We're developing a tool that lets surgeons rehearse an upcoming operation using the patient's own imaging — not a generic anatomical model, but the actual anatomy they'll be operating on.
CT and MRI scans are read as a stack of 2D slices. Turning that into a working 3D picture of a specific patient's vessel paths, tissue planes, and danger zones falls on the surgeon's own experience — before they've ever seen the anatomy in motion.
Structures that matter most aren't always easy to see or track from 2D imaging alone, and that gap shows up first in the OR, not before it.
Scan → radiologist read → mental 3D reconstruction → OR
No step in that chain lets a surgeon see, explore, or rehearse against the patient's actual anatomy before the day of surgery.
We use computer vision and machine learning to process a patient's pre-operative CT or MRI and reconstruct it into a detailed, explorable 3D model — highlighting the structures that matter most for that specific case.
The patient's own CT or MRI, taken as part of standard pre-operative workup.
Vessels, tissue planes, and the defect or target site are identified from the imaging.
Those structures are assembled into an explorable 3D reconstruction of that patient's anatomy.
The surgeon reviews the model ahead of the case — same anatomy, same defect, before the OR.
Our first focus is sarcoma resection, developed in close collaboration with a practicing general surgeon whose clinical feedback has directly shaped the concept — including tumor margin visualization, proximity to vessels and nerves, and resection planning ahead of the operation.
The same case-specific model is also being explored as a training tool — giving surgical trainees a way to rehearse against real, patient-derived anatomy under supervision, not just idealized or generic cases.
This is early-stage. We're currently developing the core imaging pipeline and working toward a first proof of concept, guided directly by feedback from a practicing general surgeon.
Background in biochemistry and economics, with experience in biopharma and healthcare finance.
Background in computer engineering and economics, with experience as a software engineer at a FAANG company.
