ML Patient Biographic Estimation from Medical Images
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Solution Overview
Problem
Current methods for estimating patient biographic data, such as weight, height, and age, are often inaccurate when not measured directly, leading to potential overdosing or excessive radiation exposure during medical procedures, especially in incapacitated patients or those in resource-limited environments where guessing occurs frequently.
Innovation Solution
A machine learning-based approach that utilizes deep learning models to estimate patient biographic data from image data, segmenting the patient's body and applying learned features to generate accurate biographic parameters like weight, height, and age without manual selection of features, thereby improving estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement of patient biographic data is performed, then measurement precision is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent replaces direct mechanical measurement systems (scales, stadiometers) with a machine learning-based computational system that estimates biographic data from medical images. The ML model processes image data to predict weight, height, age, and gender, eliminating the need for physical measurement equipment while maintaining clinical utility.
Solution Approach 2:
The patent uses medical images (CT, MRI, X-ray) as proxies or copies of the patient's physical characteristics. Instead of directly measuring the patient, the system analyzes image data that contains indirect information about biographic parameters, allowing estimation without direct contact or specialized measurement devices.
2Measurement precision
If direct measurement of patient biographic data is performed, then measurement precision is improved, but loss of time increases due to manual measurement processes
Solution Approach 1:
The patent performs biographic data estimation as a preliminary step integrated into the medical imaging workflow. The ML model automatically processes images and generates biographic data estimates before clinical decisions are made, eliminating the need for separate manual measurement steps and reducing overall workflow time.
Solution Approach 2:
The system enables self-service estimation where the medical imaging system itself generates biographic data estimates without requiring additional manual intervention. The ML model automatically extracts and processes relevant features from the images to produce accurate biographic predictions, reducing dependency on staff time for measurements.
3Device complexity
If human estimation of patient biographic data is performed, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the medical images and the biographic data estimation. The ML model acts as a computational mediator that systematically processes image features and translates them into accurate biographic predictions, eliminating the variability and inaccuracy of human guessing while keeping the system simple and equipment-free.
4Ease of operation
If human estimation of patient biographic data is performed, then ease of operation is improved, but reliability deteriorates
Solution Approach 1:
The system implements self-service estimation where the ML model automatically generates reliable biographic data from medical images without requiring human intervention. This maintains ease of operation (no additional manual steps needed) while dramatically improving reliability by replacing inconsistent human guesses with consistent, data-driven predictions.
Data Source
AI summary
Patient biographic data may be estimated by receiving patient image data, applying the patient image data to a machine learned model, the machine learned model trained on second patient data and trained to map the second patient data to associated biographic data using machine learned features, generating the patient biographic data based on the applying and the machine learned features, and outputting the patient biographic data. The patient biographic data may include a patient weight, a patient height, a patient gender, and a patient age.