Neural Network-Enhanced, Real-Time, Multi-Modal Depth Sensing for Next-Generation Automated Surgical Robots

This inventive concept envisions a next-generation version of the technology that integrates neural network-based fusion modules to enable real-time, intra-operative, multi-modal sensing of depth in vision systems for automated surgical robots, providing high-resolution, three-dimensional point clouds of the surgical site and enabling advanced robotic control and haptic feedback.

The original patent addressed the challenge of multi-modal sensing of depth in vision systems for automated surgical robots. However, the existing technology has limitations in terms of real-time processing, data fusion, and accuracy. This inventive concept addresses these limitations by leveraging neural network-based fusion modules to enable real-time, intra-operative, multi-modal sensing of depth, providing a more accurate and reliable solution for surgeons.

The inventive concept comprises a neural network-based fusion module that integrates depth data from multiple imaging modalities, including optical coherence tomography, ultrasound, and structured light, to generate a high-resolution, three-dimensional point cloud of the surgical site. The system can be used in conjunction with a robotic arm to move in response to depth data and a haptic feedback system to provide tactile feedback to the surgeon. The neural network-based fusion module can be trained using a large dataset of images of the surgical site, enabling the system to learn and improve over time.

The use of neural network-based fusion modules to enable real-time, intra-operative, multi-modal sensing of depth in vision systems for automated surgical robots is a novel and non-obvious advancement over the original patent. The integration of multiple imaging modalities and the use of machine learning algorithms to generate high-resolution, three-dimensional point clouds of the surgical site are key inventive steps that distinguish this concept from the prior art.

Alternative embodiments of the inventive concept could include the use of different neural network architectures, such as convolutional neural networks or recurrent neural networks, or the integration of additional imaging modalities, such as fluorescence or hyperspectral imaging. Variations of the concept could also include the use of different robotic arm configurations or haptic feedback systems.

The inventive concept has significant commercial potential in the field of automated surgical robotics, enabling surgeons to perform complex procedures with greater accuracy and precision. The technology could be integrated into existing robotic systems or used to develop new, next-generation systems. The target market includes hospitals, surgical centers, and medical device manufacturers.