Spinal Alignment Diagnosis Using Neural Network Landmark Labeling
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Solution Overview
Problem
Current methods for diagnosing and tracking spinal alignment, particularly in patients with spinal deformities like scoliosis, are invasive, costly, and expose young patients to excessive radiation, leading to inconsistencies and discomfort in assessment processes.
Innovation Solution
A computerized system utilizing a pre-trained neural network to analyze optical and medical images of the spinal region, allowing for the labeling of anatomical landmarks and the generation of output images for clinical assessment, which can be conducted in a non-invasive and comfortable environment using portable image acquisition devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional X-ray imaging methods are used for spinal alignment diagnosis, then measurement precision is improved, but radiation exposure to patients increases
Solution Approach 1:
The patent creates a virtual 3D copy of the spinal structure from external surface images. The system captures photographs of the patient's back and uses computer vision algorithms to generate a virtual spine model that replicates the anatomical landmarks and curvature without requiring actual X-ray exposure. This virtual copy enables accurate measurement of spinal alignment parameters while avoiding ionizing radiation entirely.
Solution Approach 2:
The patent replaces the physical X-ray imaging mechanism with an optical-based computer vision system. Instead of using ionizing radiation to visualize internal structures, the system uses cameras to capture external surface geometry and processes these images through neural networks and 3D reconstruction algorithms to derive spinal alignment information, substituting a harmless optical-mechanical system for the harmful radiological system.
2Measurement precision
If multiple clinical assessments are conducted to ensure diagnostic accuracy, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary automated processing of spinal alignment data through pre-trained neural networks and computer vision algorithms. The system automatically detects anatomical landmarks, constructs 3D spinal models, and calculates alignment parameters before clinical review, preparing comprehensive diagnostic data in advance. This preliminary automated action reduces the time required for manual assessment while maintaining or improving diagnostic consistency through algorithmic precision.
Solution Approach 2:
The patent implements a feedback mechanism where the computerized assessment system provides automated measurements and visualizations to clinicians, who can then verify and refine the results. The system allows for iterative review where clinicians can request re-assessment of specific parameters, and the system provides consistent, repeatable measurements across multiple assessments. This feedback loop ensures diagnostic accuracy while minimizing time loss through automated consistency checks and reduced need for repeated manual measurements.
Data Source
AI summary
A process operable using a computerized system for providing one or more output images of the spinal region of a subject for which anatomical landmarks applicable for clinical assessment are labeled in a pre-trained neural network (120a,120b) for clinical assessment of malalignment of a spine of a subject, the computerized system (100a,100b) including an image data acquisition device (110a), a pre-trained neural network (120a,120b) and an output module (140a) operably interconnected together via a communication link, said process including the steps of (i) by an image data acquisition device (110a,), acquiring one or more data input sets indicative of the spinal region of a subject, wherein each data input set of the one or more data input sets is indicative of an optical image of said subject at one or more corresponding postures of the subject; (ii) in a pre-trained neural network (120a,120b), providing labels to anatomical landmarks of said one or more data input sets each of which is indicative of said optical image of the subject at said one or more postures of the subject acquired during step (i) so as to provide one or more optical output images for subsequent clinical assessment of the spine of said subject, wherein the pre-trained neural network (120a,120b) has been pre-trained utilising one or more training data input sets corresponding to one or more predetermined postures of training subjects acquired from a plurality of training subjects, wherein said one or more predetermined postures are postures utilized for clinical assessment of malalignment of the spine of a subject; wherein the anatomical landmarks of the spine of said one or more training data input sets acquired from said plurality of training subjects have been pre-labeled by at least one clinician; and wherein the one or more postures of said subject for which the one or more data input sets of the subject acquired during step (i) correspond to one or more of said predetermined postures; and (iii) displaying by the output module (140a), the one or more optical output images of the spinal region of said subject having said labels provided thereto by the pre-trained neural network (120a,120b), for clinical assessment.