{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:29:32Z","timestamp":1791394172139,"version":"4.3.3"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T00:00:00Z","timestamp":1714608000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T00:00:00Z","timestamp":1714608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004702","name":"Universit\u00e0 degli Studi di Genova","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004702","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Arch Otorhinolaryngol"],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Informative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagnosis models. This paper aims to demonstrate the feasibility of AI in providing automatic informative laryngoscopy frame selection also capable of working in real-time providing visual feedback to guide the otolaryngologist during the examination.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Several deep learning models were trained and tested on an internal dataset (n\u2009=\u20095147 images) and then tested on an external test set (n\u2009=\u2009646 images) composed of both white light and narrow band images. Four videos were used to assess the real-time performance of the best-performing model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>ResNet-50, pre-trained with the pretext strategy, reached a precision\u2009=\u200995% vs. 97%, recall\u2009=\u200997% vs, 89%, and the F1-score\u2009=\u200996% vs. 93% on the internal and external test set respectively (p\u2009=\u20090.062). The four testing videos are provided in the supplemental materials.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00405-024-08676-z","type":"journal-article","created":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T13:01:58Z","timestamp":1714654918000},"page":"4255-4264","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning"],"prefix":"10.1007","volume":"281","author":[{"given":"Chiara","family":"Baldini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Adeel","family":"Azam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7699-2291","authenticated-orcid":false,"given":"Claudio","family":"Sampieri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Ioppi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Ruiz-Sevilla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabel","family":"Vilaseca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Berta","family":"Alegre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Tirrito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessia","family":"Pennacchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giorgio","family":"Peretti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Moccia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo S.","family":"Mattos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,2]]},"reference":[{"issue":"3","key":"8676_CR1","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/S00405-009-1121-6","volume":"267","author":"C Piazza","year":"2010","unstructured":"Piazza C, Cocco D, de Benedetto L et al (2010) Narrow band imaging and high definition television in the assessment of laryngeal cancer: a prospective study on 279 patients. Eur Arch Oto-Rhino-Laryngol 267(3):409\u2013414. https:\/\/doi.org\/10.1007\/S00405-009-1121-6","journal-title":"Eur Arch Oto-Rhino-Laryngol"},{"key":"8676_CR2","doi-asserted-by":"publisher","first-page":"1854","DOI":"10.1002\/HED.24849","volume":"39","author":"I Vilaseca","year":"2017","unstructured":"Vilaseca I, Valls-Mateus M, Nogu\u00e9s A et al (2017) Usefulness of office examination with narrow band imaging for the diagnosis of head and neck squamous cell carcinoma and follow-up of premalignant lesions. Head Neck 39:1854\u20131863. https:\/\/doi.org\/10.1002\/HED.24849","journal-title":"Head Neck"},{"key":"8676_CR3","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1056\/NEJMRA2302038","volume":"388","author":"CJ Haug","year":"2023","unstructured":"Haug CJ, Drazen JM (2023) Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med 388:1201\u20131208. https:\/\/doi.org\/10.1056\/NEJMRA2302038","journal-title":"N Engl J Med"},{"key":"8676_CR4","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1002\/OHN.343","volume":"169","author":"C Sampieri","year":"2023","unstructured":"Sampieri C, Baldini C, Azam MA et al (2023) Artificial intelligence for upper aerodigestive tract endoscopy and laryngoscopy: a guide for physicians and state-of-the-art review. Otolaryngol Head Neck Surg 169:811\u2013829. https:\/\/doi.org\/10.1002\/OHN.343","journal-title":"Otolaryngol Head Neck Surg"},{"key":"8676_CR5","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/ISBI.2019.8759511","volume":"2019","author":"A Galdran","year":"2019","unstructured":"Galdran A, Costa P, Campilho A (2019) Real-time informative laryngoscopic frame classification with pre-trained convolutional neural networks. Proc Int Symp Biomed Imag 2019:87\u201390. https:\/\/doi.org\/10.1109\/ISBI.2019.8759511","journal-title":"Proc Int Symp Biomed Imag"},{"key":"8676_CR6","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1007\/s11517-020-02127-7","volume":"58","author":"I Patrini","year":"2020","unstructured":"Patrini I, Ruperti M, Moccia S et al (2020) Transfer learning for informative-frame selection in laryngoscopic videos through learned features. Med Biol Eng Comput 58:1225\u20131238. https:\/\/doi.org\/10.1007\/s11517-020-02127-7","journal-title":"Med Biol Eng Comput"},{"key":"8676_CR7","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1002\/lio2.754","volume":"7","author":"P Yao","year":"2022","unstructured":"Yao P, Witte D, Gimonet H et al (2022) Automatic classification of informative laryngoscopic images using deep learning. Laryngosc Investig Otolaryngol 7:460\u2013466. https:\/\/doi.org\/10.1002\/lio2.754","journal-title":"Laryngosc Investig Otolaryngol"},{"key":"8676_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.cmpb.2018.01.030","volume":"158","author":"S Moccia","year":"2018","unstructured":"Moccia S, Vanone GO, De ME et al (2018) Learning-based classification of informative laryngoscopic frames. Comput Methods Progr Biomed 158:21\u201330. https:\/\/doi.org\/10.1016\/j.cmpb.2018.01.030","journal-title":"Comput Methods Progr Biomed"},{"key":"8676_CR9","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1002\/lary.29960","volume":"132","author":"MA Azam","year":"2022","unstructured":"Azam MA, Sampieri C, Ioppi A et al (2022) Deep learning applied to white light and narrow band imaging videolaryngoscopy: toward real-time laryngeal cancer detection. Laryngoscope 132:1798\u20131806. https:\/\/doi.org\/10.1002\/lary.29960","journal-title":"Laryngoscope"},{"key":"8676_CR10","doi-asserted-by":"publisher","DOI":"10.1002\/LARY.31255","author":"C Sampieri","year":"2024","unstructured":"Sampieri C, Azam MA, Ioppi A et al (2024) Real-time laryngeal cancer boundaries delineation on white light and narrow-band imaging laryngoscopy with deep learning. Laryngoscope. https:\/\/doi.org\/10.1002\/LARY.31255","journal-title":"Laryngoscope"},{"key":"8676_CR11","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.900451","volume":"12","author":"MA Azam","year":"2022","unstructured":"Azam MA, Sampieri C, Ioppi A et al (2022) Videomics of the upper aero-digestive tract cancer: deep learning applied to white light and narrow band imaging for automatic segmentation of endoscopic images. Front Oncol 12:900451. https:\/\/doi.org\/10.3389\/fonc.2022.900451","journal-title":"Front Oncol"},{"key":"8676_CR12","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1002\/lary.28708","volume":"132","author":"ME Dunham","year":"2022","unstructured":"Dunham ME, Kong KA, McWhorter AJ, Adkins LK (2022) Optical biopsy: automated classification of airway endoscopic findings using a convolutional neural network. Laryngoscope 132:S1\u2013S8. https:\/\/doi.org\/10.1002\/lary.28708","journal-title":"Laryngoscope"},{"key":"8676_CR13","unstructured":"Gidaris S, Singh P, Komodakis N (2018) Unsupervised representation learning by predicting image rotations. international conference on learning representations"},{"key":"8676_CR14","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju RR, Cogswell M, Das A et al (2020) Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis 128:336\u2013359. https:\/\/doi.org\/10.1007\/s11263-019-01228-7","journal-title":"Int J Comput Vis"},{"key":"8676_CR15","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29\u201336","journal-title":"Radiology"},{"key":"8676_CR16","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1148\/radiology.148.3.6878708","volume":"148","author":"JA Hanley","year":"1983","unstructured":"Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148:839\u2013843. https:\/\/doi.org\/10.1148\/radiology.148.3.6878708","journal-title":"Radiology"},{"key":"8676_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/S00405-023-08190-8\/FIGURES\/5","volume":"1","author":"P Yao","year":"2023","unstructured":"Yao P, Witte D, German A et al (2023) A deep learning pipeline for automated classification of vocal fold polyps in flexible laryngoscopy. Eur Arch Otorhinolaryngol 1:1\u20138. https:\/\/doi.org\/10.1007\/S00405-023-08190-8\/FIGURES\/5","journal-title":"Eur Arch Otorhinolaryngol"},{"key":"8676_CR18","doi-asserted-by":"publisher","first-page":"2558","DOI":"10.1002\/lary.29595","volume":"131","author":"WK Cho","year":"2021","unstructured":"Cho WK, Lee YJ, Joo HA et al (2021) Diagnostic accuracies of laryngeal diseases using a convolutional neural network-based image classification system. Laryngoscope 131:2558\u20132566. https:\/\/doi.org\/10.1002\/lary.29595","journal-title":"Laryngoscope"},{"key":"8676_CR19","doi-asserted-by":"publisher","first-page":"3129","DOI":"10.1002\/HED.27543","volume":"45","author":"Z You","year":"2023","unstructured":"You Z, Han B, Shi Z et al (2023) Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images. Head Neck 45:3129\u20133145. https:\/\/doi.org\/10.1002\/HED.27543","journal-title":"Head Neck"}],"container-title":["European Archives of Oto-Rhino-Laryngology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00405-024-08676-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00405-024-08676-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00405-024-08676-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T15:46:05Z","timestamp":1721749565000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00405-024-08676-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,2]]},"references-count":19,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["8676"],"URL":"https:\/\/doi.org\/10.1007\/s00405-024-08676-z","relation":{},"ISSN":["0937-4477","1434-4726"],"issn-type":[{"value":"0937-4477","type":"print"},{"value":"1434-4726","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,2]]},"assertion":[{"value":"18 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}]}}