Evaluation of Deep Learning-Based Automated Detection of Primary Spine Tumors on MRI Using the Turing Test

doi:10.3389/fonc.2022.814667

	Evaluation of Deep Learning-Based Automated Detection of Primary Spine Tumors on MRI Using the Turing Test
	Ouyang, Hanqiang 1,2,3; Meng, Fanyu 4,5; Liu, Jianfang 6; Song, Xinhang 4; Li, Yuan 6; Yuan, Yuan 6; Wang, Chunjie 6; Lang, Ning 6; Tian, Shuai 6; Yao, Meiyi 4,5; Liu, Xiaoguang 1,2,3; Yuan, Huishu 6; Jiang, Shuqiang 4; Jiang, Liang 1,2,3
	2022-03-11
发表期刊	FRONTIERS IN ONCOLOGY
ISSN	2234-943X
卷号	12 页码:12
摘要	BackgroundRecently, the Turing test has been used to investigate whether machines have intelligence similar to humans. Our study aimed to assess the ability of an artificial intelligence (AI) system for spine tumor detection using the Turing test. MethodsOur retrospective study data included 12179 images from 321 patients for developing AI detection systems and 6635 images from 187 patients for the Turing test. We utilized a deep learning-based tumor detection system with Faster R-CNN architecture, which generates region proposals by Region Proposal Network in the first stage and corrects the position and the size of the bounding box of the lesion area in the second stage. Each choice question featured four bounding boxes enclosing an identical tumor. Three were detected by the proposed deep learning model, whereas the other was annotated by a doctor; the results were shown to six doctors as respondents. If the respondent did not correctly identify the image annotated by a human, his answer was considered a misclassification. If all misclassification rates were >30%, the respondents were considered unable to distinguish the AI-detected tumor from the human-annotated one, which indicated that the AI system passed the Turing test. ResultsThe average misclassification rates in the Turing test were 51.2% (95% CI: 45.7%-57.5%) in the axial view (maximum of 62%, minimum of 44%) and 44.5% (95% CI: 38.2%-51.8%) in the sagittal view (maximum of 59%, minimum of 36%). The misclassification rates of all six respondents were >30%; therefore, our AI system passed the Turing test. ConclusionOur proposed intelligent spine tumor detection system has a similar detection ability to annotation doctors and may be an efficient tool to assist radiologists or orthopedists in primary spine tumor detection.
关键词	spine tumor Turing test deep learning MRI primary tumor
DOI	10.3389/fonc.2022.814667
收录类别	SCI
语种	英语
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000777009300001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18909
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Yuan, Huishu; Jiang, Shuqiang; Jiang, Liang
作者单位	1.Peking Univ Third Hosp, Dept Orthopaed, Beijing, Peoples R China 2.Engn Res Ctr Bone & Joint Precis Med, Beijing, Peoples R China 3.Beijing Key Lab Spinal Dis Res, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Beijing, Peoples R China 6.Peking Univ Third Hosp, Dept Radiol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Ouyang, Hanqiang,Meng, Fanyu,Liu, Jianfang,et al. Evaluation of Deep Learning-Based Automated Detection of Primary Spine Tumors on MRI Using the Turing Test[J]. FRONTIERS IN ONCOLOGY,2022,12:12.
APA	Ouyang, Hanqiang.,Meng, Fanyu.,Liu, Jianfang.,Song, Xinhang.,Li, Yuan.,...&Jiang, Liang.(2022).Evaluation of Deep Learning-Based Automated Detection of Primary Spine Tumors on MRI Using the Turing Test.FRONTIERS IN ONCOLOGY,12,12.
MLA	Ouyang, Hanqiang,et al."Evaluation of Deep Learning-Based Automated Detection of Primary Spine Tumors on MRI Using the Turing Test".FRONTIERS IN ONCOLOGY 12(2022):12.