  Purpose: The goal of this work is to use multi-camera video to classify open
surgery tools as well as identify which tool is held in each hand. Multi-camera
systems help prevent occlusions in open surgery video data. Furthermore,
combining multiple views such as a Top-view camera covering the full operative
field and a Close-up camera focusing on hand motion and anatomy, may provide a
more comprehensive view of the surgical workflow. However, multi-camera data
fusion poses a new challenge: a tool may be visible in one camera and not the
other. Thus, we defined the global ground truth as the tools being used
regardless their visibility. Therefore, tools that are out of the image should
be remembered for extensive periods of time while the system responds quickly
to changes visible in the video.
  Methods: Participants (n=48) performed a simulated open bowel repair. A
Top-view and a Close-up cameras were used. YOLOv5 was used for tool and hand
detection. A high frequency LSTM with a 1 second window at 30 frames per second
(fps) and a low frequency LSTM with a 40 second window at 3 fps were used for
spatial, temporal, and multi-camera integration.
  Results: The accuracy and F1 of the six systems were: Top-view (0.88/0.88),
Close-up (0.81,0.83), both cameras (0.9/0.9), high fps LSTM (0.92/0.93), low
fps LSTM (0.9/0.91), and our final architecture the Multi-camera
classifier(0.93/0.94).
  Conclusion: By combining a system with a high fps and a low fps from the
multiple camera array we improved the classification abilities of the global
ground truth.
