This paper introduces the smart tissue anastomosis robot (STAR). Currently, the STAR is a proof-of-concept for a vision-guided robotic system featuring an actuated laparoscopic suturing tool capable of executing running sutures from image-based commands. The STAR tool is designed around a commercially available laparoscopic suturing tool that is attached to a custom-made motor stage and the STAR supervisory control architecture that enables a surgeon to select and track incisions and the placement of stitches. The STAR supervisory-control interface provides two modes: A manual mode that enables a surgeon to specify the placement of each stitch and an automatic mode that automatically computes equally-spaced stitches based on an incision contour. Our experiments on planar phantoms demonstrate that the STAR in either mode is more accurate, up to four times more consistent and five times faster than surgeons using state-of-the-art robotic surgical system, four times faster than surgeons using manual Endo360(°)®, and nine times faster than surgeons using manual laparoscopic tools.

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Contactless hand-tracking technology as a surgical master can execute simple surgical tasks. Whereas traditional master controllers outperformed, given that contactless hand-tracking is a first-generation technology, clinical potential is promising and could become a reality with some technical improvements.

This paper specifies and evaluates the accuracy of the Smart Tissue Anastomosis Robot (STAR). The STAR is a proof of concept vision-guided robotic system equipped with an actuated laparoscopic suturing tool and a multispectral vision system. The STAR supports image-based suturing commands and is capable of detecting near-infrared fluorescent (NIRF) markers that provide reliable visual segmentation and tracking. The paper reports the best case scenario accuracy specifications of the STAR as derived from its configuration and calibration parameters. We also evaluate experimentally the effects of overlaying NIRF markers on the accuracy of the STAR when these markers are used as the source of image-based commands and we compare these results to the accuracy of the STAR with image-based commands generated from plain color images. Our results demonstrate that the STAR is able to place sutures on a planar phantom with an average accuracy of 0.5 mm with a standard deviation of 0.2 mm and that NIRF markers have no statistically significant adverse effect on the accuracy.

Neuropeptides and neurotransmitters act as intermediaries to transmit impulses from one neuron to another via a synapse. These neuropeptides are also related to nerve degeneration and regeneration during nerve damage. Although there are various neuropeptides, three are associated with neural regeneration in facial nerve damage: calcitonin gene-related peptide (CGRP), galanin, and pituitary adenylyl cyclase-activating peptide (PACAP). Alpha CGRP in facial motoneurons is a signaling factor involved in neuroglial and neuromuscular interactions during regeneration. Thus, it may be a marker for facial nerve regeneration. Galanin is a marker of injured axons rather than nerve regeneration. PACAP has various effects on nerve regeneration by regulating the surrounding cells and providing neurotrophic factors. Thus, it may also be used as a marker for facial nerve regeneration. However, the precise roles of these substances in nerve generation are not yet fully understood. Animal studies have demonstrated that they may act as neuromodulators to promote neurotrophic factors involved in nerve regeneration as they appear early, before changes in the injured cells and their environment. Therefore, they may be markers of nerve regeneration.