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Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">An endoscopic images\u2010based nasopharyngeal malignancy detection model (eNPM\u2010DM) consisting of a fully convolutional network based on the inception architecture was developed and fine\u2010tuned using separate training and validation sets for both classification and segmentation. Briefly, a total of 28,966 qualified images were collected. Among these images, 27,536 biopsy\u2010proven images from 7951 individuals obtained from January 1st, 2008, to December 31st, 2016, were split into the training, validation and test sets at a ratio of 7:1:2 using simple randomization. Additionally, 1430 images obtained from January 1st, 2017, to March 31st, 2017, were used as a prospective test set to compare the performance of the established model against oncologist evaluation. The dice similarity coefficient (DSC) was used to evaluate the efficiency of eNPM\u2010DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images, by comparing automatic segmentation with manual segmentation performed by the experts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">All images were histopathologically confirmed, and included 5713 (19.7%) normal control, 19,107 (66.0%) nasopharyngeal carcinoma (NPC), 335 (1.2%) NPC and 3811 (13.2%) benign diseases. The eNPM\u2010DM attained an overall accuracy of 88.7% (95% confidence interval (CI) 87.8%\u201389.5%) in detecting malignancies in the test set. In the prospective comparison phase, eNPM\u2010DM outperformed the experts: the overall accuracy was 88.0% (95% CI 86.1%\u201389.6%) vs. 80.5% (95% CI 77.0%\u201384.0%). The eNPM\u2010DM required less time (40 s vs. 110.0 \u00b1 5.8 min) and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background, with an average DSC of 0.78 \u00b1 0.24 and 0.75 \u00b1 0.26 in the test and prospective test sets, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">The eNPM\u2010DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant, and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40880-018-0325-9","type":"journal-article","created":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T10:39:56Z","timestamp":1537871996000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Development and validation of an endoscopic images\u2010based deep learning model for detection with nasopharyngeal malignancies"],"prefix":"10.34133","volume":"38","author":[{"given":"Chaofeng","family":"Li","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center of Cancer Medicine","place":["Guangzhou, P. R. China"]},{"name":"Sun Yat\u2010Sen University Cancer Center","place":["Guangzhou, P. R. China"]},{"name":"Sun Yat\u2010Sen University Cancer Center","place":["Guangzhou, P. R. China"]},{"name":"Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy","place":["Guangzhou, P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingzhong","family":"Jing","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Cancer Medicine","place":["Guangzhou, P. R. China"]},{"name":"Sun Yat\u2010Sen University Cancer Center","place":["Guangzhou, P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangru","family":"Ke","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Cancer Medicine","place":["Guangzhou, P. R. China"]},{"name":"Sun Yat\u2010Sen University Cancer Center","place":["Guangzhou, P. R. 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Chin J Cancer. 2014; 33 (12): 598\u20136034308655","journal-title":"Chin J Cancer"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.21147\/j.issn.1000\u20109604.2016.03.05"}],"container-title":["Cancer Communications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40880-018-0325-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s40880-018-0325-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1186%2Fs40880-018-0325-9","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40880-018-0325-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/spj.science.org\/doi\/pdf\/10.1186\/s40880-018-0325-9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T08:56:20Z","timestamp":1773824180000},"score":1,"resource":{"primary":{"URL":"https:\/\/spj.science.org\/doi\/10.1186\/s40880-018-0325-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,25]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["10.1186\/s40880-018-0325-9"],"URL":"https:\/\/doi.org\/10.1186\/s40880-018-0325-9","relation":{},"ISSN":["2523-3548"],"issn-type":[{"value":"2523-3548","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,25]]},"assertion":[{"value":"2018-01-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-09-04","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-09-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"59"}}