From image-guided surgery to surgeOmics: Facing the era of intelligent automation and robotics
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Digital images do not represent anatomy, they are just a reliable translation from a full-colored and three-dimensional physical world to a grayscale and two dimensions image domain. As a consequence of the imaging technologies disruption in the medical field, a vertiginous “race to see” had been seeking higher definitions. This process got closer and even exceed human naked-eye resolution and the human brain skills to decode gray-scale bidimensional pictures. In the diagnostic field, as a result, the last decade showed important technological advances addressing this issue and computer-aided detection and diagnosis emerged to complement and enhance radiologist frameworks. In this new era methodologies designed to extract more information from images are a must. Radiomics addressed this item using images as datasets, focusing in the region of interest and extracting features, allowing reproducibility, and finally introducing radiology into the quantitative sciences. Technological disruption in medical imaging also found momentum in the therapeutics arena, empowering a bunch of audacious surgeons to perform less invasive procedures with more confidence and precision, finally launching and shaping the image-guided surgery (IGS) discipline. Here instead, there is no “use as data” counter-part and surgeOmics is the first proposed approach. A wide range of semantic and agnostic features can be extracted from the different phases of the IGS’s workflow, like entry point coordinates, angle, distance, target location, amongst the most important ones. In summary, we are coining the surgeOmics neologism to name this approach, which holds great promises to deal with future demands. It has the potential to improve surgeon-hardware interaction and its central hypothesis is that distinctive algorithms using images as data can provide valuable information for personalized and precision surgery in the era of robotics and intelligent automation. SurgeOmics has emerged from IGS, but can be applied to other wide range of medical problems.
Abstract
Digital images do not represent anatomy, they are just a reliable translation from a full-colored and three-dimensional physical world to a grayscale and two dimensions image domain. As a consequence of the imaging technologies disruption in the medical field, a vertiginous “race to see” had been seeking higher definitions. This process got closer and even exceed human naked-eye resolution and the human brain skills to decode gray-scale bidimensional pictures. In the diagnostic field, as a result, the last decade showed important technological advances addressing this issue and computer-aided detection and diagnosis emerged to complement and enhance radiologist frameworks. In this new era methodologies designed to extract more information from images are a must. Radiomics addressed this item using images as datasets, focusing in the region of interest and extracting features, allowing reproducibility, and finally introducing radiology into the quantitative sciences. Technological disruption in medical imaging also found momentum in the therapeutics arena, empowering a bunch of audacious surgeons to perform less invasive procedures with more confidence and precision, finally launching and shaping the image-guided surgery (IGS) discipline. Here instead, there is no “use as data” counter-part and surgeOmics is the first proposed approach. A wide range of semantic and agnostic features can be extracted from the different phases of the IGS’s workflow, like entry point coordinates, angle, distance, target location, amongst the most important ones. In summary, we are coining the surgeOmics neologism to name this approach, which holds great promises to deal with future demands. It has the potential to improve surgeon-hardware interaction and its central hypothesis is that distinctive algorithms using images as data can provide valuable information for personalized and precision surgery in the era of robotics and intelligent automation. SurgeOmics has emerged from IGS, but can be applied to other wide range of medical problems.
참고문헌 (6)
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