干旱区生态环境知识资源中心(Arid): Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging

Arid

DOI	10.1007/s00405-023-08424-9
	Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging
	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
通讯作者	Kunz, H
来源期刊	EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY
ISSN	0937-4477
EISSN	1434-4726
出版年	2024
卷号	281 期号:4 页码:2153-2158
英文摘要	PurposeArtificial 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.MethodsThe 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.ResultsThe 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.ConclusionAutoML 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.
英文关键词	AutoML Automated machine learning Paranasal sinus disease MRI Artificial intelligence
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001139440100003
WOS关键词	ARTIFICIAL-INTELLIGENCE
WOS类目	Otorhinolaryngology
WOS研究方向	Otorhinolaryngology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403688
推荐引用方式 GB/T 7714	Cheong, Ryan Chin Taw,Jawad, Susan,Adams, Ashok,et al. Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging[J],2024,281(4):2153-2158.
APA	Cheong, Ryan Chin Taw.,Jawad, Susan.,Adams, Ashok.,Campion, Thomas.,Lim, Zhe Hong.,...&Kunz, Holger.(2024).Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging.EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY,281(4),2153-2158.
MLA	Cheong, Ryan Chin Taw,et al."Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging".EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY 281.4(2024):2153-2158.