Inline Image Scan Enrichment Using Machine Learning Focal Points
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Solution Overview
Problem
The digitization of medical images is cumbersome and often results in insufficient scans that fail to capture targeted details, necessitating repeated scanning processes due to the reliance on archaic methods and stringent patient confidentiality requirements in healthcare.
Innovation Solution
An apparatus and method utilizing machine-learning to identify focal points during the scanning process, optimizing image capture by generating a candidate set to model scan focal points, digitally capturing intended details, and storing the optimized images in an accessible repository.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scanning methods are used to digitize medical images, then patient confidentiality is maintained through individual scanning, but the scanning process becomes tedious and time-consuming
Solution Approach 1:
The system enables automated scanning where the computing device autonomously identifies focal points, determines scan parameters, and executes the scanning process without requiring manual intervention for each document, thereby maintaining confidentiality while dramatically improving efficiency
Solution Approach 2:
The patent replaces manual mechanical scanning operations with an automated system that uses machine learning models and computer vision algorithms to identify and capture focal points, substituting human effort with intelligent automation
2Device complexity
If traditional scanning methods are used, then equipment simplicity is maintained, but scan quality is insufficient and fails to capture targeted details
Solution Approach 1:
The system performs preliminary analysis of documents using machine learning models to identify focal points and determine optimal scan parameters before the actual scanning occurs, ensuring high-quality capture of critical details while maintaining relatively simple hardware
Solution Approach 2:
The patent dynamically adjusts scanning parameters such as resolution, focus, and exposure based on the identified focal points and document characteristics, allowing the system to optimize scan quality for different types of medical documents without requiring complex specialized equipment
3Ease of operation
If traditional scanning methods are used, then operational simplicity is maintained, but repeated scanning is necessary due to insufficient capture of details
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously learns from scan results and adjusts focal point identification and scan parameter selection to ensure complete capture of critical details on the first attempt, eliminating the need for repeated scanning
Solution Approach 2:
By pre-identifying focal points and optimizing scan parameters before scanning, the system ensures that the first scan captures all necessary details, preventing the need for time-consuming repeat scans while keeping the operational process simple
Data Source
AI summary
An apparatus and method for inline scanned image enrichment is provided. The apparatus includes a computing device configured to receive a plurality of subject data, generate a candidate set to model the scan focal points from, digitally capture the slide using the identified focal points through machine-learning processes, processing the captured image to optimize its viewability, and storing the captured image in an accessible repository.