Leduyvo's advanced AI-powered surgical guidance system delivers real-time 3D anatomical mapping, instrument tracking, and predictive alerts during operations. Using multiple RGB-D cameras and sophisticated computer vision algorithms, we create a comprehensive digital twin of the surgical field, empowering surgeons with unprecedented visualization and safety.
Our integrated platform combines cutting-edge computer vision, machine learning, and augmented reality to provide surgeons with unparalleled situational awareness and decision support throughout every procedure.
Advanced multi-camera RGB-D sensing creates a continuously updated three-dimensional reconstruction of the surgical field. Our proprietary fusion algorithms combine depth and color data from strategically positioned cameras to generate a coherent, millimeter-accurate 3D model that updates at 60Hz. The system automatically compensates for tissue deformation, blood pooling, and instrument occlusion, maintaining spatial coherence even during complex manipulations. Surgeons gain a complete understanding of anatomical relationships that would be impossible to visualize through traditional surgical visualization methods.
Our AI-powered object detection and tracking system identifies and follows all surgical instruments in real-time with sub-millimeter precision. Using deep learning models trained on millions of surgical images, the system recognizes over 400 different instrument types and configurations. The tracking algorithm maintains instrument identity even during rapid movements, temporary occlusions, and crowded instrument fields. Trajectory prediction anticipates instrument movement up to 500 milliseconds in advance, enabling proactive safety alerts and guidance. The system provides precise 6-DOF (degrees of freedom) pose estimation for each instrument, allowing for accurate spatial relationship calculations between tools and anatomical structures.
Augmented reality visualization seamlessly integrates pre-operative imaging data with the live surgical view, providing surgeons with X-ray vision into subsurface anatomy. The system performs automatic registration between pre-operative CT/MRI scans and the intraoperative 3D reconstruction, achieving alignment accuracy better than 2mm. Critical structures including blood vessels, nerves, tumors, and organs are highlighted with customizable color-coded overlays that move naturally with tissue during surgery. The AR interface supports multiple visualization modes including transparency blending, edge highlighting, and selective structure display, allowing surgeons to toggle between views based on procedural needs. Depth perception cues ensure overlays appear spatially coherent with the physical anatomy.
Machine learning models continuously analyze surgical activity patterns, instrument trajectories, and anatomical proximity to predict and prevent adverse events before they occur. The system monitors hundreds of risk factors simultaneously, including instrument-to-vessel distance, approach angles to critical structures, unusual tissue appearance changes, and deviations from expected procedural flow. When potential hazards are detected, graduated alerts are delivered through visual, auditory, and haptic channels appropriate to the urgency level. The prediction engine leverages data from thousands of similar procedures to identify subtle risk patterns that might escape human attention. Alert sensitivity is automatically tuned based on procedure type, surgical phase, and individual surgeon preferences to minimize false positives while maximizing safety.
Automated phase recognition and workflow analysis provide context-aware guidance throughout the entire procedure. Our AI models recognize surgical phases with 98.2% accuracy by analyzing instrument usage patterns, anatomical exposure, and procedural milestones. The system provides phase-specific checklists, optimal visualization settings, and relevant anatomical warnings customized to the current surgical step. Workflow analytics track procedure duration, phase transitions, and efficiency metrics, enabling post-operative review and continuous quality improvement. Integration with hospital information systems allows automated documentation of key procedural events, reducing administrative burden and improving record completeness.
Complete 3D recordings of every procedure create a comprehensive digital archive for education, quality assurance, and medicolegal documentation. Unlike traditional video recording, our digital twin captures the full three-dimensional geometry, instrument trajectories, timing data, and system alerts throughout the operation. Recordings can be reviewed from any viewpoint, including perspectives impossible during live surgery, enabling superior surgical education and peer review. Advanced playback features include variable speed control, surgical phase indexing, side-by-side comparison of similar cases, and annotation capabilities for teaching. All recorded data is encrypted and stored with full HIPAA compliance, including granular access controls and comprehensive audit logging.
Our surgical guidance system integrates seamlessly into your operating room workflow, providing intelligent assistance from pre-operative planning through post-operative analysis without disrupting established procedures.
The Leduyvo workflow begins well before the patient enters the operating room. Our system automatically imports and processes pre-operative imaging data including CT scans, MRI sequences, and angiography results from your existing PACS (Picture Archiving and Communication System). Advanced segmentation algorithms automatically identify and delineate critical anatomical structures including vasculature, nervous tissue, organs, and pathological features such as tumors or lesions. Surgeons can review and refine these segmentations using our intuitive 3D planning interface, which allows rotation, zooming, and cross-sectional views from any angle.
The planning module enables simulation of different surgical approaches, helping identify the optimal trajectory that minimizes risk to critical structures. Surgeons can place virtual instruments in the 3D model to test approach angles and verify adequate working space. The system calculates safety margins between planned instrument paths and vulnerable anatomy, flagging approaches that may pose elevated risk. Custom anatomical landmarks and regions of interest can be annotated and will automatically appear as AR overlays during the live procedure.
Integration with electronic medical records ensures all relevant patient information including medical history, previous surgeries, and specific anatomical variations is available to inform the AI's decision-making during the procedure. The pre-operative plan is stored and becomes the baseline for intraoperative registration and guidance.
Once the patient is positioned and draped in the operating room, the Leduyvo system initiates automatic calibration of the RGB-D camera array. Multiple cameras mounted on adjustable arms around the surgical field undergo simultaneous intrinsic and extrinsic calibration, establishing precise spatial relationships between each camera's coordinate system. This calibration process takes less than 60 seconds and requires no active participation from the surgical team.
The cameras begin streaming synchronized depth and color data, which our real-time reconstruction engine fuses into a unified 3D model of the exposed anatomy. Advanced registration algorithms then automatically align this live 3D reconstruction with the pre-operative imaging data. The registration process uses a combination of surface matching, anatomical landmark detection, and iterative closest point algorithms to achieve sub-2mm alignment accuracy between the pre-operative plan and the current patient anatomy.
The system continuously monitors registration quality throughout the procedure and can automatically re-register if significant tissue deformation or patient movement is detected. This dynamic registration ensures that AR overlays remain accurately aligned with the actual anatomy even as conditions change during surgery. The surgical team receives real-time feedback on registration quality through a color-coded confidence indicator, ensuring they can trust the system's guidance at every moment.
With calibration and registration complete, Leduyvo enters active guidance mode, continuously analyzing the surgical field and providing intelligent assistance. The 3D reconstruction updates at 60Hz, capturing every movement and tissue change with minimal latency. All surgical instruments entering the field are automatically detected, classified, and tracked in three dimensions. Their positions relative to critical anatomical structures are calculated in real-time, with safety margins continuously monitored.
Augmented reality overlays are rendered on high-resolution displays positioned within the surgeon's natural line of sight, showing critical structures that lie beneath the visible tissue surface. Vasculature appears as color-coded tubular structures, nerves are highlighted in yellow, tumor margins in red, and other organs or structures in customizable colors. The overlay rendering engine uses depth information to ensure proper occlusion, so overlays appear to exist in the correct spatial position relative to visible anatomy.
The AI monitoring system continuously evaluates hundreds of safety parameters, including proximity of instruments to vulnerable structures, unusual bleeding patterns, unexpected anatomical findings, and deviations from the pre-operative plan. When potential concerns are identified, graduated alerts are delivered based on severity. Low-priority notifications appear as non-intrusive on-screen indicators. Medium-priority alerts include gentle audio tones. High-priority warnings trigger prominent visual alerts and distinctive audio patterns that immediately capture attention without startling the surgical team.
Throughout the procedure, the system provides phase-aware guidance, recognizing which step of the surgery is currently being performed and offering relevant checklists, optimal instrument suggestions, and phase-specific anatomical warnings. This contextual intelligence helps maintain procedural flow and reduces cognitive load on the surgical team.
When the procedure concludes, Leduyvo automatically generates comprehensive documentation and analytical reports that provide value far beyond the operating room. The complete digital twin recording captures every moment of the surgery in full 3D, including all instrument movements, tissue changes, system alerts, and surgeon actions. This rich dataset becomes an invaluable resource for multiple post-operative applications.
Automated surgical reports are generated, documenting key procedural events, phases completed, instruments used, and any safety alerts that occurred. These reports integrate seamlessly with electronic health record systems, reducing documentation burden while improving record completeness and accuracy. Critical moments can be automatically timestamped and indexed, making it easy to locate specific events during review.
For quality assurance and continuous improvement, the system provides detailed performance analytics including procedure duration, phase timing, efficiency metrics, and safety indicator trends. Comparison with similar historical cases helps identify opportunities for technique optimization and workflow enhancement. Hospitals can aggregate anonymized data across multiple procedures to identify institutional trends and benchmark performance.
The 3D recordings serve as exceptional educational resources. Surgical residents and students can review procedures from any viewpoint, including perspectives impossible to achieve during live observation. They can pause, rewind, zoom, and rotate the view to understand complex three-dimensional relationships and surgical techniques. Annotated recordings with expert commentary can be created for teaching specific procedures or techniques.
For medicolegal purposes, the objective, comprehensive record provides unprecedented documentation of surgical decision-making and execution. The system's records include not only what occurred but also the information available to the surgeon at each moment, supporting evidence-based assessment of surgical judgment and adherence to standards of care.
Leduyvo's surgical guidance platform is built on a foundation of cutting-edge technologies spanning computer vision, machine learning, augmented reality, and high-performance computing. Our integrated system delivers real-time performance while maintaining the accuracy and reliability demanded by surgical applications.
At the core of Leduyvo's perception system is an array of high-precision RGB-D (color plus depth) cameras strategically positioned around the surgical field. We utilize Intel RealSense D455 cameras, each providing 1280×720 depth resolution at 90fps with depth accuracy better than 2% at 2m range. The cameras employ stereo vision with an active infrared projector to generate dense depth maps even on textureless or reflective surfaces common in surgical settings.
Our typical configuration deploys 4-6 cameras in complementary positions to ensure complete coverage of the surgical workspace while minimizing occlusions. Camera mounting arms are designed for OR integration, attaching securely to existing surgical lighting systems or ceiling rails without interfering with established workflows. Quick-release mechanisms enable rapid installation and removal.
Synchronized data capture across all cameras is achieved through hardware triggering, ensuring temporal alignment of frames to within 1 millisecond. This precise synchronization is critical for accurate 3D reconstruction from multiple viewpoints. Custom firmware modifications enable simultaneous exposure control across the camera array, adapting to the challenging lighting conditions of operating rooms including bright surgical lights and shadows.
Converting multiple 2.5D depth maps into a unified, coherent 3D model of the surgical field requires sophisticated reconstruction algorithms operating under strict real-time constraints. Leduyvo employs a truncated signed distance function (TSDF) volumetric fusion approach, optimized for GPU acceleration and capable of processing over 6 million depth pixels per frame at 60Hz.
The reconstruction pipeline begins with depth map preprocessing to remove noise and correct systematic errors. Statistical outlier removal filters eliminate spurious depth readings caused by specular reflections, while bilateral filtering smooths depth maps while preserving sharp edges at anatomical boundaries. Depth maps are then transformed into the common world coordinate frame using the precisely calibrated camera extrinsics.
Volumetric integration fuses depth information from all cameras into a unified 3D representation. The surgical workspace is discretized into a 512×512×512 voxel grid with 0.5mm voxel size, providing sub-millimeter spatial resolution across a 25cm³ volume. Each voxel maintains a weighted TSDF value that represents the distance to the nearest surface. As new depth frames arrive, they are integrated into the volume using weighted averaging, with weights based on view angle and measurement confidence. This incremental fusion naturally handles sensor noise and produces smooth, accurate surface reconstructions.
Surface extraction via marching cubes generates a triangle mesh from the TSDF volume, which is then simplified using quadric error metrics to achieve real-time rendering performance while maintaining anatomical fidelity. The mesh is updated dynamically as the TSDF volume evolves, capturing tissue deformation and surgical modifications.
Accurate real-time tracking of surgical instruments is essential for safety monitoring and spatial guidance. Leduyvo's instrument detection system is built on deep learning architectures specifically optimized for the surgical domain. Our primary detector is based on YOLOv8, a state-of-the-art object detection network that balances detection accuracy with inference speed. The network has been extensively trained on a proprietary dataset of over 2 million annotated surgical images spanning 400+ instrument types across multiple surgical specialties.
Training data includes diverse lighting conditions, viewing angles, partial occlusions, and various stages of instrument wear to ensure robust generalization to real surgical environments. Data augmentation techniques including random rotations, color jittering, simulated blood effects, and synthetic occlusions further enhance model robustness. The resulting detector achieves 99.1% mean average precision (mAP) on held-out test data while maintaining inference times under 8ms on GPU.
Beyond 2D bounding box detection, Leduyvo performs 6-DOF pose estimation to determine each instrument's complete position and orientation in 3D space. This is accomplished by combining 2D keypoint detection with PnP (Perspective-n-Point) algorithms that leverage the calibrated camera geometry and 3D instrument models. Instrument tips, shafts, and handles are localized with sub-millimeter precision, enabling accurate calculation of distances to anatomical structures.
Temporal tracking algorithms maintain instrument identity across frames, associating detections over time to produce smooth, continuous trajectories. Kalman filtering predicts instrument motion and resolves temporary occlusions. The tracking system can maintain identity even when instruments leave and re-enter the field of view by matching visual appearance descriptors.
Preventing surgical complications requires not just monitoring current conditions but anticipating potential hazards before they materialize. Leduyvo's predictive safety system employs machine learning models trained on extensive datasets of surgical procedures to identify risk patterns and predict adverse events.
The safety monitoring pipeline evaluates multiple risk dimensions simultaneously. Geometric analysis continuously calculates minimum distances between tracked instrument tips and segmented critical structures like blood vessels and nerves. When distances fall below safety thresholds (typically 5-10mm depending on structure type and instrument), graded alerts are triggered. Thresholds are dynamically adjusted based on instrument velocity and trajectory, providing earlier warnings when instruments are moving rapidly toward vulnerable anatomy.
Trajectory prediction algorithms use Kalman filters and LSTM (Long Short-Term Memory) neural networks to forecast instrument motion up to 500ms into the future. By anticipating where instruments will be rather than just where they are, the system can provide proactive alerts that give surgeons time to adjust their approach before critical structures are threatened.
Activity recognition models classify current surgical actions (cutting, dissecting, retracting, cauterizing, etc.) and cross-reference them against expected activities for the current procedure phase. Unexpected activities or unusual action sequences can indicate confusion, distraction, or technical difficulties, prompting the system to offer guidance or request confirmation before proceeding.
Anomaly detection algorithms trained on thousands of routine procedures learn to recognize "normal" patterns of anatomical appearance, instrument usage, and procedural flow. Significant deviations from learned patterns trigger investigation, whether they represent anatomical variations, unexpected findings, or potential complications. This unsupervised approach can detect novel failure modes not explicitly programmed.
Accurate alignment between pre-operative imaging and intraoperative anatomy is critical for AR overlay accuracy and surgical navigation. However, this registration problem is complicated by tissue deformation, surgical manipulation, and anatomical changes that occur between imaging and surgery. Leduyvo addresses these challenges through advanced deformable registration techniques.
Initial rigid registration aligns pre-operative and intraoperative coordinate systems using anatomical landmarks and surface matching. Key anatomical features (bone landmarks, organ boundaries, major vessels) are automatically detected in both pre-operative images and live 3D reconstructions. Corresponding feature sets are aligned using robust point-set registration algorithms that are insensitive to outliers and partial matches.
Following rigid alignment, deformable registration warps the pre-operative anatomy to match observed tissue deformation. Our approach uses biomechanically-informed finite element models that constrain deformations to be physically plausible based on tissue material properties. The registration optimization minimizes surface distance between the pre-operative model and the live reconstruction while penalizing implausible deformations.
Registration is performed continuously throughout the procedure, with the deformation field updated in real-time as tissue shifts and changes. GPU acceleration enables registration updates at 1-2Hz, sufficient to track typical surgical manipulations. Registration quality is continuously assessed using surface matching error metrics, with visual feedback provided to the surgical team via color-coded confidence maps.
Meeting the computational demands of real-time surgical guidance requires careful optimization and hardware acceleration throughout the processing pipeline. Leduyvo's computing architecture is built on industrial-grade workstation hardware featuring NVIDIA RTX 4090 GPUs, Intel Xeon processors with 32+ cores, 128GB RAM, and high-speed NVMe storage arrays.
The software architecture employs extensive parallel processing to maximize throughput. Depth map preprocessing, 3D reconstruction, instrument detection, registration, and rendering all execute concurrently on different GPU streams, with careful pipeline management to minimize latency. CUDA kernels are hand-optimized for critical paths, achieving near-peak GPU memory bandwidth utilization.
Network architecture enables distributed processing when single-machine performance is insufficient. High-bandwidth 10GbE connectivity allows camera streams and intermediate processing results to be distributed across multiple GPU nodes, with intelligent load balancing to maximize aggregate throughput. Latency-critical components remain on local GPUs to minimize network delays.
Extensive profiling and optimization ensures end-to-end system latency remains below 15ms from photon capture to display update. This low latency is essential for maintaining visual coherence and enabling precise hand-eye coordination with AR overlays. Deterministic real-time scheduling prevents priority inversion and ensures consistent frame times even under peak load.
Leduyvo's surgical guidance technology enhances safety and precision across a diverse range of surgical specialties. Our platform is configurable for the specific anatomical, procedural, and workflow requirements of each surgical domain.
In neurosurgical procedures, where millimeter-scale precision can mean the difference between success and permanent disability, Leduyvo provides critical guidance for navigating complex cerebrovascular and neural anatomy. The system displays real-time overlays of blood vessels, functional brain areas, and tumor margins derived from pre-operative MRI and fMRI imaging. Surgeons performing tumor resections can visualize the complete extent of pathological tissue including non-visible infiltrative margins, enabling maximal safe resection while preserving eloquent cortex.
For vascular neurosurgery including aneurysm clipping and AVM resection, Leduyvo maps the complete vascular tree with sub-millimeter accuracy, showing vessel positions even when obscured by brain tissue. The system tracks microvascular instruments and alerts surgeons when clip placement might compromise perforating vessels or when dissection approaches critical perforators. Trajectory planning for deep lesions optimizes approach corridors that minimize white matter disruption.
Deep brain stimulation (DBS) electrode placement benefits from Leduyvo's ability to register pre-operative atlas-based targeting with intraoperative microelectrode recordings, improving targeting accuracy for structures like the subthalamic nucleus. The system can compensate for brain shift due to CSF loss, maintaining accurate targeting throughout the procedure.
Cardiac surgical procedures benefit substantially from Leduyvo's real-time visualization of coronary anatomy and intracardiac structures. During coronary artery bypass grafting (CABG), the system maps the complete coronary vascular tree including small diagonal and marginal branches, helping surgeons identify optimal bypass targets and plan anastomosis sites. AR overlays show vessel location even when obscured by epicardial fat, streamlining vessel identification and dissection.
For valve repair and replacement procedures, Leduyvo provides precise visualization of valve anatomy, annular dimensions, and subvalvular apparatus. The system guides optimal suture placement for mitral valve repair, predicting the effect of different repair techniques on valve geometry. During transcatheter valve procedures, real-time 3D reconstruction enables precise device positioning and deployment monitoring.
Complex congenital cardiac repairs benefit from Leduyvo's ability to clarify abnormal anatomy. The system integrates pre-operative CT angiography with live surgical views, helping surgeons navigate unfamiliar anatomy and plan complex reconstructions. For minimally invasive cardiac procedures, the enhanced visualization compensates for limited direct visualization, improving safety and outcomes.
Liver surgery requires detailed knowledge of complex vascular and biliary anatomy that varies significantly between patients. Leduyvo addresses this challenge by overlaying patient-specific vascular and biliary trees onto the liver surface, derived from CT angiography and MRCP imaging. During hepatic resections, surgeons can visualize portal and hepatic vein branches, determining optimal resection planes that preserve vascular supply to the remnant liver while achieving oncologic margins.
The system displays liver segment boundaries based on vascular territories, helping surgeons perform anatomic resections aligned with functional liver architecture. Tumor locations relative to vascular structures inform decisions about resectability and surgical approach. For tumors near major vessels, Leduyvo shows precise relationships that guide vascular control strategies.
Biliary anatomy visualization is particularly valuable given the high variability and small caliber of bile ducts. The system alerts surgeons to aberrant biliary anatomy at risk during dissection, reducing iatrogenic bile duct injuries. During complex hilar dissections, real-time overlays clarify ductal anatomy even when not directly visible, improving safety in challenging cases.
Orthopedic procedures including joint replacement, fracture fixation, and spinal surgery benefit from Leduyvo's precision guidance and navigation capabilities. For total joint arthroplasty, the system provides real-time feedback on component positioning, alignment, and limb mechanics. Surgeons can visualize the planned implant position overlaid on the prepared bone, ensuring accurate placement that matches pre-operative planning.
Pedicle screw placement in spinal surgery is enhanced by AR visualization of vertebral anatomy including pedicle boundaries and critical structures like the spinal cord and nerve roots. The system tracks drill trajectory in real-time, alerting surgeons to deviations from planned screw paths that might risk breach or neural injury. This guidance is particularly valuable in deformed anatomy where normal landmarks are absent.
Complex fracture reduction and fixation benefit from 3D visualization of fracture fragments and planned reduction positions. The system can overlay pre-injury anatomy reconstructed from the contralateral limb, providing a template for anatomic reduction. Instrument tracking ensures precise placement of plates and screws according to pre-operative plans.
Ear, nose, throat, and head and neck procedures often navigate intricate anatomy where critical structures are closely apposed. Leduyvo's detailed anatomical mapping is invaluable in these constrained surgical fields. During endoscopic sinus surgery, the system displays real-time overlays of orbital walls, optic nerves, internal carotid arteries, and skull base, helping surgeons avoid devastating complications while achieving complete disease clearance.
Skull base approaches benefit from visualization of vascular structures including the cavernous sinus and critical neurovascular bundles. The system alerts surgeons to proximity of instruments to these structures, enabling aggressive tumor resection while preserving function. For cochlear implantation, precise trajectory guidance ensures optimal electrode placement within the scala tympani.
Head and neck oncologic procedures require clear delineation of tumor extent and critical structures including major vessels, cranial nerves, and the airway. Leduyvo overlays pre-operative imaging showing tumor margins, nodal disease, and vascular involvement, guiding resection and reconstruction. The system helps achieve negative margins while preserving maximum functional tissue.
Laparoscopic and robotic surgical approaches present unique challenges including limited field of view, two-dimensional visualization, and restricted tactile feedback. Leduyvo addresses these limitations by providing enhanced three-dimensional perception and anatomical awareness. The system tracks laparoscopic instruments in 3D space and overlays critical anatomy not visible through the laparoscopic camera, restoring spatial context that would be available in open surgery.
For laparoscopic liver or kidney procedures, AR overlays show subsurface vessels and collecting systems, enabling precise parenchymal transection while avoiding vascular injury. During laparoscopic colectomy, the system maps vascular anatomy showing variant vessel positions that might be encountered during mesenteric dissection.
Robotic surgery integration enables AR overlays directly in the surgeon console view, providing enhanced visualization without separate displays. The system compensates for tissue motion due to pneumoperitoneum and patient ventilation, maintaining stable overlay registration. Haptic feedback can be augmented with virtual force feedback when instruments approach critical structures, restoring a sense of touch lost in minimally invasive approaches.
Detailed technical specifications and performance characteristics of the Leduyvo surgical guidance platform.
Discover how Leduyvo's AI-powered surgical guidance system can enhance safety, precision, and outcomes at your institution. Schedule a personalized demonstration to see our technology in action and discuss how we can tailor the system to your specific surgical programs.