AUTOMATIC HIP
SEGMENTATION
STATE-OF-THE-ART
ANATOMICAL ANALYSIS
AUGMENTED REALITY
AND ARTIFICIAL INTELLIGENCE
NEW GRAPHICAL
USER INTERFACE
INTUITIVE AND EASY TO USE
AUTOMATIC KNEE
SEGMENTATION
STATE-OF-THE-ART
ANATOMICAL ANALYSIS
AUTOMATIC SEGMENTATION
STATE-OF-THE-ART ANATOMICAL ANALYSIS
X-RAY VISION
Ergonomic and comfortable augmented reality experience for 3D visualization of internal anatomy without soft-tissue exposure
- 3D real-time perfectly matched image overlay technology
- Visualize all internal anatomy without soft tissue exposure
- Vital real-time information available in an intuitive manner
- Displayed directly onto the operative field
Smart Guidance
Artificial Intelligence-based algorithms for fully autonomous surgical planning and intuitive surgical assistance
- Automatic anatomy identification (color-coded)
- Autonomous surgical planning
- Autonomous computer guidance throughout surgery
- Adapts to surgeons using big data-based guidance
X-RAY VISION
Ergonomic and comfortable augmented reality experience for 3D visualization of internal anatomy without soft-tissue exposure
- 3D real-time perfectly matched image overlay technology
- Visualize all internal anatomy without soft tissue exposure
- Vital real-time information available in an intuitive manner
- Displayed directly onto the operative field
Smart Guidance
Artificial Intelligence-based algorithms for fully autonomous surgical planning and intuitive surgical assistance
- Automatic anatomy identification (color-coded)
- Autonomous surgical planning
- Autonomous computer guidance throughout surgery
- Adapts to surgeons using big data-based guidance
Augmented Reality
IMAGE OVERLAY
Allows the surgeon to focus their attention on the patient’s internal anatomy, without actually exposing it
MINIMALLY INVASIVE
Decreased tissue morbidity by decreasing soft tissue exposure
REAL-TIME 3D VISUALIZATION
Intuitive visualization of virtual internal anatomy that is responsive and adapts to surgeon’s 3D perspective of the surgical field
Artificial intelligence
Thousands of medical imagery of real patients were used to train proprietary neural networks to be able to automatically recognize human anatomy. Holosurgical® validated machine-learning algorithms autonomously identify, label, segment, and analyze bony, soft tissue, solid organ, vascular, and nervous system anatomy without any human intervention.
AUTONOMOUS SEGMENTATION
- Proprietary algorithms automatically identify and label anatomical landmarks before the surgeon makes the incision
- Fully validated for spine surgery in cadaver labs
- Autonomously identifies pedicles, lamina, spinous and transverse process, vertebral body, facet joints, and nerves
DATA ANALYTICS
- Machine learning algorithms provide intraoperative suggestions and alerts
- Computer suggests optimal implant placement for automatic presurgical planning and aids the surgeon to execute plan with intraoperative surgical guidance
Augmented Reality
IMAGE OVERLAY
Allows the surgeon to focus their attention on the patient’s internal anatomy, without actually exposing it
MINIMALLY INVASIVE
Decreased tissue morbidity by decreasing soft tissue exposure
REAL-TIME 3D VISUALIZATION
Intuitive visualization of virtual internal anatomy that is responsive and adapts to surgeon’s 3D perspective of the surgical field
Artificial intelligence
AUTONOMOUS SEGMENTATION
- Proprietary algorithms automatically identify and label anatomical landmarks before the surgeon makes the incision
- Fully validated for spine surgery in cadaver labs
- Autonomously identifies pedicles, lamina, spinous and transverse process, vertebral body, facet joints, and nerves
DATA ANALYTICS
- Machine learning algorithms provide intraoperative suggestions and alerts
- Computer suggests optimal implant placement for automatic presurgical planning and aids the surgeon to execute plan with intraoperative surgical guidance
Augmented Reality
INNOVATIVE PATIENT REGISTRATION
Proprietary machine-learning algorithms perform patient registration in few minutes after scanning. Compatible with all intraoperative CT scanners.
USING SEGMENTATION FOR DIAGNOSTICS AND INTRAOPERATIVE WARNINGS
Holosurgical’s neural networks have been trained to identify bony, neural, and vascular anatomy of the spine. This feature allows ARAI surgical navigation system to autonomously identify the implant, as well as the relationship between the implant and the neural structures, providing intraoperative guidance and warnings to the surgeon as the surgical instruments are placed into the patient anatomy. This is a key differentiator as current robotic/IGS systems do not know the difference between bone and nerve. This features can be used to serve the following functions:
- Post operative surveillance and assessment of neural injury
- Establishment of collision detection algorithms
- Establish automatic “go/no-go zones” to prevent erroneous placement of implants by robotic system
