Pathology Image AI Model Updates for Protocol Revisions
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Solution Overview
Problem
Pathologists and researchers face challenges in staying up to date with frequently updated pathology protocols, which can be time-consuming and tedious to manually adapt in existing systems, leading to inefficiencies in creating updated synoptic reports and analyzing digital medical images.
Innovation Solution
A system and method for automatically processing electronic medical images using machine learning models that are trained and updated based on new pathology protocols, including parsing protocol data, creating synoptic reports, and fine-tuning models with annotated training images to ensure compliance with the latest guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pathologists manually adapt to new pathology protocols, then they can ensure accurate analysis according to guidelines, but the process becomes time-consuming and tedious
Solution Approach 1:
The system enables self-service by automatically detecting protocol changes, parsing the updated protocols, and fine-tuning machine learning models without requiring manual intervention from pathologists. The system monitors protocol updates, extracts changes, and autonomously updates the analysis models to comply with new guidelines.
Solution Approach 2:
The patent replaces the mechanical manual process of protocol adaptation with an automated machine learning-based system. Instead of pathologists manually reviewing and adapting protocols, the system uses automated image processing, protocol parsing, and machine learning model fine-tuning to achieve the same outcome efficiently.
2Reliability
If pathologists manually create updated synoptic reports, then they can ensure compliance with new protocols, but the workflow becomes inefficient
Solution Approach 1:
The system performs self-service by automatically generating updated synoptic reports based on parsed protocol changes and machine learning model predictions. The system monitors protocol updates, extracts relevant changes, and autonomously generates compliant reports without requiring manual report creation by pathologists.
Solution Approach 2:
The patent replaces manual synoptic report creation with an automated system that uses machine learning models to generate reports. The system processes digital medical images, applies updated protocols, and automatically produces compliant synoptic reports, eliminating the tedious manual workflow.
3Adaptability or versatility
If machine learning models are frequently updated to reflect new protocols, then the analysis remains current with guidelines, but the complexity of model maintenance increases
Solution Approach 1:
The system performs preliminary action by continuously monitoring protocol changes and pre-processing the updates before they affect model training. The system detects protocol changes, parses the updated protocols, and prepares fine-tuning datasets in advance, so when model updates are needed, the process is streamlined and less complex.
Solution Approach 2:
The patent replaces complex manual model maintenance with an automated machine learning pipeline. The system automatically detects protocol changes, extracts relevant information, fine-tunes models using updated training data, and validates performance, eliminating the need for manual model maintenance while keeping the system adaptable to new guidelines.
Data Source
AI summary
Systems and methods are described herein for processing electronic medical images. The method may include determining, using an automated routine, whether a pathology protocol is accessible; determining a first set of one or more training images, the first set of one or more training images comprising digital medical images annotated utilizing the pathology protocol; and providing the training images to a machine learning model capable of analyzing digital medical images according to the pathology protocol or guideline. The providing may further include determining a starting model, splitting the first set of one or more training images into a training set A and an evaluation set B of digital medical images, fine tuning the starting model with the training set A to determine the machine learning model, evaluating the machine learning model with the training set B, and upon receiving a passing evaluation, saving the determined machine learning model.