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Postoperative outcome analysis of chronic rhinosinusitis using transfer learning with pre-trained foundation models
Wentao Gong1,2, Keguang Chen3, Xiao Chen2
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
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.
