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Purpose: Overageing and climate change cause a need for making processes in the operating room wing (OR wing) more efficient. While many promising technologies are available today, traditional OR wings are not designed for seamlessly integrating these aids. To overcome this discrepancy, we present and motivate multiple ideas on how to transform current architectural design strategies. Methods: The presented concepts originate from expert discussions and studies of the available literature, but also from experiences made in the course of daily care delivery. Additionally, a comprehensive evaluation of current and historic OR theatre designs and the problems which are encountered herein has been conducted. Results: We present three innovative concepts regarding the restructuring of traditional OR wing layouts. To achieve better process optimization, hygiene, and energy efficiency, we propose to divide the OR wing into separate "patient", "procedure" and "staff" zones. For better flexibility regarding perioperative needs and technology integration, we propose to use a hexagon shape combined with reconfigurable walls for designing operating rooms. Conclusion: The concepts presented herein provide a solid foundation for further considerations regarding perioperative process optimization and seamless integration of technology into modern OR wing facilities. We aim at expanding on these results to develop a comprehensive vision for the OR wing of the future. [ABSTRACT FROM AUTHOR]

• Peg Transfer data set containing video, kinematic, semantic segmentation and workflow annotation. • Challenge of surgical workflow recognition with different modality. • Comparison of multiple deep learning based recognition methods. Background and objective: In order to be context-aware, computer-assisted surgical systems require accurate, real-time automatic surgical workflow recognition. In the past several years, surgical video has been the most commonly-used modality for surgical workflow recognition. But with the democratization of robot-assisted surgery, new modalities, such as kinematics, are now accessible. Some previous methods use these new modalities as input for their models, but their added value has rarely been studied. This paper presents the design and results of the "PEg TRAnsfer Workflow recognition" (PETRAW) challenge with the objective of developing surgical workflow recognition methods based on one or more modalities and studying their added value. Methods: The PETRAW challenge included a data set of 150 peg transfer sequences performed on a virtual simulator. This data set included videos, kinematic data, semantic segmentation data, and annotations, which described the workflow at three levels of granularity: phase, step, and activity. Five tasks were proposed to the participants: three were related to the recognition at all granularities simultaneously using a single modality, and two addressed the recognition using multiple modalities. The mean application-dependent balanced accuracy (AD-Accuracy) was used as an evaluation metric to take into account class balance and is more clinically relevant than a frame-by-frame score. Results: Seven teams participated in at least one task with four participating in every task. The best results were obtained by combining video and kinematic data (AD-Accuracy of between 93% and 90% for the four teams that participated in all tasks). Conclusion: The improvement of surgical workflow recognition methods using multiple modalities compared with unimodal methods was significant for all teams. However, the longer execution time required for video/kinematic-based methods(compared to only kinematic-based methods) must be considered. Indeed, one must ask if it is wise to increase computing time by 2000 to 20,000% only to increase accuracy by 3%. The PETRAW data set is publicly available at www.synapse.org/PETRAW to encourage further research in surgical workflow recognition. [ABSTRACT FROM AUTHOR]

Abstract: From the time a patient enters the surgical arena until the time they leave, a tremendous data stream is produced. These data pass through various unrelated systems mostly going unnoticed and unrecorded. We have developed a system to integrate these various systems and display the real-time integrated data in an easy to visualize system. The system captures, records and displays data from anesthesia and surgical devices and OR/hospital information systems, consolidating information on a single operating room display. The system will provide improved contextual real-time monitoring of physiological data, real-time access to readiness information and improved patient identity management. [Copyright &y& Elsevier]

Abstract: The rising cost of health care provision coupled with constrained reimbursements in North America drive the need to use operating rooms as effectively as possible. We created an OR of the Future demonstration project to assess the impact of a ‘parallel processing’ perioperative system for patient management and OR preparation. Running traditionally sequential activities in parallel creates substantial time savings and potential for additional throughput. We measured the performance of the new system as a function of time, and found that the new perioperative system was both easy to learn and durable over time. Comparison of the OR of the Future to another hospital''s perioperative systems design effort shows that there are many ways to establish the parallel flow needed to enhance OR throughput. [Copyright &y& Elsevier]

• Micro-surgical anastomosis data set containing video, kinematic and workflow annotation. • Challenge of surgical workflow recognition at different granularity levels. • Comparison of multiple deep learning based recognition methods. Background and Objective: Automatic surgical workflow recognition is an essential step in developing context-aware computer-assisted surgical systems. Video recordings of surgeries are becoming widely accessible, as the operational field view is captured during laparoscopic surgeries. Head and ceiling mounted cameras are also increasingly being used to record videos in open surgeries. This makes videos a common choice in surgical workflow recognition. Additional modalities, such as kinematic data captured during robot-assisted surgeries, could also improve workflow recognition. This paper presents the design and results of the MIcro-Surgical Anastomose Workflow recognition on training sessions (MISAW) challenge whose objective was to develop workflow recognition models based on kinematic data and/or videos. Methods: The MISAW challenge provided a data set of 27 sequences of micro-surgical anastomosis on artificial blood vessels. This data set was composed of videos, kinematics, and workflow annotations. The latter described the sequences at three different granularity levels: phase, step, and activity. Four tasks were proposed to the participants: three of them were related to the recognition of surgical workflow at three different granularity levels, while the last one addressed the recognition of all granularity levels in the same model. We used the average application-dependent balanced accuracy (AD-Accuracy) as the evaluation metric. This takes unbalanced classes into account and it is more clinically relevant than a frame-by-frame score. Results: Six teams participated in at least one task. All models employed deep learning models, such as convolutional neural networks (CNN), recurrent neural networks (RNN), or a combination of both. The best models achieved accuracy above 95%, 80%, 60%, and 75% respectively for recognition of phases, steps, activities, and multi-granularity. The RNN-based models outperformed the CNN-based ones as well as the dedicated modality models compared to the multi-granularity except for activity recognition. Conclusion: For high levels of granularity, the best models had a recognition rate that may be sufficient for applications such as prediction of remaining surgical time. However, for activities, the recognition rate was still low for applications that can be employed clinically. The MISAW data set is publicly available at http://www.synapse.org/MISAW to encourage further research in surgical workflow recognition. [ABSTRACT FROM AUTHOR]