Machine Learning Veterinary Pathology Data Processing
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Solution Overview
Problem
Current pathology data processing methods are manual, time-consuming, and often incomplete, lacking efficient tools for veterinarians to analyze and interpret veterinary pathology data, leading to challenges in generating comprehensive reports and scheduling follow-on testing.
Innovation Solution
A computer-implemented method using machine-learning logic trained on veterinary pathology data to generate pathology summaries, providing graphical user interfaces for structured input and output, including the option to create digitally-stained slides for additional analysis and scheduling follow-on testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual pathology data processing is used, then veterinarians can review pathology reports, but the process is time-consuming and inefficient
Solution Approach 1:
The system enables self-service through automated machine learning algorithms that independently process pathology data, extract keywords, generate summaries, and identify follow-on testing requirements without requiring manual intervention from veterinarians or pathologists for routine cases
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computer-based system using machine learning models that process pathology reports, extract meaningful information, and generate structured outputs, thereby eliminating time-consuming manual operations
2Loss of information
If conventional text-based pathology reports are used, then pathologists can provide diagnostic information, but the reports are incomplete and require extensive interpretation
Solution Approach 1:
The system segments the pathology report into distinct components including key terms extraction, pathology summary generation, background information modules, contact information modules, and ordering modules, making the information more organized and easier to process while reducing overall complexity
Solution Approach 2:
The machine learning algorithm acts as an intermediary between the raw pathology report and the final processed output, automatically extracting meaningful information, filling in missing data, and generating structured summaries that enhance completeness without requiring complex manual analysis
3Measurement precision
If additional samples are stained differently for better analysis, then diagnostic accuracy improves, but additional samples are often not available at the time of evaluation
Solution Approach 1:
The system performs preliminary analysis by automatically extracting key terms and generating pathology summaries from the initial sample data before final diagnosis is made, enabling veterinarians to make informed decisions about whether additional staining or sampling is needed based on the pre-processed information
Solution Approach 2:
The system changes the parameter of data processing from manual text analysis to automated machine learning-based extraction and summarization, improving the efficiency and accuracy of information processing while enabling better decision-making about additional testing requirements
Data Source
AI summary
An example computer-implemented method for processing pathology data includes providing a first graphical user interface for display on a first device, receiving pathology data associated with a patient, extracting a keyword from the pathology data, determining by executing a first machine-learning logic and based on the keyword extracted from the pathology data a pathology summary, providing a second graphical user interface for display on a second device presenting the pathology summary, and receiving a second input from the second graphical user interface. In response to receiving the second input at the second graphical user interface, the method includes providing for display at least one of: a background information module comprising data associated with the pathology summary; a contact information module comprising contact information of a pathologist associated with the pathology data; and an ordering module, which when initiated, generates an order for follow-on testing.