{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T08:54:04Z","timestamp":1791968044479,"version":"4.3.4"},"reference-count":32,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:00:00Z","timestamp":1638921600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:00:00Z","timestamp":1638921600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2020YFC2006406"],"award-info":[{"award-number":["2020YFC2006406"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Laryngoscopy, the most common diagnostic method for vocal cord lesions (VCLs), is based mainly on the visual subjective inspection of otolaryngologists. This study aimed to establish a highly objective computer\u2010aided VCLs diagnosis system based on deep convolutional neural network (DCNN) and transfer learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To classify VCLs, our method combined the DCNN backbone with transfer learning on a system specifically finetuned for a laryngoscopy image dataset. Laryngoscopy image database was collected to train the proposed system. The diagnostic performance was compared with other DCNN\u2010based models. Analysis of F1 score and receiver operating characteristic curves were conducted to evaluate the performance of the system.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Beyond the existing VCLs diagnosis method, the proposed system achieved an overall accuracy of 80.23%, an F1 score of 0.7836, and an area under the curve (AUC) of 0.9557 for four fine\u2010grained classes of VCLs, namely, normal, polyp, keratinization, and carcinoma. It also demonstrated robust classification capacity for detecting urgent (keratinization, carcinoma) and non\u2010urgent (normal, polyp), with an overall accuracy of 0.939, a sensitivity of 0.887, a specificity of 0.993, and an AUC of 0.9828. The proposed method also outperformed clinicians in the classification of normal, polyps, and carcinoma at an extremely low time cost.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The VCLs diagnosis system succeeded in using DCNN to distinguish the most common VCLs and normal cases, holding a practical potential for improving the overall diagnostic efficacy in VCLs examinations. The proposed VCLs diagnosis system could be appropriately integrated into the conventional workflow of VCLs laryngoscopy as a highly objective auxiliary method.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.15371","type":"journal-article","created":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T13:07:35Z","timestamp":1637672855000},"page":"432-442","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Vocal cord lesions classification based on deep convolutional neural network and transfer learning"],"prefix":"10.1002","volume":"49","author":[{"given":"Qian","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education School of Optics and Photonics Beijing Institute of Technology  Beijing 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiasen","family":"Wang","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Otolaryngologic Diseases College of Otolaryngology\u2013Head and Neck Surgery Chinese PLA General Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremy Jianshuo\u2010li","family":"Mahr","sequence":"additional","affiliation":[{"name":"Division of Life Sciences Rutgers, The State University of New Jersey  New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"issue":"2","key":"e_1_2_9_2_1","article-title":"Phonosurgery of vocal fold polyps, cysts and nodules is beneficial","volume":"60","author":"Jensen JB","year":"2013","journal-title":"Dan Med 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