Toward Perception-based Anticipation of Cortical Breach During K-wire Fixation of the Pelvis

Take a shot! Natural language control of intelligent robotic X-ray systems in surgery

PURPOSE

The expanding capabilities of surgical systems bring with them increasing complexity in the interfaces that humans use to control them. Robotic C-arm X-ray imaging systems, for instance, often require manipulation of independent axes via joysticks, while higher-level control options hide inside device-specific menus. The complexity of these interfaces hinder "ready-to-hand" use of high-level functions. Natural language offers a flexible, familiar interface for surgeons to express their desired outcome rather than remembering the steps necessary to achieve it, enabling direct access to task-aware, patient-specific C-arm functionality.

METHODS

We present an English language voice interface for controlling a robotic X-ray imaging system with task-aware functions for pelvic trauma surgery. Our fully integrated system uses a large language model (LLM) to convert natural spoken commands into machine-readable instructions, enabling low-level commands like "Tilt back a bit," to increase the angular tilt or patient-specific directions like, "Go to the obturator oblique view of the right ramus," based on automated image analysis.

RESULTS

We evaluate our system with 212 prompts provided by an attending physician, in which the system performed satisfactory actions 97% of the time. To test the fully integrated system, we conduct a real-time study in which an attending physician placed orthopedic hardware along desired trajectories through an anthropomorphic phantom, interacting solely with an X-ray system via voice.

CONCLUSION

Voice interfaces offer a convenient, flexible way for surgeons to manipulate C-arms based on desired outcomes rather than device-specific processes. As LLMs grow increasingly capable, so too will their applications in supporting higher-level interactions with surgical assistance systems.

Stand in surgeon's shoes: virtual reality cross-training to enhance teamwork in surgery

PURPOSE

Teamwork in surgery depends on a shared mental model of success, i.e., a common understanding of objectives in the operating room. A shared model leads to increased engagement among team members and is associated with fewer complications and overall better outcomes for patients. However, clinical training typically focuses on role-specific skills, leaving individuals to acquire a shared model indirectly through on-the-job experience.

METHODS

We investigate whether virtual reality (VR) cross-training, i.elet@tokeneonedotexposure to other roles, can enhance a shared mental model for non-surgeons more directly. Our study focuses on X-ray guided pelvic trauma surgery, a procedure where successful communication depends on the shared model between the surgeon and a C-arm technologist. We present a VR environment supporting both roles and evaluate a cross-training curriculum in which non-surgeons swap roles with the surgeon.

RESULTS

Exposure to the surgical task resulted in higher engagement with the C-arm technologist role in VR, as measured by the mental demand and effort expended by participants ( p < 0.001 ). It also has a significant effect on non-surgeon's mental model of the overall task; novice participants' estimation of the mental demand and effort required for the surgeon's task increases after training, while their perception of overall performance decreases ( p < 0.05 ), indicating a gap in understanding based solely on observation. This phenomenon was also present for a professional C-arm technologist.

CONCLUSION

Until now, VR applications for clinical training have focused on virtualizing existing curricula. We demonstrate how novel approaches which are not possible outside of a virtual environment, such as role swapping, may enhance the shared mental model of surgical teams by contextualizing each individual's role within the overall task in a time- and cost-efficient manner. As workflows grow increasingly sophisticated, we see VR curricula as being able to directly foster a shared model for success, ultimately benefiting patient outcomes through more effective teamwork in surgery.

An autonomous X-ray image acquisition and interpretation system for assisting percutaneous pelvic fracture fixation

PURPOSE

Percutaneous fracture fixation involves multiple X-ray acquisitions to determine adequate tool trajectories in bony anatomy. In order to reduce time spent adjusting the X-ray imager's gantry, avoid excess acquisitions, and anticipate inadequate trajectories before penetrating bone, we propose an autonomous system for intra-operative feedback that combines robotic X-ray imaging and machine learning for automated image acquisition and interpretation, respectively.

METHODS

Our approach reconstructs an appropriate trajectory in a two-image sequence, where the optimal second viewpoint is determined based on analysis of the first image. A deep neural network is responsible for detecting the tool and corridor, here a K-wire and the superior pubic ramus, respectively, in these radiographs. The reconstructed corridor and K-wire pose are compared to determine likelihood of cortical breach, and both are visualized for the clinician in a mixed reality environment that is spatially registered to the patient and delivered by an optical see-through head-mounted display.

RESULTS

We assess the upper bounds on system performance through in silico evaluation across 11 CTs with fractures present, in which the corridor and K-wire are adequately reconstructed. In post hoc analysis of radiographs across 3 cadaveric specimens, our system determines the appropriate trajectory to within 2.8 ± 1.3 mm and 2.7 ± 1.8[Formula: see text].

CONCLUSION

An expert user study with an anthropomorphic phantom demonstrates how our autonomous, integrated system requires fewer images and lower movement to guide and confirm adequate placement compared to current clinical practice. Code and data are available.