scholarly journals Review and Prospect: Artificial Intelligence in Advanced Medical Imaging

2021 ◽  
Vol 1 ◽  
Author(s):  
Shanshan Wang ◽  
Guohua Cao ◽  
Yan Wang ◽  
Shu Liao ◽  
Qian Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Medical Imaging ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Volumetric Imaging ◽  
Positron Emission ◽  
Potential Applications ◽  
Magnetic Resonance Imaging Mri

Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in medical imaging, and its potential applications range from data acquisition and image reconstruction to image analysis and understanding. In this review, we focus on the use of deep learning in image reconstruction for advanced medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). Particularly, recent deep learning-based methods for image reconstruction will be emphasized, in accordance with their methodology designs and performances in handling volumetric imaging data. It is expected that this review can help relevant researchers understand how to adapt AI for medical imaging and which advantages can be achieved with the assistance of AI.

Artificial intelligence in medical imaging: implications for patient radiation safety

2021 ◽  
pp. 20210406
Author(s):  
Jarrel Seah ◽  
Zoe Brady ◽  
Kyle Ewert ◽  
Meng Law
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Medical Imaging ◽  
Radiation Safety ◽  
Tomographic Reconstruction ◽  
Positron Emission ◽  
Learning Techniques ◽  
Current Reconstruction ◽  
Patient Radiation Dose ◽  
Dose Reductions

Artificial Intelligence (AI), including deep learning, is currently revolutionising the field of medical imaging, with far reaching implications for almost every facet of diagnostic imaging, including patient radiation safety. This paper introduces basic concepts in deep learning and provides an overview of its recent history and its application in tomographic reconstruction as well as other applications in medical imaging to reduce patient radiation dose, as well as a brief description of previous tomographic reconstruction techniques. This review also describes the commonly used deep learning techniques as applied to tomographic reconstruction and draws parallels to current reconstruction techniques. Finally, this paper reviews some of the estimated dose reductions in computed tomography (CT) and positron emission tomography (PET) in the recent literature enabled by deep learning, as well as some of the potential problems that may be encountered such as the obscuration of pathology, and highlights the need for additional clinical reader studies from the imaging community.

scholarly journals TAILORING THE NANOPARTICLES SURFACE FOR EFFICIENT CANCER THERAPEUTICS DELIVERY

2020 ◽  
pp. 11-17
Author(s):  
ARUSHI CHAUHAN ◽  
RAVI RANJAN ◽  
PRAMOD AVTI ◽  
ARVIND GULBAKE
Keyword(s):  
Blood Circulation ◽  
Cancer Therapeutics ◽  
Surface Modifications ◽  
Emission Tomography ◽  
Resonance Imaging ◽  
Cancer Disease ◽  
Positron Emission ◽  
Anti Cancer ◽  
Potential Applications ◽  
Magnetic Resonance Imaging Mri

Nanotechnology has tremendous advantages in many areas of scientific as well as clinical research. The development of nanoparticles (NPs) that can efficiently deliver drugs specifically to the cancer cells can help reduce normal cells toxicity and co-morbidities. Cancer can be treated by exploiting the unique physiochemical of the NPs, and modulating their surface modifications using ligands which further could be used as drug cargo vehicles. To enhance biocompatibility and drug delivery towards the target site, various modifications can be included to modify the surface of the NPs, such as carbohydrates, dendrimers, DNA, RNA, siRNA, drugs, and other ligands. These ligand-coated NPs have potential applications in the field of biomedical research, including diagnosis, contrast agents for molecular and clinical imaging (Magnetic Resonance Imaging (MRI), Computed tomography (CT), positron emission tomography (PET)), as cargo vehicles for drugs, increasing the blood circulation half-life, and blood detoxification. Further, the conjugation of anti-cancer drugs to the NPs can be efficiently used to target the cancer disease. This review highlights some of the features and surface modification strategies of the NPs, such as an iron oxide (IO), liposomes (LP)-based NPs, and polymer-based NPs, which show their effectiveness as cargo agents for cancer therapeutics.

scholarly journals Application of Artificial Intelligence in Cardiovascular Imaging

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Panjiang Ma ◽  
Qiang Li ◽  
Jianbin Li
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Cardiovascular Imaging ◽  
Development Trend ◽  
Imaging Data ◽  
Label Information

During the last two decades, as computer technology has matured and business scenarios have diversified, the scale of application of computer systems in various industries has continued to expand, resulting in a huge increase in industry data. As for the medical industry, huge unstructured data has been accumulated, so exploring how to use medical image data more effectively to efficiently complete diagnosis has an important practical impact. For a long time, China has been striving to promote the process of medical informatization, and the combination of big data and artificial intelligence and other advanced technologies in the medical field has become a hot industry and a new development trend. This paper focuses on cardiovascular diseases and uses relevant deep learning methods to realize automatic analysis and diagnosis of medical images and verify the feasibility of AI-assisted medical treatment. We have tried to achieve a complete diagnosis of cardiovascular medical imaging and localize the vulnerable lesion area. (1) We tested the classical object based on a convolutional neural network and experiment, explored the region segmentation algorithm, and showed its application scenarios in the field of medical imaging. (2) According to the data and task characteristics, we built a network model containing classification nodes and regression nodes. After the multitask joint drill, the effect of diagnosis and detection was also enhanced. In this paper, a weighted loss function mechanism is used to improve the imbalance of data between classes in medical image analysis, and the effect of the model is enhanced. (3) In the actual medical process, many medical images have the label information of high-level categories but lack the label information of low-level lesions. The proposed system exposes the possibility of lesion localization under weakly supervised conditions by taking cardiovascular imaging data to resolve these issues. Experimental results have verified that the proposed deep learning-enabled model has the capacity to resolve the aforementioned issues with minimum possible changes in the underlined infrastructure.

scholarly journals ESR white paper: blockchain and medical imaging

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
◽  
Elmar Kotter ◽  
Luis Marti-Bonmati ◽  
Adrian P. Brady ◽  
Nandita M. Desouza
Keyword(s):  
Medical Imaging ◽  
Image Data ◽  
Distributed Database ◽  
Relevant Information ◽  
White Paper ◽  
Imaging Data ◽  
Data Set ◽  
Blockchain Technology ◽  
Potential Applications ◽  
Basic Technology

AbstractBlockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different “authors” including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

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