AI Spinal Canal Volume Prediction for Surgical Planning

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Solution Overview

Problem

Accurately predicting the outcomes of surgical procedures for spinal stenosis has been limited, making it difficult to determine which procedure will most effectively increase spinal canal volume and alleviate symptoms.

Innovation Solution

A system using AI, such as a neural network, analyzes pre-operation medical images to calculate initial spinal canal volume, predicts post-operation volumes for various surgical options, and selects the most effective surgery based on these predictions, incorporating patient-specific data and updating the AI with post-operation data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional surgical planning methods are used, then the process is simple and quick, but the accuracy of predicting spinal canal volume changes is insufficient

Engineering Contradiction:

Improveprediction accuracy of spinal canal volume changesVSAvoidcomplexity of surgical planning system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical/manual surgical planning methods with an AI-based neural network system that automatically analyzes medical images and predicts spinal canal volume changes, thereby improving prediction accuracy while managing system complexity through automation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system creates a virtual 3D model (copy) of the patient's spine based on medical images, allowing accurate prediction of surgical outcomes without requiring complex physical measurements or multiple imaging procedures

Inventive Principle:

Principle #26Copying

2Reliability

If multiple surgical options are evaluated in detail, then the selection of optimal surgery is improved, but the time required for surgical planning increases

Engineering Contradiction:

Improvereliability of surgical option selectionVSAvoidtime required for surgical planning

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The neural network system performs preliminary calculations and predictions for multiple surgical options simultaneously during the planning phase, allowing surgeons to evaluate various approaches quickly before making a decision, thereby improving selection reliability without significantly increasing planning time

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system efficiently evaluates multiple surgical parameters and variables through the neural network's automated analysis, processing complex geometric and anatomical data to provide reliable comparisons of different surgical approaches within a reasonable time frame

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250339205A1System and method for automated volumetric spinal assessment

Publication Date: 2025.11.06 AGADA MEDICAL LTD

AI summary

Systems, methods, and computer-readable storage media for measuring spinal canal volume in vertebrates, and more specifically to using Artificial Intelligence (AI) to predict how surgical options will affect spinal canal volume. A system configured as disclosed herein can receive two or more pre-operation medical images capturing at least one functional spinal unit, then calculate an initial spinal canal regional volume using the pre-operation medical images for at least a portion of the at least one functional spinal unit. The system can then calculate, using a neural network, a predicted spinal canal regional volume for at least a portion of the at least one functional spinal unit undergoing various spine surgery options separately, resulting in predicted spinal canal regional volumes corresponding to the plurality of spine surgery options. The system or a surgeon can then select, using that data, one or more of the spine surgery options.