Neural Network Augmented Cystoscopy for Prostate Landmark Detection
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Solution Overview
Problem
Current minimally invasive procedures for treating Benign Prostatic Hyperplasia (BPH) lack accurate positional information regarding anatomical landmarks, leading to potential complications such as inadvertent puncture of the rectum or contact with the pubic bone, and require improved targeting and positioning of treatment instruments within the prostate gland.
Innovation Solution
A system and method utilizing a neural network deep learning model to process and augment cystoscopic images in real-time, providing identification of anatomical landmarks, device features, relative distances, speed of device movement, and estimated efficacy of the procedure, through modules such as detection, segmentation, location measuring, speed measuring, and display modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If minimally invasive procedures are performed without accurate positional information, then the procedures can be performed with simpler equipment and less complex systems, but the risk of complications such as inadvertent puncture of the rectum or contact with the pubic bone increases
Solution Approach 1:
The patent introduces an intermediary system comprising image capture devices, processors, and display devices that act as a mediator between the treatment instrument and the anatomical structures. This intermediary system processes real-time images to generate positional information about anatomical landmarks relative to the treatment instrument, enabling safe procedure performance without requiring direct visual observation of deep internal structures.
Solution Approach 2:
The patent replaces complex mechanical positioning systems with an imaging-based information system. Instead of relying on mechanical guides or physical landmarks, the system uses image capture devices to detect anatomical landmarks and processors to generate positional information, substituting mechanical complexity with informational processing.
2Measurement precision
If real-time image processing and augmentation systems are implemented, then the precision of anatomical landmark identification and implant placement improves, but the device complexity and processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network deep learning model with large datasets of anatomical images before actual procedure use. The model is预先 prepared to recognize anatomical landmarks and generate positional information, so that during the actual procedure, the system can quickly and accurately process real-time images without requiring complex runtime computations.
Solution Approach 2:
The patent uses copying by creating a virtual representation of the anatomical structures through image processing and augmentation. The system generates augmented images that copy and highlight key anatomical landmarks and their spatial relationships, allowing the operator to see positional information without directly observing the actual anatomical structures, thereby simplifying the perception task.
3Loss of information
If multiple modules for detection, segmentation, location measuring, and speed measuring are integrated, then the comprehensiveness of positional information provided improves, but the device complexity and computational load increase
Solution Approach 1:
The patent merges multiple functional modules into an integrated system where the image capture device, neural network processor, and display device work together as a unified information processing chain. The detection, segmentation, location measuring, and speed measuring functions are combined in a sequential processing pipeline, where each module's output becomes the input for the next module, reducing overall system complexity compared to separate independent systems.
Solution Approach 2:
The patent applies universality by designing the image capture device and processor to perform multiple functions: capturing images, detecting anatomical landmarks, segmenting tissue structures, measuring locations, and calculating speeds. This multi-functional approach reduces the need for separate specialized devices for each measurement task, thereby reducing overall device complexity while maintaining information completeness.
Data Source
AI summary
A system and method for providing relative positional information of a therapeutic or diagnostic device during the treatment of urinary tract diseases and conditions.