AI Article Synopsis

  • Various medical fields are focusing on early detection and treatment of diseases, utilizing advancements in radiology and diagnostic imaging.
  • Recent developments include the integration of artificial intelligence and machine learning to enhance imaging accuracy and patient diagnosis.
  • This review explores how collaboration between radiology and omics sciences, particularly in detecting extracellular vesicles, could improve disease diagnosis.

Article Abstract

Currently, several pathologies have corresponding and specific diagnostic and therapeutic branches of interest focused on early and correct detection, as well as the best therapeutic approach. Radiology never ceases to develop newer technologies in order to give patients a clear, safe, early, and precise diagnosis; furthermore, in the last few years diagnostic imaging panoramas have been extended to the field of artificial intelligence (AI) and machine learning. On the other hand, clinical and laboratory tests, like flow cytometry and the techniques found in the "omics" sciences, aim to detect microscopic elements, like extracellular vesicles, with the highest specificity and sensibility for disease detection. If these scientific branches started to cooperate, playing a conjugated role in pathology diagnosis, what could be the results? Our review seeks to give a quick overview of recent state of the art research which investigates correlations between extracellular vesicles and the known radiological features useful for diagnosis.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8698452PMC
http://dx.doi.org/10.3390/biology10121265DOI Listing

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