{"title":"Automatic Hardy and Clapham's classification of hallux sesamoid position on foot radiographs using deep neural network.","authors":"Ryutaro Takeda, Akihiro Uchio, Toshiko Iidaka, Kenta Makabe, Taro Kasai, Yasunori Omata, Noriko Yoshimura, Sakae Tanaka, Takumi Matsumoto","doi":"10.1016/j.fas.2024.10.002","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>There is currently no deep neural network (DNN) capable of automatically classifying tibial sesamoid position (TSP) on foot radiographs.</p><p><strong>Methods: </strong>A DNN was developed to predict TSP according to the Hardy and Clapham's classification. A total of 1554 foot radiographs were used for model development. The validation of the model was conducted using radiographs obtained from 113 consecutive first-visit patients of our foot and ankle clinic. On these 113 radiographs, TSP was independently classified by three foot and ankle surgeons and the DNN, and their results were compared. The weighted kappa value of TSP between the DNN prediction and the median of the three surgeons (K<sub>AI</sub>) was calculated.</p><p><strong>Results: </strong>The K<sub>AI</sub> was 0.95 (95 %CI: 0.85- 1.00), indicating sufficient consistency between the surgeons and the DNN.</p><p><strong>Conclusions: </strong>We have developed a DNN for automated TSP classification that demonstrates sufficient consistency with foot and ankle surgeons.</p><p><strong>Levels of evidence: </strong>Level 3 - Retrospective Cohort Study.</p>","PeriodicalId":48743,"journal":{"name":"Foot and Ankle Surgery","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Foot and Ankle Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.fas.2024.10.002","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
引用次数: 0
Abstract
Background: There is currently no deep neural network (DNN) capable of automatically classifying tibial sesamoid position (TSP) on foot radiographs.
Methods: A DNN was developed to predict TSP according to the Hardy and Clapham's classification. A total of 1554 foot radiographs were used for model development. The validation of the model was conducted using radiographs obtained from 113 consecutive first-visit patients of our foot and ankle clinic. On these 113 radiographs, TSP was independently classified by three foot and ankle surgeons and the DNN, and their results were compared. The weighted kappa value of TSP between the DNN prediction and the median of the three surgeons (KAI) was calculated.
Results: The KAI was 0.95 (95 %CI: 0.85- 1.00), indicating sufficient consistency between the surgeons and the DNN.
Conclusions: We have developed a DNN for automated TSP classification that demonstrates sufficient consistency with foot and ankle surgeons.
Levels of evidence: Level 3 - Retrospective Cohort Study.
期刊介绍:
Foot and Ankle Surgery is essential reading for everyone interested in the foot and ankle and its disorders. The approach is broad and includes all aspects of the subject from basic science to clinical management. Problems of both children and adults are included, as is trauma and chronic disease. Foot and Ankle Surgery is the official journal of European Foot and Ankle Society.
The aims of this journal are to promote the art and science of ankle and foot surgery, to publish peer-reviewed research articles, to provide regular reviews by acknowledged experts on common problems, and to provide a forum for discussion with letters to the Editors. Reviews of books are also published. Papers are invited for possible publication in Foot and Ankle Surgery on the understanding that the material has not been published elsewhere or accepted for publication in another journal and does not infringe prior copyright.