Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging

• Published in: European archives of oto-rhino-laryngology Vol. 281; no. 4; pp. 2153 - 2158
• Main Authors: Cheong, Ryan Chin Taw, Jawad, Susan, Adams, Ashok, Campion, Thomas, Lim, Zhe Hong, Papachristou, Nikolaos, Unadkat, Samit, Randhawa, Premjit, Joseph, Jonathan, Andrews, Peter, Taylor, Paul, Kunz, Holger
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024
• Subjects: Head and Neck Surgery; Medicine; Medicine & Public Health; Miscellaneous; Neurosurgery; Otorhinolaryngology; Automated machine learning; Paranasal sinus disease; AutoML; MRI; Artificial intelligence
• ISSN: 0937-4477; 1434-4726
• DOI: 10.1007/s00405-023-08424-9

Purpose Artificial intelligence (AI) in the form of automated machine learning (AutoML) offers a new potential breakthrough to overcome the barrier of entry for non-technically trained physicians. A Clinical Decision Support System (CDSS) for screening purposes using AutoML could be beneficial to ease the clinical burden in the radiological workflow for paranasal sinus diseases. Methods The main target of this work was the usage of automated evaluation of model performance and the feasibility of the Vertex AI image classification model on the Google Cloud AutoML platform to be trained to automatically classify the presence or absence of sinonasal disease. The dataset is a consensus labelled Open Access Series of Imaging Studies (OASIS-3) MRI head dataset by three specialised head and neck consultant radiologists. A total of 1313 unique non-TSE T2w MRI head sessions were used from the OASIS-3 repository. Results The best-performing image classification model achieved a precision of 0.928. Demonstrating the feasibility and high performance of the Vertex AI image classification model to automatically detect the presence or absence of sinonasal disease on MRI. Conclusion AutoML allows for potential deployment to optimise diagnostic radiology workflows and lay the foundation for further AI research in radiology and otolaryngology. The usage of AutoML could serve as a formal requirement for a feasibility study.