From Signal to Knowledge: The Diagnostic Value of Raw Data in the Artificial Intelligence Prediction of Human Data for the First Time

He, Bing; Guo, Yu; Zhu, Yongbei; Tong, Lixia; Kong, Boyu; Wang, Kun; Sun, Chao; Li, Hailin; Huang, Feng; Wu, Liwei; Wang, Meng; Meng, Fanyang; Dou, Le; Sun, Kai; Tong, Tong; Liu, Zhenyu; Wei, Ziqi; Mu, Wei; Tang, Zhenchao; Zhang, Shuaitong; Wei, Jingwei; Shao, Lizhi; Fang, Minghao; Li, Juntao; Zhu, Sencun; Zhou, Lili; Wang, Shuo; Dong, Di; Zhang, Huimao; Tian, Jie

doi:10.1016/j.eng.2023.02.013

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…As a result, both AI-based and human-based diagnostic processes adopt a signal-to-image-to-knowledge approach. Medical images are prone to information distortion during both the acquisition and reconstruction processes [176]. Consequently, a novel approach to medical image analysis has emerged, emphasizing direct analysis of the raw data.…”

Section: Discussionmentioning

confidence: 99%

Radiomics and Its Feature Selection: A Review

Zhang,

Guo,

Jin

2023

Symmetry

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Medical imaging plays an indispensable role in evaluating, predicting, and monitoring a range of medical conditions. Radiomics, a specialized branch of medical imaging, utilizes quantitative features extracted from medical images to describe underlying pathologies, genetic information, and prognostic indicators. The integration of radiomics with artificial intelligence presents innovative avenues for cancer diagnosis, prognosis evaluation, and therapeutic choices. In the context of oncology, radiomics offers significant potential. Feature selection emerges as a pivotal step, enhancing the clinical utility and precision of radiomics. It achieves this by purging superfluous and unrelated features, thereby augmenting model performance and generalizability. The goal of this review is to assess the fundamental radiomics process and the progress of feature selection methods, explore their applications and challenges in cancer research, and provide theoretical and methodological support for future investigations. Through an extensive literature survey, articles pertinent to radiomics and feature selection were garnered, synthesized, and appraised. The paper provides detailed descriptions of how radiomics is applied and challenged in different cancer types and their various stages. The review also offers comparative insights into various feature selection strategies, including filtering, packing, and embedding methodologies. Conclusively, the paper broaches the limitations and prospective trajectories of radiomics.

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Section: Discussionmentioning

confidence: 99%

Radiomics and Its Feature Selection: A Review

Zhang,

Guo,

Jin

2023

Symmetry

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“…The results demonstrated the superiority of end-to-end optimization, but this study did not skip the 'signal-toimage' process, indicating that there is still room for optimization. Furthermore, we also performed empirical studies using sinograms from 276 patients in authentic clinical scenarios, and our results show that the integration of unprocessed raw data greatly improves the performance of CT models (He et al 2023). In summary, researches dominated by CT images are still performed in the context of partial information loss, and the information distortion inherent in the reconstruction process cannot be retrievable.…”

Section: Introductionmentioning

confidence: 90%

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction

He,

Sun,

et al. 2024

Phys. Med. Biol.

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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 1,994 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 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.

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“…However, traditional image analysis technologies require extensive image preprocessing, such as background determination and elimination, object segmentation, and fragment elimination. ,, Furthermore, extracting information from images is both labor-intensive and time-consuming, with a high risk of artificial errors being introduced due to differences in operator technique . In summary, skipping the image process and going directly from signal to knowledge will hopefully lead to new breakthroughs in AS monitoring . With the development of environmental engineering technology and equipment, in the internet of things (IOT), nodes of the WWTPs set up sensors and combined them with machine learning methods, gradually realizing the intelligent real-time detection of water quality. − Convolutional neural networks (CNNs) have been widely used in computer vision for purposes such as image classification and object detection. , CNN model can be learned without any image preprocessing or custom feature extraction techniques .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Activated Sludge Sedimentation Performance Using Deep Transfer Learning

Wang,

Yang,

Chen

et al. 2024

ACS EST Eng.

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Activated sludge (AS) bulking is a significant challenge in AS processes, and therefore, predicting the settling performance of AS is essential to maintaining the long-term stable operation of wastewater treatment plants (WWTPs). In this study, AS samples taken from 42 WWTPs and three laboratory reactors were imaged and labeled with sludge volume indexes to predict the sedimentation performance of AS based on deep learning models. A tagged AS image database was established with 105,695 images. Comparing five different deep learning algorithms suggested that the ImageNet-trained lightweight MobileNetV3-Large model obtained optimal performance. This model achieved an accuracy of 98.06%, with the F1-Score values for AS nonbulking, limitedbulking, and bulking categories of 98.8, 95.4, and 98.4%, respectively. These findings demonstrate that this model can precisely predict the process of AS transitioning from nonbulking to bulking and provide early warning during the limited-bulking stage to facilitate timely regulation by WWTPs.

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From Signal to Knowledge: The Diagnostic Value of Raw Data in the Artificial Intelligence Prediction of Human Data for the First Time

Cited by 7 publications

References 36 publications

Radiomics and Its Feature Selection: A Review

Radiomics and Its Feature Selection: A Review

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction

Prediction of Activated Sludge Sedimentation Performance Using Deep Transfer Learning

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