RASEC: Rescaling Acquisition Strategy With Energy Constraints Under Fusion Kernel for Active Incision Recommendation in Tracheotomy

Tracheotomy is commonly performed for patients needing prolonged intubation, airway obstruction, and neck injuries. Accurate placement of the incision and the tracheal window is paramount in order to avoid complications. Current surgical technique heavily relies on palpating cartilage landmarks on the neck to place the incision. In order to achieve the accelerated goals of the robot-assisted subtask in a tracheotomy, this paper proposes a novel autonomous palpation-based acquisition strategy -RASEC in the tracheal region, which can interactively predict the next acquisition point to maximize the expected information and minimize the costs of palpation procedure. We employ a Gaussian Process (GP) to model the distribution of hardness and utilize anatomical information as a priori input to guide the point of palpation for medical robots. The dynamic tactile sensor based on the resonant frequency is introduced to measure tissue hardness in the tracheal region by millimeter-scale contact to secure the interaction. We investigate the kernel fusion method to blend the Squared Exponential (SE) kernel with the Ornstein-Uhlenbeck (OU) kernel and optimize the Bayesian optimization search by leveraging the anatomical information of the larynx as a priori knowledge. Moreover, we further regularize the exploration and greed factors. The tactile sensor’s moving distance and the robotic base link’s rotation angle during the incision localization process are considered new factors in the acquisition strategy. Simulation and physical phantom experiments are conducted for comparison with state-of-the-art GP-based exploration approaches. The results show that the sensor’s moving distance was reduced by 53.1
$\%$
and the rotation angle of the base was reduced by 75.2
$\%$
of the previous values without sacrificing overall performance capabilities. The satisfying algorithmic index (average precision 0.932, average recall 0.973, average F1 score 0.952) with fewer central estimation distance errors (0.423 mm) and high resolution (1 mm) indicates the performance of the proposed RASEC in terms of exploration efficiency, cost awareness, and localization accuracy for incision localization and recommendation in real robot-assisted subtask in the tracheotomy procedure.
Note to Practitioners
—This work is well motivated to introduce the Level of Autonomy (LoA) 2 -task-level autonomy, specifically in the context of tracheotomy procedures. The incorporation of robotic palpation techniques aims to provide surgeons with enhanced capabilities for incision recommendations, which directly benefit surgeons to visualize hands-on information and localize the trachea regions more efficiently and further reduce cognitive load. To detect the trachea region for intubation incision without costly ergodic acquisition, this article suggests a highly efficient acquisition strategy utilizing the fusion kernel function and regularized impact factors, eliminating the time consumption for such localization task. The actual clinical value is that our proposed strategy can earn more time for further increasing the probability of patient resuscitation, to facilitate supervised autonomy in the real clinic scene.