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Intelligent Computing Systems in Biomedical Image Processing

Medical imaging technologies using unorthodox artificial intelligence for early disease diagnoses My research reduces dependence on specialized medical devices, biological and chemical processes and creates new paradigms for low-cost biomarker imaging and clinical diagnoses at the point-of-care. Our method uses neural networks to help physicians provide timely information about the anatomy and structure of the organ and saving time and precious biopsy samples.

In collaboration with Beth Israel Deaconess Medical Center in Boston MA, we investigated use of dark field imaging of capillary bed under the tongue of consenting patients in emergency rooms for diagnosing sepsis a blood borne bacterial infection. This approach can rapidly stratify and offer rational use of antibiotics and reduce disease burden in hospital emergency rooms and patients. We successfully predicted signatures associated with fluorescent porphyrin biomarkers linked with tumors and periodontal diseases from standard white-light photographs of the mouth, thus reducing the need for fluorescent imaging [ 4 ].

Publications High accuracy tumor diagnoses and benchmarking of hematoxylin and eosin stained prostate core biopsy images generated by explainable deep neural networks. Another important factor was the availability of large datasets "big data" , which are needed to train large neural network models such as AUTOMAP.

Because it capitalizes on these and other advances, Zhu says, the technique would not have been possible five years ago or maybe even one year ago.

Top 5 Use Cases for Artificial Intelligence in Medical Imaging

Because of its processing speed, the technique could help in making real-time decisions about imaging protocols while the patient is in the scanner. In those cases, immediate feedback is not available during initial imaging, and a repeat study may be required to better identify a suspected abnormality. AUTOMAP would provide instant image reconstruction to inform the decision-making process during scanning and could prevent the need for additional visits. Notably, the technique could also aid in advancing other artificial intelligence and machine learning applications.

Much of the current excitement surrounding machine learning in clinical imaging is focused on computer-aided diagnostics.

Because these systems rely on high-quality images for accurate diagnostic evaluations, AUTOMAP could play a role in advancing them for future clinical use. More from Computing and Technology. Please sign in to add a comment.

Medical imaging technologies using unorthodox artificial intelligence for early disease diagnoses

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Top 5 Use Cases for Artificial Intelligence in Medical Imaging | IEEE Signal Processing Society

By using our site, you acknowledge that you have read and understand our Privacy Policy and Terms of Use. Home Technology Computer Sciences. March 21, Credit: Athinoula A. Explore further. More information: Bo Zhu et al, Image reconstruction by domain-transform manifold learning, Nature DOI: Provided by Massachusetts General Hospital. This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission.

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