in computer-aided diagnosis (CAD) technologies have facilitated the development of sophisticated diagnostic tools, which significantly improve the accuracy of fracture detection. To enhance the capability of detecting pediatric wrist fractures, this study presents the WH-DETR model, specifically designed for pediatric wrist fracture detection. WH-DETR is configured as a DEtection TRansformer framework, an end-to-end object detection algorithm that obviates the need for non-maximum suppression post-processing. To further enhance its performance, this study first introduces a wavelet transform projection module to capture different frequency features from the feature maps extracted by the backbone. This module allows the network to effectively capture multi-scale and multi-frequency information, improving the detection of subtle and complex features in medical images. Secondly, this study designs a hierarchical hybrid matching framework that decouples the prediction tasks of different decoder layers during training, thereby improving the final predictive capabilities of the model. The framework improves prediction robustness while maintaining inference efficiency. Extensive experiments on the GRAZPEDWRI-DX dataset demonstrate that our WH-DETR model achieves state-of-the-art performance with only 43\u00a0M parameters, attaining an\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$ \\text {mAP}_{50} $$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msub>\n                            <mml:mtext>mAP<\/mml:mtext>\n                            <mml:mn>50<\/mml:mn>\n                          <\/mml:msub>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    score of 68.8%, an\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$ \\text {mAP}_{50-90} $$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msub>\n                            <mml:mtext>mAP<\/mml:mtext>\n                            <mml:mrow>\n                              <mml:mn>50<\/mml:mn>\n                              <mml:mo>-<\/mml:mo>\n                              <mml:mn>90<\/mml:mn>\n                            <\/mml:mrow>\n                          <\/mml:msub>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    score of 48.3%, and an F1 score of 64.1%. These results represent improvements of 1.78% in\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$ \\text {mAP}_{50} $$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msub>\n                            <mml:mtext>mAP<\/mml:mtext>\n                            <mml:mn>50<\/mml:mn>\n                          <\/mml:msub>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    , 1.69% in\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$ \\text {mAP}_{50-90} $$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msub>\n                            <mml:mtext>mAP<\/mml:mtext>\n                            <mml:mrow>\n                              <mml:mn>50<\/mml:mn>\n                              <mml:mo>-<\/mml:mo>\n                              <mml:mn>90<\/mml:mn>\n                            <\/mml:mrow>\n                          <\/mml:msub>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    , and 1.75% in F1 score, respectively, over the next best-performing model, highlighting its superior efficiency and robustness in pediatric wrist fracture detection.\n                  <\/jats:p>","DOI":"10.1007\/s10278-025-01512-8","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T12:03:51Z","timestamp":1748606631000},"page":"436-453","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Wavelet Transform and Hierarchical Hybrid Matching for Enhancing End-to-End Pediatric Wrist Fracture Detection"],"prefix":"10.1007","volume":"39","author":[{"given":"Bin","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuliang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiuming","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"key":"1512_CR1","doi-asserted-by":"publisher","unstructured":"Randsborg PH, Gulbrandsen P, \u0160altyt\u0117 Benth J, Sivertsen EA, Hammer OL, Fuglesang HF, \u00c5r\u00f8en A: Fractures in Children: Epidemiology and Activity-Specific Fracture Rates. 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According to the Guangzhou Medical University Medical Ethics Committee, the use of such public data does not require additional ethical approval. All authors adhered to research ethical guidelines and are responsible for the content and conclusions of the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"This study utilized the publicly available GRAZPEDWRI-DX dataset. According to the dataset provider\u2019s statement, all data have obtained informed consent from the original participants and have been appropriately anonymized.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The dataset provider has confirmed that all data releases have obtained the necessary consents, ensuring the protection of participants\u2019 privacy. Therefore, the use of data in this study complies with ethical requirements, and no additional consent for publication is needed.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}