Image Time Series Template for Clinical Imaging Data Automation
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Solution Overview
Problem
Existing radiology reporting software and Picture Archiving and Communication Systems (PACS) lack an efficient and customizable way to integrate data conversion, standardization, analysis, storage, access, and reporting for clinical imaging time series data, leading to time-consuming and error-prone manual processes in clinical trials.
Innovation Solution
A method that uses image time series templates to receive and calculate patient image data directly from radiologists, integrating with existing PACS and voice reporting systems to automate data entry and analysis, reducing the time required for data entry and calculation by using templates to streamline the generation of medical reports and treatment outcome assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data entry and calculation methods are used for clinical imaging data analysis, then doctors can accurately measure and record lesion dimensions, but the process takes a significant amount of time and is error-prone
Solution Approach 1:
The patent introduces an intermediary software system that automatically extracts measurement data from PACS images and performs calculations according to clinical trial protocols. This intermediary automates the time-consuming manual processes while maintaining measurement accuracy by using standardized extraction and calculation methods.
Solution Approach 2:
The patent replaces the mechanical manual process of data entry and calculation with an automated computer-based system. The system automatically retrieves images from PACS, extracts measurement data, performs calculations, and generates reports, eliminating manual labor while preserving measurement precision through standardized algorithms.
2Productivity
If modified PACS software is used to export lesion measurements directly to output sheets, then data export is streamlined, but a parallel process must be set up that does not interface with standard PACS and voice reporting systems
Solution Approach 1:
The patent creates a universal interface layer that works with standard PACS and voice reporting systems while enabling automated data extraction and analysis. This multi-functional system maintains compatibility with existing infrastructure rather than requiring a completely separate parallel system.
Solution Approach 2:
The patent merges the data extraction and analysis functions with the existing PACS and voice reporting workflows rather than creating a separate parallel process. The system integrates seamlessly with standard radiology processes while adding automated analysis capabilities.
3Manufacturing precision
If standardized templates are used for data collection and analysis, then data consistency and standardization are improved, but the system requires integration with multiple existing systems (PACS, voice reporting)
Solution Approach 1:
The patent introduces an intermediary standardized template system that sits between existing PACS and voice reporting systems, enforcing data standardization while managing integrations through a unified interface layer that handles protocol-specific requirements.
Solution Approach 2:
The patent uses configurable templates with adjustable parameters to adapt to different clinical trial protocols and data requirements. This allows standardized data collection while accommodating variations in protocol specifications without requiring separate systems for each protocol.
Data Source
AI summary
A method for generating a medical report includes receiving patient image data from a user in response to displaying a patient image to the user that is retrieved from a server. The user enters patient image data directly into an image time series template. The patient image data, in one embodiment, pertains to measurements of objects (both target and non-target) of the patient shown in the patient image. Patient image data is then extracted from the image time series template and data is calculated pertaining to an object (e.g., a lesion) in the image based on the extracted data and the location of the extracted data in the image time series template. The calculated results are presented for review by a user. In response to the user approving the results, a report of the results is generated.