Computational Orthopaedics & Surgical Innovation
We are a research lab led by Professor Stefano A. Bini, MD. We study hip and knee surgery using machine learning, large clinical datasets, biomechanical modeling, and data from surgical robots, with a focus on hip abductor disease and joint replacement.
What we work on
The lab works in five areas, listed below and described in full on the Current research page. They connect: clinical data raises mechanical questions, models inform new techniques, and those techniques generate new data.
01Population-scale evidence
Analyses of routinely collected clinical data to screen for risk factors in musculoskeletal disease, with attention to confounding and study design.
→ 02Computational biomechanics
Patient-specific finite element models of surgical repairs: how a construct shares load, and where it is likely to fail.
→ 03Robotics and intraoperative data
Treating the robot's surgical plan as a prediction and testing it against what the surgeon measures and does.
→ 04AI agents in the clinical workflow
Software the lab builds and uses for evidence synthesis, cohort assembly, and documentation, evaluated against the task it claims to do.
→ 05Surgical technique innovation
Taking anatomic and biomechanical findings to the operating room as new approaches and repair strategies, then measuring them.
→A medication-wide association study of hip and knee osteoarthritis across 50 drugs
Screening dozens of medications at once for their association with osteoarthritis in a federated clinical dataset.
Load-sharing mechanics of scaffold augmentation in gluteus medius repair
How a scaffold redistributes load across a tendon repair, modeled patient by patient.
Native coronal deformity and gap prediction in robotic-assisted knee replacement
Why the robot underpredicts the medial gap as native deformity increases.
The lab takes on undergraduates, medical students, orthopaedic trainees, and postdoctoral collaborators on a rolling basis. See how to join.