从信号到知识——基于人工智能的医学影像裸数据诊断价值初探

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-toknowledge 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.

MRI-derived radiomics models for diagnosis,aggressiveness,and prognosis evaluation in prostate cancer

Prostate cancer(PCa)is a pernicious tumor with high heterogeneity,which creates a conundrum for making a precise diagnosis and choosing an optimal treatment approach.Multiparametric magnetic resonance imaging(mp-MRI)with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of PCa.Moreover,using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence(AI)and image data processing.Radiomics acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise evaluation.Radiomics models provide a reliable and noninvasive alternative to aid in precision medicine,demonstrating advantages over traditional models based on clinicopathological parameters.The purpose of this review is to provide an overview of related studies of radiomics in PCa,specifically around the development and validation of radiomics models using MRI-derived image features.The current landscape of the literature,focusing mainly on PCa detection,aggressiveness,and prognosis evaluation,is reviewed and summarized.Rather than studies that exclusively focus on image biomarker identification and method optimization,models with high potential for universal clinical implementation are identified.Furthermore,we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical scenario.This review will encourage researchers to design models based on actual clinical needs,as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics.

The Applications of Artificial Intelligence in Digestive System Neoplasms:A Review

Importance:Digestive system neoplasms(DSNs)are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%.Subjective evaluation of medical images including endoscopic images,whole slide images,computed tomography images,and magnetic resonance images plays a vital role in the clinical practice of DSNs,but with limited performance and increased workload of radiologists or pathologists.The application of artificial intelligence(AI)in medical image analysis holds promise to augment the visual interpretation of medical images,which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.Highlights:We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis,assessment of treatment response,and prognosis prediction on 4 typical DSNs including esophageal cancer,gastric cancer,colorectal cancer,and hepatocellular carcinoma.Conclusion:AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs.Several technical issues should be overcome before its application into clinical practice of DSNs.