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Engineering >> 2024, Volume 34, Issue 3 doi: 10.1016/j.eng.2023.02.013
From Signal to Knowledge: The Diagnostic Value of Raw Data in the Artificial Intelligence Prediction of Human Data for the First Time
a Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China
b Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing 100191, China
c CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
d Department of Radiology, The First Hospital of Jilin University, Changchun 130021, China
e Neusoft Medical Systems Co. Ltd., Shenyang 110167, China
f Neusoft Research of Intelligent Healthcare Technology Co. Ltd., Shenyang 110167, China
g School of Life Science and Technology, Xidian University, Xi'an 710126, China
h Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an 710126, China
i School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
Abstract
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.
Keywords
Computed tomography ; Diagnosis ; Deep learning ; Lung cancer ; Raw data
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