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AI in Chronic Rhinosinusitis: A Review of Diagnostic and Surgical Applications
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Lead
This review synthesizes the rapid evolution of artificial intelligence in chronic rhinosinusitis (CRS) care. It details how machine learning and deep learning now support diagnosis, endotyping, therapy selection, and surgical planning, while highlighting that external validation remains the primary barrier to clinical translation PMID: 42785629.
Key Facts
| Domain | AI Application | Key Insight |
|---|---|---|
| Diagnosis | Convolutional Neural Networks (CNNs) | Interpret CT, endoscopy, and histopathology to infer eosinophilic endotype noninvasively PMID: 42785629. |
| Endotyping | Latent-factor & Deep Learning | Resolve molecular subsets, repeatedly implicating IL-5 and mixed type 2/neutrophilic inflammation PMID: 42785629. |
| Therapeutics | Machine Learning | Identifies molecular predictors of biologic benefit and clarifies medical-therapy response PMID: 42785629. |
| Surgery | Predictive Models | Forecast endoscopic sinus surgery success; key predictors include asthma and radiographic opacification PMID: 42785629. |
Why It Matters / Context
Chronic rhinosinusitis is a biologically heterogeneous disease where diagnosis and treatment selection remain imperfect. For over a decade, AI has advanced from classical regression to deep learning and generative models, now touching the entire care pathway. This review is significant because it moves beyond isolated technical achievements to synthesize how these tools integrate into clinical workflows. It highlights a critical tension in modern medical AI: while complex algorithms are increasingly available, they do not consistently outperform parsimonious (simpler) models. Furthermore, the review underscores the urgent need for international consensus on how AI is developed and evaluated, a gap that currently hinders the reliable adoption of these tools in routine practice PMID: 42785629.
Details
The review categorizes AI applications into four present-day capabilities and two emerging frontiers. The following table summarizes the specific capabilities and their clinical implications as described in the source material.
| Capability Area | Specific AI Tools/Methods | Clinical Outcome/Target |
|---|---|---|
| Diagnosis | Machine Learning (Pre-visit data) | Predicts probable CRS before patient visit PMID: 42785629. |
| Diagnosis | Convolutional Networks (CT, Endoscopy, Histopathology) | Infers eosinophilic endotype noninvasively PMID: 42785629. |
| Endotyping | Supervised, Unsupervised, Latent-factor models | Resolves molecularly defined subsets; identifies IL-5 and mixed type 2/neutrophilic inflammation PMID: 42785629. |
| Therapeutics | Machine Learning | Clarifies medical-therapy response; identifies molecular predictors of biologic benefit PMID: 42785629. |
| Surgery | Predictive Models | Forecasts endoscopic sinus surgery success and disease recurrence PMID: 42785629. |
| Surgery | Key Predictors Identified | Asthma, radiographic sinus opacification, eosinophilic/neutrophilic inflammation PMID: 42785629. |
| Emerging | Multimodal Foundation Models | Promise integrated, patient-specific decision support PMID: 42785629. |
| Emerging | Federated Learning & Computer Vision | Enable privacy-preserving data sharing and intraoperative navigation PMID: 42785629. |
Limitations
Critical Limitation: The review explicitly states that external validation remains the principal barrier to clinical translation PMID: 42785629. Additionally, the authors note that complex algorithms do not consistently outperform parsimonious models, suggesting that increased model complexity does not automatically yield superior clinical utility PMID: 42785629. There is also a noted lack of international consensus on how AI is developed and evaluated, which poses a risk for inconsistent quality and applicability across different healthcare systems PMID: 42785629.
Clinical Takeaway
Clinicians should view current AI tools in CRS as promising decision-support aids rather than definitive diagnostic replacements, prioritizing models that have undergone rigorous external validation and recognizing that simpler algorithms may offer comparable performance with greater interpretability.
Full Reference
Abuzeid WM, Mozingo K, Bai J, Turner J, Tan BK, Mueller SK. Artificial Intelligence in Chronic Rhinosinusitis: From the Present to the Future. The Journal of Allergy and Clinical Immunology. In Practice. 2026. PMID: 42785629 [DOI: 10.1016/j.jaip.2026.09.018]