Postoperative outcome analysis of chronic rhinosinusitis using transfer learning with pre-trained foundation models based on endoscopic images: a multicenter, observational study
Abstract Background This study developed a foundation model-based analytical framework for the analysis of postoperative endoscopic images in chronic rhinosinusitis (CRS). The framework leverages the standardized identification and reproducible results enabled by artificial intelligence algorithms,...
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BMC
2025-07-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | https://doi.org/10.1186/s12938-025-01428-y |
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| author | Wentao Gong Keguang Chen Xiao Chen Xueli Liu Zhen Li Li Wang Yuxuan Shi Quan Liu Xicai Sun Xinsheng Huang Xu Luo Hongmeng Yu |
| author_facet | Wentao Gong Keguang Chen Xiao Chen Xueli Liu Zhen Li Li Wang Yuxuan Shi Quan Liu Xicai Sun Xinsheng Huang Xu Luo Hongmeng Yu |
| author_sort | Wentao Gong |
| collection | DOAJ |
| description | Abstract Background This study developed a foundation model-based analytical framework for the analysis of postoperative endoscopic images in chronic rhinosinusitis (CRS). The framework leverages the standardized identification and reproducible results enabled by artificial intelligence algorithms, combined with the strengths of pre-trained foundation models in developing downstream applications. This approach effectively addresses the inherent challenge of strong subjectivity in conventional postoperative endoscopic evaluation for CRS. Methods The postoperative sinus cavity status in CRS was classified into three states: "polyp", "edema", and "smooth", to establish an endoscopic image dataset. Using transfer learning based on pre-trained large models for endoscopic images, we developed an analytical model for postoperative outcome evaluation in CRS. Comparative studies with various traditional training methods were conducted to evaluate this approach, demonstrating that it can achieve satisfactory model performance even with limited datasets. Results The endoscopic image-based pre-trained transfer learning model proposed in this study demonstrates significant advantages over conventional methods in diagnostic performance. In the precision evaluation for distinguishing smooth mucosa from rest conditions (edema and polyps), our model achieved mean accuracy and AUC values of 91.17% and 0.97, respectively, with specificity reaching 86.35% and sensitivity attaining 91.85%. This represents an approximate 4% improvement in mean accuracy compared to traditional algorithms. Notably, in the differential diagnosis between polyps and rest conditions (smooth mucosa and edema), the proposed algorithm attained mean accuracy and AUC values of 81.87% and 0.90, respectively, demonstrating specificity of 80.53% and sensitivity of 81.04%. This configuration shows a substantial 15% enhancement in mean accuracy relative to conventional diagnostic approaches. Conclusion The transfer learning algorithm model based on pre-trained foundation models can provide accurate and reproducible analysis of postoperative outcomes in CRS, effectively addressing the issue of high subjectivity in postoperative evaluation. With limited data, our model can achieve better generalization performance compared to traditional algorithms. |
| format | Article |
| id | doaj-art-d5bbd5393c0547e5adce43b4fde1b103 |
| institution | Kabale University |
| issn | 1475-925X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj-art-d5bbd5393c0547e5adce43b4fde1b1032025-08-20T03:43:14ZengBMCBioMedical Engineering OnLine1475-925X2025-07-0124111110.1186/s12938-025-01428-yPostoperative outcome analysis of chronic rhinosinusitis using transfer learning with pre-trained foundation models based on endoscopic images: a multicenter, observational studyWentao Gong0Keguang Chen1Xiao Chen2Xueli Liu3Zhen Li4Li Wang5Yuxuan Shi6Quan Liu7Xicai Sun8Xinsheng Huang9Xu Luo10Hongmeng Yu11School of Health Science and Engineering, University of Shanghai for Science and TechnologyDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan UniversityENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan UniversityENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan UniversityENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan UniversityENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan UniversityENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan UniversityENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan UniversityENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan UniversityDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan UniversityShanghai ZEHNIT Medical Technology Co.,Ltd.School of Health Science and Engineering, University of Shanghai for Science and TechnologyAbstract Background This study developed a foundation model-based analytical framework for the analysis of postoperative endoscopic images in chronic rhinosinusitis (CRS). The framework leverages the standardized identification and reproducible results enabled by artificial intelligence algorithms, combined with the strengths of pre-trained foundation models in developing downstream applications. This approach effectively addresses the inherent challenge of strong subjectivity in conventional postoperative endoscopic evaluation for CRS. Methods The postoperative sinus cavity status in CRS was classified into three states: "polyp", "edema", and "smooth", to establish an endoscopic image dataset. Using transfer learning based on pre-trained large models for endoscopic images, we developed an analytical model for postoperative outcome evaluation in CRS. Comparative studies with various traditional training methods were conducted to evaluate this approach, demonstrating that it can achieve satisfactory model performance even with limited datasets. Results The endoscopic image-based pre-trained transfer learning model proposed in this study demonstrates significant advantages over conventional methods in diagnostic performance. In the precision evaluation for distinguishing smooth mucosa from rest conditions (edema and polyps), our model achieved mean accuracy and AUC values of 91.17% and 0.97, respectively, with specificity reaching 86.35% and sensitivity attaining 91.85%. This represents an approximate 4% improvement in mean accuracy compared to traditional algorithms. Notably, in the differential diagnosis between polyps and rest conditions (smooth mucosa and edema), the proposed algorithm attained mean accuracy and AUC values of 81.87% and 0.90, respectively, demonstrating specificity of 80.53% and sensitivity of 81.04%. This configuration shows a substantial 15% enhancement in mean accuracy relative to conventional diagnostic approaches. Conclusion The transfer learning algorithm model based on pre-trained foundation models can provide accurate and reproducible analysis of postoperative outcomes in CRS, effectively addressing the issue of high subjectivity in postoperative evaluation. With limited data, our model can achieve better generalization performance compared to traditional algorithms.https://doi.org/10.1186/s12938-025-01428-yChronic rhinosinusitisSurgical prognosisTransfer learningPre-trained foundation model |
| spellingShingle | Wentao Gong Keguang Chen Xiao Chen Xueli Liu Zhen Li Li Wang Yuxuan Shi Quan Liu Xicai Sun Xinsheng Huang Xu Luo Hongmeng Yu Postoperative outcome analysis of chronic rhinosinusitis using transfer learning with pre-trained foundation models based on endoscopic images: a multicenter, observational study BioMedical Engineering OnLine Chronic rhinosinusitis Surgical prognosis Transfer learning Pre-trained foundation model |
| title | Postoperative outcome analysis of chronic rhinosinusitis using transfer learning with pre-trained foundation models based on endoscopic images: a multicenter, observational study |
| title_full | Postoperative outcome analysis of chronic rhinosinusitis using transfer learning with pre-trained foundation models based on endoscopic images: a multicenter, observational study |
| title_fullStr | Postoperative outcome analysis of chronic rhinosinusitis using transfer learning with pre-trained foundation models based on endoscopic images: a multicenter, observational study |
| title_full_unstemmed | Postoperative outcome analysis of chronic rhinosinusitis using transfer learning with pre-trained foundation models based on endoscopic images: a multicenter, observational study |
| title_short | Postoperative outcome analysis of chronic rhinosinusitis using transfer learning with pre-trained foundation models based on endoscopic images: a multicenter, observational study |
| title_sort | postoperative outcome analysis of chronic rhinosinusitis using transfer learning with pre trained foundation models based on endoscopic images a multicenter observational study |
| topic | Chronic rhinosinusitis Surgical prognosis Transfer learning Pre-trained foundation model |
| url | https://doi.org/10.1186/s12938-025-01428-y |
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