{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T21:33:46Z","timestamp":1776375226098,"version":"3.51.2"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-024-01403-2","type":"journal-article","created":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T22:14:46Z","timestamp":1735596886000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images"],"prefix":"10.1038","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6348-555X","authenticated-orcid":false,"given":"Yubiao","family":"Yue","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanjie","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"KeLin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5504-7197","authenticated-orcid":false,"given":"Zhenzhang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"key":"1403_CR1","doi-asserted-by":"publisher","first-page":"3490","DOI":"10.3390\/cancers13143490","volume":"13","author":"PY Siak","year":"2021","unstructured":"Siak, P. Y., Khoo, A. S.-B., Leong, C. O., Hoh, B.-P. & Cheah, S.-C. Current status and future perspectives about molecular biomarkers of nasopharyngeal carcinoma. Cancers 13, 3490 (2021).","journal-title":"Cancers"},{"key":"1403_CR2","doi-asserted-by":"publisher","first-page":"3976","DOI":"10.7150\/jca.42734","volume":"11","author":"Y Tian","year":"2020","unstructured":"Tian, Y. et al. MiRNAs in radiotherapy resistance of nasopharyngeal carcinoma. J. Cancer 11, 3976\u20133985 (2020).","journal-title":"J. Cancer"},{"key":"1403_CR3","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1002\/cac2.12218","volume":"41","author":"L-L Tang","year":"2021","unstructured":"Tang, L.-L. et al. The Chinese Society of Clinical Oncology (CSCO) clinical guidelines for the diagnosis and treatment of nasopharyngeal carcinoma. Cancer Commun. 41, 1195\u20131227 (2021).","journal-title":"Cancer Commun."},{"key":"1403_CR4","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J. Clin. 71, 209\u2013249 (2021).","journal-title":"CA A Cancer J. Clin."},{"key":"1403_CR5","doi-asserted-by":"publisher","first-page":"e638058","DOI":"10.1155\/2011\/638058","volume":"2011","author":"K Tabuchi","year":"2011","unstructured":"Tabuchi, K., Nakayama, M., Nishimura, B., Hayashi, K. & Hara, A. Early detection of nasopharyngeal carcinoma. Int. J. Otolaryngol. 2011, e638058 (2011).","journal-title":"Int. J. Otolaryngol."},{"key":"1403_CR6","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.ejca.2016.12.009","volume":"73","author":"H Liang","year":"2017","unstructured":"Liang, H. et al. Survival impact of waiting time for radical radiotherapy in nasopharyngeal carcinoma: A large institution-based cohort study from an endemic area. Eur. J. Cancer 73, 48\u201360 (2017).","journal-title":"Eur. J. Cancer"},{"key":"1403_CR7","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.ijrobp.2005.12.003","volume":"65","author":"J Yi","year":"2006","unstructured":"Yi, J. et al. Nasopharyngeal carcinoma treated by radical radiotherapy alone: Ten-year experience of a single institution. Int. J. Radiat. Oncol.*Biol.*Phys. 65, 161\u2013168 (2006).","journal-title":"Int. J. Radiat. Oncol.*Biol.*Phys."},{"key":"1403_CR8","doi-asserted-by":"publisher","first-page":"565","DOI":"10.5732\/cjc.010.10547","volume":"30","author":"S-F Su","year":"2011","unstructured":"Su, S.-F. et al. Treatment outcomes for different subgroups of nasopharyngeal carcinoma patients treated with intensity-modulated radiation therapy. Chin. J. Cancer 30, 565\u2013573 (2011).","journal-title":"Chin. J. Cancer"},{"key":"1403_CR9","doi-asserted-by":"publisher","first-page":"209","DOI":"10.4103\/0973-1482.157334","volume":"12","author":"Z-X Wu","year":"2016","unstructured":"Wu, Z.-X., Xiang, L., Rong, J.-F., He, H.-L. & Li, D. Nasopharyngeal carcinoma with headaches as the main symptom: a potential diagnostic pitfall. J. Cancer Res. Ther. 12, 209 (2016).","journal-title":"J. Cancer Res. Ther."},{"key":"1403_CR10","first-page":"50","volume":"16","author":"B Abdullah","year":"2009","unstructured":"Abdullah, B., Alias, A. & Hassan, S. Challenges in the management of nasopharyngeal carcinoma: a review. Malays. J. Med Sci. 16, 50\u201354 (2009).","journal-title":"Malays. J. Med Sci."},{"key":"1403_CR11","doi-asserted-by":"publisher","DOI":"10.1186\/s13104-017-2990-1","volume":"10","author":"AH Siti-Azrin","year":"2017","unstructured":"Siti-Azrin, A. H., Norsa\u2019adah, B. & Naing, N. N. Prognostic factors of nasopharyngeal carcinoma patients in a tertiary referral hospital: a retrospective cohort study. BMC Res. Notes 10, 705 (2017).","journal-title":"BMC Res. Notes"},{"key":"1403_CR12","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/s00405-011-1665-0","volume":"269","author":"R Balachandran","year":"2012","unstructured":"Balachandran, R. et al. Exploring the knowledge of nasopharyngeal carcinoma among medical doctors at primary health care level in Perak state, Malaysia. Eur. Arch. Otorhinolaryngol. 269, 649\u2013658 (2012).","journal-title":"Eur. Arch. Otorhinolaryngol."},{"key":"1403_CR13","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6920-10-81","volume":"10","author":"R Fles","year":"2010","unstructured":"Fles, R., Wildeman, M. A., Sulistiono, B., Haryana, S. M. & Tan, I. B. Knowledge of general practitioners about nasopharyngeal cancer at the Puskesmas in Yogyakarta, Indonesia. BMC Med. Educ. 10, 81 (2010).","journal-title":"BMC Med. Educ."},{"key":"1403_CR14","doi-asserted-by":"publisher","first-page":"e102353","DOI":"10.1371\/journal.pone.0102353","volume":"9","author":"M Adham","year":"2014","unstructured":"Adham, M. et al. Current status of cancer care for young patients with nasopharyngeal carcinoma in Jakarta, Indonesia. PLoS ONE 9, e102353 (2014).","journal-title":"PLoS ONE"},{"key":"1403_CR15","doi-asserted-by":"publisher","first-page":"e1382","DOI":"10.1634\/theoncologist.2019-0804","volume":"25","author":"LG Qu","year":"2020","unstructured":"Qu, L. G., Brand, N. R., Chao, A. & Ilbawi, A. M. Interventions addressing barriers to delayed cancer diagnosis in low\u2010 and middle\u2010income countries: a systematic review. Oncologist 25, e1382\u2013e1395 (2020).","journal-title":"Oncologist"},{"key":"1403_CR16","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-017-4429-y","volume":"17","author":"R Fles","year":"2017","unstructured":"Fles, R. et al. The role of Indonesian patients\u2019 health behaviors in delaying the diagnosis of nasopharyngeal carcinoma. BMC Public Health 17, 510 (2017).","journal-title":"BMC Public Health"},{"key":"1403_CR17","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1037\/a0024485","volume":"42","author":"DD Luxton","year":"2011","unstructured":"Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C. & Reger, G. M. mHealth for mental health: Integrating smartphone technology in behavioral healthcare. Prof. Psychol. Res. Pract. 42, 505\u2013512 (2011).","journal-title":"Prof. Psychol. Res. Pract."},{"key":"1403_CR18","doi-asserted-by":"publisher","unstructured":"G\u00f6\u00e7eri, E. Impact of deep learning and smartphone technologies in dermatology: automated diagnosis. In Proc. 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA) 1\u20136 (IEEE, 2020). https:\/\/doi.org\/10.1109\/IPTA50016.2020.9286706.","DOI":"10.1109\/IPTA50016.2020.9286706"},{"key":"1403_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-24116-6","volume":"12","author":"Z Li","year":"2021","unstructured":"Li, Z. et al. Preventing corneal blindness caused by keratitis using artificial intelligence. Nat. Commun. 12, 3738 (2021).","journal-title":"Nat. Commun."},{"key":"1403_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-21466-z","volume":"12","author":"W Zhou","year":"2021","unstructured":"Zhou, W. et al. Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images. Nat. Commun. 12, 1259 (2021).","journal-title":"Nat. Commun."},{"key":"1403_CR21","doi-asserted-by":"publisher","first-page":"E2344","DOI":"10.1002\/lary.29302","volume":"131","author":"Z Wu","year":"2021","unstructured":"Wu, Z. et al. Deep learning for classification of pediatric otitis media. Laryngoscope 131, E2344\u2013E2351 (2021).","journal-title":"Laryngoscope"},{"key":"1403_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2022.101543","volume":"51","author":"Y-C Chen","year":"2022","unstructured":"Chen, Y.-C. et al. Smartphone-based artificial intelligence using a transfer learning algorithm for the detection and diagnosis of middle ear diseases: a retrospective deep learning study. eClinicalMedicine 51, 101543 (2022).","journal-title":"eClinicalMedicine"},{"key":"1403_CR23","doi-asserted-by":"publisher","unstructured":"Oztel, I., Oztel, G. Y. & Sahin, V. H. Deep learning-based skin diseases classification using smartphones. Adv. Intell. Syst. https:\/\/doi.org\/10.1002\/aisy.202300211 (2023).","DOI":"10.1002\/aisy.202300211"},{"key":"1403_CR24","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1016\/j.jaad.2021.02.043","volume":"85","author":"H Wu","year":"2021","unstructured":"Wu, H. et al. A deep learning-based smartphone platform for cutaneous lupus erythematosus classification assistance: simplifying the diagnosis of complicated diseases. J. Am. Acad. Dermatol. 85, 792\u2013793 (2021).","journal-title":"J. Am. Acad. Dermatol."},{"key":"1403_CR25","doi-asserted-by":"publisher","unstructured":"Liu, Z. et al. Swin transformer: hierarchical vision transformer using shifted windows. In Proc. 2021 IEEE\/CVF International Conference on Computer Vision (ICCV) 9992\u201310002 (IEEE, 2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1403_CR26","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1038\/s41746-019-0206-x","volume":"3","author":"SP Rowland","year":"2020","unstructured":"Rowland, S. P., Fitzgerald, J. E., Holme, T., Powell, J. & McGregor, A. What is the clinical value of mHealth for patients? NPJ Digit Med. 3, 4 (2020).","journal-title":"NPJ Digit Med."},{"key":"1403_CR27","doi-asserted-by":"publisher","first-page":"100806","DOI":"10.1016\/j.hlpt.2023.100806","volume":"12","author":"GT Aboye","year":"2023","unstructured":"Aboye, G. T., Vande Walle, M., Simegn, G. L. & Aerts, J.-M. Current evidence on the use of mHealth approaches in Sub-Saharan Africa: a scoping review. Health Policy Technol. 12, 100806 (2023).","journal-title":"Health Policy Technol."},{"key":"1403_CR28","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.mattod.2020.07.004","volume":"40","author":"R-Q Li","year":"2020","unstructured":"Li, R.-Q. et al. mHealth: A smartphone-controlled, wearable platform for tumour treatment. Mater. Today 40, 91\u2013100 (2020).","journal-title":"Mater. Today"},{"key":"1403_CR29","doi-asserted-by":"publisher","first-page":"267","DOI":"10.4258\/hir.2021.27.4.267","volume":"27","author":"S Zakerabasali","year":"2021","unstructured":"Zakerabasali, S., Ayyoubzadeh, S. M., Baniasadi, T., Yazdani, A. & Abhari, S. Mobile health technology and healthcare providers: systemic barriers to adoption. Health. Inf. Res. 27, 267\u2013278 (2021).","journal-title":"Health. Inf. Res."},{"key":"1403_CR30","unstructured":"Global smartphone penetration 2016-2022. Statista https:\/\/www.statista.com\/statistics\/203734\/global-smartphone-penetration-per-capita-since-2005\/."},{"key":"1403_CR31","doi-asserted-by":"publisher","unstructured":"Messner, E.-M., Probst, T., O\u2019Rourke, T., Stoyanov, S. & Baumeister, H. mHealth Applications: Potentials, Limitations, Current Quality and Future Directions. In Proc. Digital Phenotyping and Mobile Sensing: New Developments in Psychoinformatics (eds. Baumeister, H. & Montag, C.) 235\u2013248 (Springer International Publishing, Cham, 2019). https:\/\/doi.org\/10.1007\/978-3-030-31620-4_15.","DOI":"10.1007\/978-3-030-31620-4_15"},{"key":"1403_CR32","unstructured":"Rising smartphone usage paves way for ecommerce opportunities in Southeast Asia. EMARKETER https:\/\/www.emarketer.com\/content\/rising-smartphone-usage-paves-way-ecommerce-opportunities-southeast-asia."},{"key":"1403_CR33","doi-asserted-by":"publisher","first-page":"185","DOI":"10.5732\/cjc.011.10328","volume":"31","author":"M Adham","year":"2012","unstructured":"Adham, M. et al. Nasopharyngeal carcinoma in Indonesia: epidemiology, incidence, signs, and symptoms at presentation. Chin. J. Cancer 31, 185\u2013196 (2012).","journal-title":"Chin. J. Cancer"},{"key":"1403_CR34","doi-asserted-by":"publisher","first-page":"5924","DOI":"10.15419\/bmrat.v10i9.830","volume":"10","author":"TN Dung","year":"2023","unstructured":"Dung, T. N. et al. Epstein\u2013Barr virus-encoded RNA expression and its relationship with the clinicopathological parameters of Vietnamese patients with nasopharyngeal carcinoma. Biomed. Res. Ther. 10, 5924\u20135933 (2023).","journal-title":"Biomed. Res. Ther."},{"key":"1403_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3892\/ol.2021.12775","volume":"22","author":"RE Linton","year":"2021","unstructured":"Linton, R. E. et al. Nasopharyngeal carcinoma among the Bidayuh of Sarawak, Malaysia: history and risk factors (Review). Oncol. Lett. 22, 1\u20138 (2021).","journal-title":"Oncol. Lett."},{"key":"1403_CR36","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1002\/ijc.33998","volume":"151","author":"Z Long","year":"2022","unstructured":"Long, Z. et al. Trend of nasopharyngeal carcinoma mortality and years of life lost in China and its provinces from 2005 to 2020. Int. J. Cancer 151, 684\u2013691 (2022).","journal-title":"Int. J. Cancer"},{"key":"1403_CR37","doi-asserted-by":"publisher","first-page":"6365","DOI":"10.2147\/CMAR.S197544","volume":"11","author":"J Lang","year":"2019","unstructured":"Lang, J., Hu, C., Lu, T., Pan, J. & Lin, T. Chinese expert consensus on diagnosis and treatment of nasopharyngeal carcinoma: evidence from current practice and future perspectives. Cancer Manag. Res. 11, 6365\u20136376 (2019).","journal-title":"Cancer Manag. Res."},{"key":"1403_CR38","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s40880-018-0325-9","volume":"38","author":"C Li","year":"2018","unstructured":"Li, C. et al. Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies. Cancer Commun. 38, 59 (2018).","journal-title":"Cancer Commun."},{"key":"1403_CR39","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1002\/lary.29894","volume":"132","author":"J Xu","year":"2022","unstructured":"Xu, J. et al. Deep learning for nasopharyngeal carcinoma identification using both white light and narrow-band imaging endoscopy. Laryngoscope 132, 999\u20131007 (2022).","journal-title":"Laryngoscope"},{"key":"1403_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2023.107463","volume":"26","author":"Z He","year":"2023","unstructured":"He, Z. et al. Deep learning for real-time detection of nasopharyngeal carcinoma during nasopharyngeal endoscopy. iScience 26, 107463 (2023).","journal-title":"iScience"},{"key":"1403_CR41","doi-asserted-by":"publisher","first-page":"12113","DOI":"10.1109\/TPAMI.2023.3275156","volume":"45","author":"P Xu","year":"2023","unstructured":"Xu, P., Zhu, X. & Clifton, D. A. Multimodal learning with transformers: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45, 12113\u201312132 (2023).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1403_CR42","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1038\/s41551-023-01045-x","volume":"7","author":"H-Y Zhou","year":"2023","unstructured":"Zhou, H.-Y. et al. A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics. Nat. Biomed. Eng. 7, 743\u2013755 (2023).","journal-title":"Nat. Biomed. Eng."},{"key":"1403_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107856","volume":"114","author":"H Ayesha","year":"2021","unstructured":"Ayesha, H. et al. Automatic medical image interpretation: state of the art and future directions. Pattern Recognit. 114, 107856 (2021).","journal-title":"Pattern Recognit."},{"key":"1403_CR44","doi-asserted-by":"publisher","unstructured":"Lee, H. M., Okuda, K. S., Gonz\u00e1lez, F. E. & Patel, V. Current Perspectives on Nasopharyngeal Carcinoma. In Proc. Human Cell Transformation: Advances in Cell Models for the Study of Cancer and Aging (eds. Rhim, J. S., Dritschilo, A. & Kremer, R.) 11\u201334 (Springer International Publishing, Cham, 2019). https:\/\/doi.org\/10.1007\/978-3-030-22254-3_2.","DOI":"10.1007\/978-3-030-22254-3_2"},{"key":"1403_CR45","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1002\/lary.24532","volume":"124","author":"S-D Chung","year":"2014","unstructured":"Chung, S.-D., Wu, C.-S., Lin, H.-C. & Hung, S.-H. Association between allergic rhinitis and nasopharyngeal carcinoma: a population-based study. Laryngoscope 124, 1744\u20131749 (2014).","journal-title":"Laryngoscope"},{"key":"1403_CR46","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1002\/lary.24435","volume":"124","author":"S-H Hung","year":"2014","unstructured":"Hung, S.-H., Chen, P.-Y., Lin, H.-C., Ting, J. & Chung, S.-D. Association of rhinosinusitis with nasopharyngeal carcinoma: a population-based study. Laryngoscope 124, 1515\u20131520 (2014).","journal-title":"Laryngoscope"},{"key":"1403_CR47","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1111\/ajco.13464","volume":"17","author":"P-W Huang","year":"2021","unstructured":"Huang, P.-W., Chiou, Y.-R., Wu, S.-L., Liu, J.-C. & Chiou, K.-R. Risk of nasopharyngeal carcinoma in patients with chronic rhinosinusitis: a nationwide propensity score matched study in Taiwan. Asia-Pac. J. Clin. Oncol. 17, 442\u2013447 (2021).","journal-title":"Asia-Pac. J. Clin. Oncol."},{"key":"1403_CR48","doi-asserted-by":"publisher","first-page":"611","DOI":"10.3390\/diagnostics10090611","volume":"10","author":"AA Irekeola","year":"2020","unstructured":"Irekeola, A. A. & Yean Yean, C. Diagnostic and prognostic indications of nasopharyngeal carcinoma. Diagnostics 10, 611 (2020).","journal-title":"Diagnostics"},{"key":"1403_CR49","doi-asserted-by":"publisher","first-page":"2700","DOI":"10.1002\/hed.27466","volume":"45","author":"Y Yuan","year":"2023","unstructured":"Yuan, Y. et al. Early screening of nasopharyngeal carcinoma. Head. Neck 45, 2700\u20132709 (2023).","journal-title":"Head. Neck"},{"key":"1403_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/s11882-016-0607-8","volume":"16","author":"K Dass","year":"2016","unstructured":"Dass, K. & Peters, A. T. Diagnosis and management of rhinosinusitis: highlights from the 2015 practice parameter. Curr. Allergy Asthma Rep. 16, 29 (2016).","journal-title":"Curr. Allergy Asthma Rep."},{"key":"1403_CR51","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1097\/00005537-200301000-00023","volume":"113","author":"N Bhattacharyya","year":"2003","unstructured":"Bhattacharyya, N. & Fried, M. P. The accuracy of computed tomography in the diagnosis of chronic rhinosinusitis. Laryngoscope 113, 125\u2013129 (2003).","journal-title":"Laryngoscope"},{"key":"1403_CR52","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.otohns.2010.04.012","volume":"143","author":"N Bhattacharyya","year":"2010","unstructured":"Bhattacharyya, N. & Lee, L. N. Evaluating the diagnosis of chronic rhinosinusitis based on clinical guidelines and endoscopy. Otolaryngol. Head. Neck Surg. 143, 147\u2013151 (2010).","journal-title":"Otolaryngol. Head. Neck Surg."},{"key":"1403_CR53","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s13223-016-0126-0","volume":"12","author":"IF Nevis","year":"2016","unstructured":"Nevis, I. F., Binkley, K. & Kabali, C. Diagnostic accuracy of skin-prick testing for allergic rhinitis: a systematic review and meta-analysis. Allergy Asthma Clin. Immunol. 12, 20 (2016).","journal-title":"Allergy Asthma Clin. Immunol."},{"key":"1403_CR54","doi-asserted-by":"crossref","unstructured":"M., S., Gopal, S., P.M., R., C.R.K., B. & N., R. A study on the significance of nasal smear eosinophil count and blood absolute eosinophil count in patients with allergic rhinitis of varied severity of symptoms. Indian J. Otolaryngol. Head Neck Surg. 75, 3449\u20133452 (2023).","DOI":"10.1007\/s12070-023-03945-5"},{"key":"1403_CR55","doi-asserted-by":"publisher","first-page":"721851","DOI":"10.3389\/falgy.2021.721851","volume":"2","author":"A Testera-Montes","year":"2021","unstructured":"Testera-Montes, A., Jurado, R., Salas, M., Eguiluz-Gracia, I. & Mayorga, C. Diagnostic tools in allergic rhinitis. Front. Allergy 2, 721851 (2021).","journal-title":"Front. Allergy"},{"key":"1403_CR56","doi-asserted-by":"publisher","first-page":"NP131","DOI":"10.1177\/0145561319871533","volume":"100","author":"N Janovic","year":"2021","unstructured":"Janovic, N., Janovic, A., Milicic, B. & Djuric, M. Is computed tomography imaging of deviated nasal septum justified for obstruction confirmation? Ear Nose Throat J. 100, NP131\u2013NP136 (2021).","journal-title":"Ear Nose Throat J."},{"key":"1403_CR57","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.amjoto.2009.11.003","volume":"32","author":"B Saedi","year":"2011","unstructured":"Saedi, B., Sadeghi, M., Mojtahed, M. & Mahboubi, H. Diagnostic efficacy of different methods in the assessment of adenoid hypertrophy. Am. J. Otolaryngol. 32, 147\u2013151 (2011).","journal-title":"Am. J. Otolaryngol."},{"key":"1403_CR58","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.smrv.2017.06.001","volume":"38","author":"L Pereira","year":"2018","unstructured":"Pereira, L. et al. Prevalence of adenoid hypertrophy: a systematic review and meta-analysis. Sleep. Med. Rev. 38, 101\u2013112 (2018).","journal-title":"Sleep. Med. Rev."},{"key":"1403_CR59","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-022-00793-7","volume":"22","author":"HE Kim","year":"2022","unstructured":"Kim, H. E. et al. Transfer learning for medical image classification: a literature review. BMC Med Imaging 22, 69 (2022).","journal-title":"BMC Med Imaging"},{"key":"1403_CR60","doi-asserted-by":"publisher","unstructured":"Tu, Z. et al. MaxViT: Multi-axis Vision Transformer. In Proc. Computer Vision \u2013 ECCV 2022 (eds. Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G. M. & Hassner, T.) 459\u2013479 (Springer Nature Switzerland, Cham, 2022). https:\/\/doi.org\/10.1007\/978-3-031-20053-3_27.","DOI":"10.1007\/978-3-031-20053-3_27"},{"key":"1403_CR61","doi-asserted-by":"publisher","unstructured":"Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G. & J\u00e9gou, H. Going deeper with Image Transformers. In Proc. 2021 IEEE\/CVF International Conference on Computer Vision (ICCV) 32\u201342 (IEEE, 2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00010.","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"1403_CR62","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. In Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770\u2013778 (IEEE, 2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1403_CR63","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2261\u20132269 (IEEE, 2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243.","DOI":"10.1109\/CVPR.2017.243"},{"key":"1403_CR64","doi-asserted-by":"publisher","unstructured":"Chollet, F. Xception: deep learning with depthwise separable convolutions. In Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1800\u20131807 (IEEE, 2017). https:\/\/doi.org\/10.1109\/CVPR.2017.195.","DOI":"10.1109\/CVPR.2017.195"},{"key":"1403_CR65","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.3390\/diagnostics11081384","volume":"11","author":"Y Dai","year":"2021","unstructured":"Dai, Y., Gao, Y. & Liu, F. TransMed: transformers advance multi-modal medical image classification. Diagnostics 11, 1384 (2021).","journal-title":"Diagnostics"},{"key":"1403_CR66","doi-asserted-by":"publisher","unstructured":"Yu, W. et al. MetaFormer is actually what you need for vision. In Proc. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 10809\u201310819 (IEEE, 2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01055.","DOI":"10.1109\/CVPR52688.2022.01055"},{"key":"1403_CR67","doi-asserted-by":"publisher","unstructured":"Liu, Z. et al. A ConvNet for the 2020s. In Proc. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 11966\u201311976 (IEEE, 2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01167.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"1403_CR68","doi-asserted-by":"publisher","unstructured":"Deng, J. et al. ImageNet: a large-scale hierarchical image database. In Proc. 2009 IEEE Conference on Computer Vision and Pattern Recognition 248\u2013255 (IEEE, 2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1403_CR69","doi-asserted-by":"publisher","unstructured":"Selvaraju, R. R. et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. In Proc. 2017 IEEE International Conference on Computer Vision (ICCV) 618\u2013626 (IEEE, 2017). https:\/\/doi.org\/10.1109\/ICCV.2017.74.","DOI":"10.1109\/ICCV.2017.74"},{"key":"1403_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-020-00380-6","volume":"4","author":"AT Young","year":"2021","unstructured":"Young, A. T. et al. Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models. npj Digit. Med. 4, 1\u20138 (2021).","journal-title":"npj Digit. Med."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01403-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01403-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01403-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:03:28Z","timestamp":1735603408000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01403-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,31]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1403"],"URL":"https:\/\/doi.org\/10.1038\/s41746-024-01403-2","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.09.19.24313954","asserted-by":"object"}]},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,31]]},"assertion":[{"value":"7 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"384"}}