A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images
Abstract Nasal endoscopy is crucial for the early detection of nasopharyngeal carcinoma (NPC), but its accuracy relies heavily on the clinician’s expertise, posing challenges for primary healthcare providers. Here, we retrospectively analysed 39,340 nasal endoscopic white-light images from three hig...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01403-2 |
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| _version_ | 1850282197987098624 |
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| author | Yubiao Yue Xinyu Zeng Huanjie Lin Jialong Xu Fan Zhang KeLin Zhou Li Li Zhenzhang Li |
| author_facet | Yubiao Yue Xinyu Zeng Huanjie Lin Jialong Xu Fan Zhang KeLin Zhou Li Li Zhenzhang Li |
| author_sort | Yubiao Yue |
| collection | DOAJ |
| description | Abstract Nasal endoscopy is crucial for the early detection of nasopharyngeal carcinoma (NPC), but its accuracy relies heavily on the clinician’s expertise, posing challenges for primary healthcare providers. Here, we retrospectively analysed 39,340 nasal endoscopic white-light images from three high-incidence NPC centres, utilising eight advanced deep learning models to develop an Internet-enabled smartphone application, “Nose-Keeper”, that can be used for early detection of NPC and five prevalent nasal diseases and assessment of healthy individuals. Our app demonstrated a remarkable overall accuracy of 92.27% (95% Confidence Interval (CI): 90.66%–93.61%). Notably, its sensitivity and specificity in NPC detection achieved 96.39% and 99.91%, respectively, outperforming nine experienced otolaryngologists. Explainable artificial intelligence was employed to highlight key lesion areas, improving Nose-Keeper’s decision-making accuracy and safety. Nose-Keeper can assist primary healthcare providers in diagnosing NPC and common nasal diseases efficiently, offering a valuable resource for people in high-incidence NPC regions to manage nasal cavity health effectively. |
| format | Article |
| id | doaj-art-d630dd36f67941a48ef629871df18e4d |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-d630dd36f67941a48ef629871df18e4d2025-08-20T01:48:03ZengNature Portfolionpj Digital Medicine2398-63522024-12-017111610.1038/s41746-024-01403-2A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic imagesYubiao Yue0Xinyu Zeng1Huanjie Lin2Jialong Xu3Fan Zhang4KeLin Zhou5Li Li6Zhenzhang Li7School of Mathematics and Systems Science, Guangdong Polytechnic Normal UniversityDepartment of Otorhinolaryngology, The Second Affiliated Hospital of Shenzhen UniversityDepartment of Radiology, The Second Affiliated Hospital of Guangzhou Medical UniversitySchool of Biomedical Engineering, Guangzhou Medical UniversityDepartment of science and education, Foshan Sanshui District People’s HospitalDepartment of Otorhinolaryngology, Leizhou People’s HospitalDepartment of Otorhinolaryngology, Leizhou People’s HospitalSchool of Mathematics and Systems Science, Guangdong Polytechnic Normal UniversityAbstract Nasal endoscopy is crucial for the early detection of nasopharyngeal carcinoma (NPC), but its accuracy relies heavily on the clinician’s expertise, posing challenges for primary healthcare providers. Here, we retrospectively analysed 39,340 nasal endoscopic white-light images from three high-incidence NPC centres, utilising eight advanced deep learning models to develop an Internet-enabled smartphone application, “Nose-Keeper”, that can be used for early detection of NPC and five prevalent nasal diseases and assessment of healthy individuals. Our app demonstrated a remarkable overall accuracy of 92.27% (95% Confidence Interval (CI): 90.66%–93.61%). Notably, its sensitivity and specificity in NPC detection achieved 96.39% and 99.91%, respectively, outperforming nine experienced otolaryngologists. Explainable artificial intelligence was employed to highlight key lesion areas, improving Nose-Keeper’s decision-making accuracy and safety. Nose-Keeper can assist primary healthcare providers in diagnosing NPC and common nasal diseases efficiently, offering a valuable resource for people in high-incidence NPC regions to manage nasal cavity health effectively.https://doi.org/10.1038/s41746-024-01403-2 |
| spellingShingle | Yubiao Yue Xinyu Zeng Huanjie Lin Jialong Xu Fan Zhang KeLin Zhou Li Li Zhenzhang Li A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images npj Digital Medicine |
| title | A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images |
| title_full | A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images |
| title_fullStr | A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images |
| title_full_unstemmed | A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images |
| title_short | A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images |
| title_sort | deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images |
| url | https://doi.org/10.1038/s41746-024-01403-2 |
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