Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction
文献类型：期刊论文
| 作者 | He,Bingxi1,7,8; Sun,Caixia1,7,8; Li,Hailin1,7,8; Wang,Yongbo5; She,Yunlang3; Zhao,Mengmeng3; Fang,Mengjie1,7,8 ; Zhu,Yongbei1,7,8; Wang,Kun7; Liu,Zhenyu7
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| 刊名 | Physics in Medicine & Biology
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| 出版日期 | 2024-03-18 |
| 卷号 | 69期号:7 |
| 关键词 | deep learning sinogram CT scans lung cancer raw data |
| DOI | 10.1088/1361-6560/ad1e7c |
| 通讯作者 | Tian,Jie() |
| 英文摘要 | Abstract Objective. In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of ‘signal-image-knowledge’ has remained unchanged. However, the process of ‘signal to image’ inevitably introduces information distortion, ultimately leading to irrecoverable biases in the ‘image to knowledge’ process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal). Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of ‘human-signal-image’ using the workflow ‘CT-simulated data- reconstructed CT,’ and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data. Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866). Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of ‘signal-to-image’ can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of ‘signal-image-knowledge’, opening up new avenues for more accurate medical diagnostics. |
| 语种 | 英语 |
| WOS记录号 | IOP:PMB_69_7_075015 |
| 出版者 | IOP Publishing |
| 源URL | [http://ir.ia.ac.cn/handle/173211/56916]  |
| 专题 | 自动化研究所_中国科学院分子影像重点实验室 |
| 通讯作者 | Tian,Jie |
| 作者单位 | 1.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, People’s Republic of China 2.Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of China 3.Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People’s Republic of China 4.Neusoft Medical Systems Co. Ltd, Shenyang, People’s Republic of China 5.School of Biomedical Engineering, Southern Medical University, Guangzhou, People’s Republic of China 6.School of Mechanical and Materials Engineering, North China University of Technology, Beijing, People’s Republic of China 7.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of China 8.Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, People’s Republic of China |
| 推荐引用方式 GB/T 7714 | He,Bingxi,Sun,Caixia,Li,Hailin,et al. Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction[J]. Physics in Medicine & Biology,2024,69(7). |
| APA | He,Bingxi.,Sun,Caixia.,Li,Hailin.,Wang,Yongbo.,She,Yunlang.,...&Tian,Jie.(2024).Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.Physics in Medicine & Biology,69(7). |
| MLA | He,Bingxi,et al."Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction".Physics in Medicine & Biology 69.7(2024). |
入库方式： OAI收割
来源：自动化研究所
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