OBJECTIVE: This work presents a method to assess and prevent tissue trauma in real-time during surgery. BACKGROUND: Tissue trauma occurs routinely during laparoscopic surgery with potentially severe consequences. As such, it is crucial that a surgeon is able to regulate the pressure exerted by surgical instruments. We propose a novel method to assess the onset of tissue trauma by considering the mechanical response of tissue as it is loaded in real-time. METHODS: We conducted a parametric study using a lab-based grasping model and differing load conditions. Mechanical stress-time data were analyzed to characterize the tissue response to grasps. Qualitative and quantitative histological analyses were performed to inspect damage characteristics of the tissue under different load conditions. These were correlated against the mechanical measures to identify the nature of trauma onset with respect to our predictive metric. RESULTS: Results showed increasing tissue trauma with load and a strong correlation with the mechanical response of the tissue. Load rate and load history also showed a clear effect on tissue response. The proposed method for trauma assessment was effective in identifying damage. The metric can be normalized with respect to loading rate and history, making it feasible in the unconstrained environment of intraoperative surgery. SIGNIFICANCE: This work demonstrates that tissue trauma can be predicted using mechanical measures in real-time. Applying this technique to laparoscopic tools has the potential to reduce unnecessary tissue trauma and its associated complications by indicating through user feedback or actively regulating the mechanical impact of surgical instruments.
OBJECTIVE: This work presents a method to assess and prevent tissue trauma in real-time during surgery. BACKGROUND: Tissue trauma occurs routinely during laparoscopic surgery with potentially severe consequences. As such, it is crucial that a surgeon is able to regulate the pressure exerted by surgical instruments. We propose a novel method to assess the onset of tissue trauma by considering the mechanical response of tissue as it is loaded in real-time. METHODS: We conducted a parametric study using a lab-based grasping model and differing load conditions. Mechanical stress-time data were analyzed to characterize the tissue response to grasps. Qualitative and quantitative histological analyses were performed to inspect damage characteristics of the tissue under different load conditions. These were correlated against the mechanical measures to identify the nature of trauma onset with respect to our predictive metric. RESULTS: Results showed increasing tissue trauma with load and a strong correlation with the mechanical response of the tissue. Load rate and load history also showed a clear effect on tissue response. The proposed method for trauma assessment was effective in identifying damage. The metric can be normalized with respect to loading rate and history, making it feasible in the unconstrained environment of intraoperative surgery. SIGNIFICANCE: This work demonstrates that tissue trauma can be predicted using mechanical measures in real-time. Applying this technique to laparoscopic tools has the potential to reduce unnecessary tissue trauma and its associated complications by indicating through user feedback or actively regulating the mechanical impact of surgical instruments.
Authors: Jenifer Barrie; Louise Russell; Adrian J Hood; David G Jayne; Anne Neville; Peter R Culmer Journal: Surg Endosc Date: 2018-03-30 Impact factor: 4.584