دورية أكاديمية
Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging.
| العنوان: | Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging. |
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| المؤلفون: | Cheong RCT; Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS, London, UK., Jawad S; Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS, London, UK., Adams A; Barts Health NHS Trust, London, UK., Campion T; Barts Health NHS Trust, London, UK., Lim ZH; University College London, London, UK., Papachristou N; Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece., Unadkat S; Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS, London, UK., Randhawa P; Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS, London, UK., Joseph J; Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS, London, UK., Andrews P; Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS, London, UK., Taylor P; University College London, London, UK., Kunz H; University College London, London, UK. h.kunz@ucl.ac.uk.; School of Public Health, Imperial College London, London, UK. h.kunz@ucl.ac.uk. |
| المصدر: | European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery [Eur Arch Otorhinolaryngol] 2024 Apr; Vol. 281 (4), pp. 2153-2158. Date of Electronic Publication: 2024 Jan 10. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Springer International Country of Publication: Germany NLM ID: 9002937 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1434-4726 (Electronic) Linking ISSN: 09374477 NLM ISO Abbreviation: Eur Arch Otorhinolaryngol Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Heidelberg : Springer International, c1990- |
| مواضيع طبية MeSH: | Artificial Intelligence* , Paranasal Sinus Diseases*/diagnostic imaging, Humans ; Machine Learning ; Magnetic Resonance Imaging ; Head |
| مستخلص: | 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. (© 2024. The Author(s).) |
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| فهرسة مساهمة: | Keywords: Artificial intelligence; AutoML; Automated machine learning; MRI; Paranasal sinus disease |
| تواريخ الأحداث: | Date Created: 20240110 Date Completed: 20240318 Latest Revision: 20240402 |
| رمز التحديث: | 20240402 |
| مُعرف محوري في PubMed: | PMC10942883 |
| DOI: | 10.1007/s00405-023-08424-9 |
| PMID: | 38197934 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1434-4726 |
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| DOI: | 10.1007/s00405-023-08424-9 |