TY - JOUR
AU - 
AB - This article has been accepted for publication in IEEE Journal of Biomedical and Health Informatics. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2023.3311628 GENERIC COLORIZED JOURNAL, VOL. XX, NO. XX, XXXX 2017 1 Deep Learning in Surgical Workflow Analysis: A Review of Phase and Step Recognition Kubilay Can Demir, Hannah Schieber, Tobias Weise, Daniel Roth, Matthias May, Andreas Maier, Seung Hee Yang Abstract — Objective: In the last two decades, there has been a OR. Standardization of surgical routines and integration of intelligent growing interest in exploring surgical procedures with statistical systems into the surgical workflow is proposed by Herfarth et al. [6] models to analyze operations at different semantic levels. This to address this problem. Although surgeries are complex procedures, information is necessary for developing context-aware intelligent the same types of operation often have similar patterns, so-called systems, which can assist the physicians during operations, eval- uate procedures afterward or help the management team to effec- surgical phases, which be can analyzed by smart systems [7]. In tively utilize the operating room. The objective is to extract reliable that sense, an intelligent system, 
TI - Deep Learning in Surgical Workflow Analysis: A Review of Phase and Step Recognition
JF - IEEE Journal of Biomedical and Health Informatics
DO - 10.1109/jbhi.2023.3311628
DA - 2023-01-01
UR - https://www.deepdyve.com/lp/unpaywall/deep-learning-in-surgical-workflow-analysis-a-review-of-phase-and-step-cneRXVRfg2
DP - DeepDyve
ER -