Manual Computational Intelligence in Medical Imaging: Techniques and Applications

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Learn more. Volume 77 , Issue 3. The full text of this article hosted at iucr. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account.


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If the address matches an existing account you will receive an email with instructions to retrieve your username. International Statistical Review Volume 77, Issue 3. Editor: Simo Puntanen. Read the full text.

Computational Intelligence in Medical Imaging: Techniques and Applications

Tools Request permission Export citation Add to favorites Track citation. Share Give access Share full text access. Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article. As we look toward the future, ITN chose a select group of physicians, IT experts and its Advisory Board to rank, on a scale from 1 having the most impact on medicine , what existing medical imaging and IT technologies will significantly enhance patient care in the next decade.

AI in Medicine - Medical Imaging Classification (TensorFlow Tutorial)

The results were as follows. Biomarker research in cancer diagnosis and drug development is years ahead of other indication areas. Biomarkers, which are indicators of a particular disease state or a particular state of an organism, used with imaging offers the potential of increased efficiency for drug development. For example, researchers employ tumor uptake of 18F-FDG by PET scanning as an internal decision-making tool in its evaluation of drugs for the treatment of tumors, and examine the close correlation between this uptake and tumor regression using a CT or MRI scanner.

The use of biomarkers in imaging is expected to validate and apply imaging-related biomarkers at all stages of drug and medical device development. Site-specific adhesion molecules such as monoclonal antibodies, peptides, asialoglycoproteins or polysaccharides are incorporated into the shell of the microbubble or liposome to stick to the tumor. After injection into the bloodstream, the adhesion receptors containing the targeted agent accumulates at the targeted site.

Researchers are introducing new agents that track tumor development to accelerate diagnosis and therapies for cancer. The new agent tracks the process of angiogenesis, the formation of new blood vessels in the body, which are formed during wound healing, but also as cancerous tumors recruit blood vessels to sustain accelerated growth. The theory is that a molecular imaging agent that binds to angiogenesis could help physicians locate tumors.

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Imaging the angiogenic process would then enable clinicians to monitor the effectiveness of anti-angiogenic cancer drugs and patient response to drug therapy. Researchers are using genomics and proteomics to pinpoint the biomarkers of genetic mutations and rogue proteins that cause diseases. To visualize this, radiologists are using imaging devices and applying new imaging techniques to detect early diagnosis of disease and for new drug development.

Proteomics is the large-scale study of the structural and functional properties of proteins and their expression. Imaging analyzes protein content in a single cell culture or tissue and identify differences in protein expression. The adoption of genetic and proteomic pathways to disease treatment in drug design has produced a new generation of cytostatic agents that kill cancers without necessarily affecting volume.

Drugs targeted to specific protein kinases require surrogate imaging markers tuned to cell metabolism or proliferation to measure response and calculate optimal dosages.

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One ongoing project conducted by Celera Genomics, which specializes in proteomics and genomics, and GE Healthcare supports the development of novel diagnostic imaging agents for cancer that selectively target cell surface proteins associated with cancer. The objective of this technology is to visualize cancer developing at the cellular level for early diagnosis and optimal treatment decisions. Nanotechnology in medical imaging involves the encapsulation of imaging agents or antigens in cells that are delivered to lymph nodes to locate or treat cancerous tumors.


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  • Nanotechnology-enabled imaging agents will accelerate new drug development by providing information about how potential anticancer drugs reach tumors, gain entry to malignant cells and destroy those cells. Researchers are using novel nanoscale MRI contrast agents made of iron or gadolinium materials which resonate under magnetic energy, sealed in carbon nanotubes. In this case, an external light source, an MRI or PET scanner, triggers the emission of light signals in the body to locate the agents.

    Although CT, MR and PET imaging have brought clinicians closer to the core of cellular events, they just are not sensitive enough to accurately find the smallest tumors. Researchers are hopeful that nanotechnology will bridge the sensitivity gap needed for more precise detection and treatment of disease.

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    I have confidence that as the oncology and physical sciences communities continue to find common scientific ground that there are going to be some surprising advances that will come of this work. Once a patient ingests a signal-emitting pill that focuses on the molecular activity of a tumor, physicians can use a handheld device to view a tumor in real-time as fused images such as CT and MRI scans.

    Sanjiv Gambhir, M. A new method of linguistic algorithm applications, in the field of artificial intelligence, can be used to create intelligent systems of semantic data analysis in medical information systems. Such systems can be based on the methods of structural analysis of medical imaging and are directed at offering possibilities of automatic interpretation and semantic understanding of this type of data.


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