| Autor: |
Bingxi He, Yu Guo, Yongbei Zhu, Lixia Tong, Boyu Kong, Kun Wang, Caixia Sun, Hailin Li, Feng Huang, Liwei Wu, Meng Wang, Fanyang Meng, Le Dou, Kai Sun, Tong Tong, Zhenyu Liu, Ziqi Wei, Wei Mu, Shuo Wang, Zhenchao Tang, Shuaitong Zhang, Jingwei Wei, Lizhi Shao, Mengjie Fang, Juntao Li, Shouping Zhu, Lili Zhou, Di Dong, Huimao Zhang, Jie Tian |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Engineering, Vol 34, Iss , Pp 60-69 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2095-8099 |
| DOI: |
10.1016/j.eng.2023.02.013 |
| Popis: |
Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology. Modern medical care and imaging technology are becoming increasingly inseparable. However, the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction (from signal to image). Artificial intelligence (AI) technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows. In this prospective study, for the first time, we develop an AI-based signal-to-knowledge diagnostic scheme for lung nodule classification directly from the computed tomography (CT) raw data (the signal). We find that the raw data achieves almost comparable performance with CT, indicating that it is possible to diagnose diseases without reconstructing images. Moreover, the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts (with a gain ranging from 0.01 to 0.12), demonstrating that raw data contains diagnostic information that CT does not possess. Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
