Twinsight Medical builds patient‑specific digital twins of musculoskeletal anatomy using deep‑learning segmentation of CT/MRI scans, enabling surgeons to run biomechanical simulations of implant placement and surgical scenarios. The platform predicts functional outcomes, pain relief, and economic impact to guide optimal implant selection and supports device makers with standardized virtual patient cohorts for pre‑clinical testing.
Funding
Funding not disclosed
Founders
Product
Problem
Orthopedic surgeons often rely on generic implant sizing and limited preoperative planning tools, leading to suboptimal functional outcomes and persistent pain for many patients after joint replacement. The lack of patient-specific biomechanical modeling makes it difficult to predict how different surgical options will affect individual recovery and long‑term joint performance.
Solution
Twinsight Medical creates personalized digital twins of a patient’s musculoskeletal anatomy and biomechanics using static medical imaging combined with deep‑learning algorithms. These virtual replicas enable surgeons to simulate various implant positions and surgical scenarios in a dynamic, patient‑specific environment before entering the operating room. By evaluating predicted functional outcomes and cost implications, clinicians can select the optimal implant and placement strategy for each individual, aiming to improve postoperative quality of life and reduce healthcare expenditures. The platform also supports device manufacturers by providing standardized virtual patient cohorts for pre‑clinical testing and design optimization.
Target Audience
Primary customers are orthopedic surgeons and hospital orthopedic departments performing knee, hip, spine, shoulder, and foot joint replacements, as well as medical device companies seeking virtual testing of implants.
Features
- Automated generation of orthopedic digital twins from CT/MRI scans using deep‑learning‑driven image segmentation
- Biomechanical simulation engine that models joint motion, load distribution, and tissue interaction for multiple implant configurations
- Predictive analytics that estimate functional recovery, pain levels, and health‑economic impact for each simulated scenario
- Surgical planning interface allowing clinicians to visualize and adjust implant positioning on the patient’s virtual model
- Capability to run large‑scale virtual trials with standardized cohorts of digital twins for device design validation
- Integration of simulation results into clinical workflows via exportable reports and compatible imaging formats