Effectiveness of Artificial Intelligence in detecting sinonasal pathology using clinical imaging modalities: a systematic review.
Authors
Affiliations (7)
Affiliations (7)
- Department of Otolaryngology - Head and Neck Surgery, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece.
- Department of Cancer and Genomics, School of Medicine, University of Birmingham, Birmingham, United Kingdom.
- Department of Otolaryngology, Head and Neck Surgery, University Hospitals of Birmingham, Birmingham, United Kingdom.
- Surgical Innovation Centre, Department of Surgery and Cancer, Imperial College London, St. Mary's Hospital, London, United Kingdom.
- Athens Medical Centre, Marousi and Psychiko Clinic, Athens, Greece.
- Department of Head, School of Medicine, University of Nicosia, Nicosia, Cyprus.
- Department of Head, Neck and Skull Base surgery, Hygeia Hospital, Athens, Greece.
Abstract
Sinonasal pathology can be complex and requires a systematic and meticulous approach. Artificial Intelligence (AI) has the potential to improve diagnostic accuracy and efficiency in sinonasal imaging, but its clinical applicability remains an area of ongoing research. This systematic review evaluates the methodologies and clinical relevance of AI in detecting sinonasal pathology through radiological imaging. Key search terms included "artificial intelligence," "deep learning," "machine learning," "neural network," and "paranasal sinuses,". Abstract and full-text screening was conducted using predefined inclusion and exclusion criteria. Data were extracted on study design, AI architectures used (e.g., Convolutional Neural Networks (CNN), Machine Learning classifiers), and clinical characteristics, such as imaging modality (e.g., Computed Tomography (CT), Magnetic Resonance Imaging (MRI)). A total of 53 studies were analyzed, with 85% retrospective, 68% single-center, and 92.5% using internal databases. CT was the most common imaging modality (60.4%), and chronic rhinosinusitis without nasal polyposis (CRSsNP) was the most studied condition (34.0%). Forty-one studies employed neural networks, with classification as the most frequent AI task (35.8%). Key performance metrics included Area Under the Curve (AUC), accuracy, sensitivity, specificity, precision, and F1-score. Quality assessment based on CONSORT-AI yielded a mean score of 16.0 ± 2. AI shows promise in improving sinonasal imaging interpretation. However, as existing research is predominantly retrospective and single-center, further studies are needed to evaluate AI's generalizability and applicability. More research is also required to explore AI's role in treatment planning and post-treatment prediction for clinical integration.