A Comprehensive Review of Computer Vision Techniques to Interest Physicians and Surgeons, Role of A Clinical Biomechanical Engineer in Pre-Operative Surgical Planning, And Preamble To HSG-Amoeba, A New Concept of Biomedical Image Modeling Technique. - Gandhi, Harjeet Singh (INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2022)
Background: The science of computer vision is replication of human vision for pattern recognition and segregation of objects-of-interest at macro- and micro-level. There are numerous computer vision techniques with greater focus on deep learning utilizing artificial neural network. Only few of them can be readily applied to medical images for surgical interventions. Study objective: As this narrative review is aimed at the medical community it is not encumbered with mathematical algorithms, albeit important. Apart from discussion on basic concepts and chronological development of the computer vision techniques the study introduces role of clinical biomechanical engineering team at the time of surgical planning. Methodology: The study literature was searched on Google Scholar, keywords on Google chrome, Wikipedia and cited references in the reviewed articles referring to the original studies describing various computer vision techniques between 1980 to 2021. Result: There is enormous discursive literature to read with extremely variable computer vision terminology unknown to the medical community is densely populated with advanced mathematics leading to lack of interest among majority of the physicians as the end user. There are inconsistencies in the usage of medical terminology and definitions. Comments and conclusion: Standalone image processing and segmentation is meaningless without patient information for clinical applications in daily practice. There is a dire need for streamlining of computer vision science to teach medical community, introduction of a new breed of in-house clinical biomechanical engineers and supplementary residency program for residents to accept it as standard of patient care.