Automating Surgical Peg Transfer: Calibration with Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humans

TL;DR

This paper introduces an autonomous surgical system using deep learning and advanced calibration that surpasses human speed, accuracy, and consistency in peg transfer tasks, a standard surgical training exercise.

Contribution

The paper presents a novel autonomous system combining deep learning, 3D printing, and inverse kinematics to outperform humans in surgical peg transfer tasks.

Findings

System achieves human-level accuracy

System is faster and more consistent than humans

Lowest collision rate among tested methods

Abstract

Peg transfer is a well-known surgical training task in the Fundamentals of Laparoscopic Surgery (FLS). While human sur-geons teleoperate robots such as the da Vinci to perform this task with high speed and accuracy, it is challenging to automate. This paper presents a novel system and control method using a da Vinci Research Kit (dVRK) surgical robot and a Zivid depth sensor, and a human subjects study comparing performance on three variants of the peg-transfer task: unilateral, bilateral without handovers, and bilateral with handovers. The system combines 3D printing, depth sensing, and deep learning for calibration with a new analytic inverse kinematics model and a time-minimized motion controller. In a controlled study of 3384 peg transfer trials performed by the system, an expert surgical resident, and 9 volunteers, results suggest that the system achieves accuracy on par with the experienced surgical resident and is significantly faster and more consistent than the surgical resident and volunteers. The system also exhibits the highest consistency and lowest collision rate. To our knowledge, this is the first autonomous system to achieve superhuman performance on a standardized surgical task.