Artificial intelligence in medical imaging

Abstract

With the rapid development of science and technology, the application of artificial intelligence (AI) in various fields is constantly expanding, especially in the field of medical imaging [1]. AI technology is suitable to be applied to standardized digital medical image big data based on digital imaging and communications in medicine protocol and picture archiving and communication system. With the integration of AI technology, this field is undergoing profound transformation, not only improving the accuracy and efficiency of diagnosis, but also significantly reducing the workload of doctors [2]. At present, AI is widely used in medical imaging, including risk modeling and stratification, personalized screening, diagnosis (including classification of molecular pathologic subtypes), treatment response prediction, prognosis prediction, image segmentation, and image quality control. AI can help doctors identify and analyze lesions in various medical images, especially in diseases such as lung, breast, and prostate cancer. The research mainly focuses on the identification of benign and malignant, the measurement of risk factors, prognosis judgment and treatment guidance, and it is increasingly being used in the field of psychoradiology [3]. In addition, AI is also focused on reducing image acquisition time and improving data quality. Through deep learning algorithms, AI can optimize imaging parameters, improve imaging quality, and reduce noise and artifacts.

This special issue of AI includes seven latest studies, which covers artificial intelligence of disease diagnosis and prediction, imaging technology model construction, image segmentation and quality control. Wang et al. [4] used systematic review to summarize the technical methods, clinical applications and existing problems of artificial intelligence in cerebrovascular diseases, they found that the availability of algorithms, reliability of validation, and consistency of evaluation metrics may facilitate better clinical applicability and acceptance. Zhu et al. [5] proposed a diffusion magnetic diffusion magnetic (dMRI) index reconstruction model based on deep learning methods-qIRR-Net and a training framework based on data enhancement and consistency loss, The reconstruction of dMRI index is realized without the influence of signal inhomogeneity, and the model validity is verified on simulated inhomogeneity data and real ultra-high field data, thus promoting the application of ultra-high field dMRI technology in medicine and clinic. Artificial intelligence-assisted compressed sensing is a deep learning technology based on convolutional neural networks which can reconstruct images with ultra-high resolution and reduce noise. On the premise of ensuring the quality of the image, the collection time of the sequence is greatly shortened. In this special issue, The application of assisted compressed sensing technology in 5T MRI by Zhou et al. [6] significantly reduced MRI scanning time, and ensured image quality and diagnostic accuracy. This innovative study can effectively improve clinical work efficiency.

AI technology can play an important role in radiomics, through deep learning models, automatic extraction of a large number of quantitative image features, and combined with patient clinical data, gene expression information, to build high-precision diagnosis and prediction models [7]. Pawan et al. [8]reviewed the research on bone metastasis in prostate cancer from the fields of radiomics, machine learning, and deep learning. They present multiple strategies, including classification/prediction, detection, segmentation, and radiomic methods for evaluating prostate bone metastasis; it provides researchers with systematic learning opportunities for relevant research.

The application of artificial intelligence in the field of medical imaging has shown great potential and advantages. From the optimization of intelligent imaging systems to the processing and analysis of complex images, AI technology is pushing medical imaging to new heights. In the future, AI technology will further promote the development of personalized medicine, remote diagnosis, and interdisciplinary integration.

Bin Huang: Writing—original draft (equal). Bo Gao: Writing—review and editing (lead).

artificial intelligence, deep learning, medical imaging

Professor Bo Gao is a member of the iRADIOLOGY Editorial Board. To minimize bias, he was excluded from all editorial decision-making related to the acceptance of this article for publication. The remaining author declares no conflict of interest.

National Natural Science Foundation of China, Grant/Award Numbers: 81871333, 82260340; Guizhou Province 7th Thousand Innovational and Enterprising Talents, Grant/Award Number: GZQ202007086; 2020 Innovation Group Project of Guizhou Province Educational Commission, Grant/Award Number: KY[2021]017; Guizhou Provincial Science & Technology Projects, Grant/Award Number: ZK[2024] General 194; Guizhou Province Science & Technology Project, Grant/Award Numbers: [2020]4Y159, [2021]430.

Not applicable.

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