Department of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
Mini Review
Advances in Surgical Phase Recognition: Overcoming Challenges in Structure Segmentation for Rectal Resection Videos
Author(s): Simon Jones*
In the ever-evolving landscape of healthcare, technology has emerged as a powerful catalyst for enhancing the precision and efficacy of medical
procedures. One field where this transformation is particularly noteworthy is rectal surgery. This article explores the promising realm of machine
learning applied to phase and structure recognition in rectal surgery, shedding light on the significant strides being made in surgical phase
recognition within rectal resection videos. We embark on a journey through the convergence of technology and surgical excellence, showcasing
how machine learning, a subset of artificial intelligence, is reshaping the practice of medicine. With its capability to analyze vast datasets, identify
intricate patterns, and make insightful predictions, machine learning stands as a transformative force in the world of rectal surgery, promising a
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DOI:
10.37421/2471-2671.2023.9.67
Archives of Surgical Oncology received 37 citations as per Google Scholar report