AI Medical Image Analysis for Integrated Clinical Reporting
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Solution Overview
Problem
Existing medical information systems, such as spreadsheets, stand-alone documents, and cloud-based solutions, struggle to integrate disparate forms of medical information, leading to inaccurate data entries, scalability issues, and inadequate access control, particularly in prenatal ultrasound screening applications.
Innovation Solution
A computer-implemented system utilizing AI to aggregate, analyze, and report medical information, integrating with PACS and EMR systems, and employing machine learning to automate report generation, flag image quality issues, and detect abnormalities, while enabling seamless communication and access control among medical professionals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If spreadsheets or stand-alone documents are used to store and maintain clinical reports, then data entry flexibility is improved, but data accuracy deteriorates and maintenance overhead increases
Solution Approach 1:
The patent introduces an intermediary processing layer between data entry and storage that validates, standardizes, and verifies medical data before it is stored in the database. This intermediary mechanism ensures data accuracy while maintaining the flexibility of various input formats, resolving the contradiction between ease of operation and reliability.
2Quantity of substance
If databases are used to scale data storage across multiple users and organizations, then data capacity is improved, but system complexity and maintenance overhead increase
Solution Approach 1:
The patent segments the medical information system into modular functional components including separate modules for data entry, validation, storage, retrieval, and reporting. Each module operates independently with well-defined interfaces, allowing the system to scale data capacity while managing complexity through modular architecture.
Solution Approach 2:
The patent creates a universal database framework that can accommodate multiple types of medical data (clinical reports, images, patient information) from different sources and formats through a standardized schema. This multi-functional database structure allows scaling across users and organizations without proportionally increasing system complexity.
3Ease of operation
If cloud-based systems are used to generate reports, then remote accessibility is improved, but integration of disparate medical information deteriorates
Solution Approach 1:
The patent introduces intermediary integration layers and standardized data exchange protocols that mediate between disparate medical information sources and the cloud-based reporting system. These intermediaries ensure seamless integration of patient data, clinical information, and imaging data while maintaining remote accessibility capabilities.
4Adaptability or versatility
If manual report generation is used, then customization flexibility is improved, but reporting time and productivity deteriorate
Solution Approach 1:
The patent implements preliminary actions by pre-configuring report templates, data validation rules, and formatting standards that are prepared in advance. This allows the system to automatically generate customized reports by filling pre-prepared structures with patient-specific data, significantly improving reporting speed while maintaining customization flexibility.
Solution Approach 2:
The patent enables self-service report generation where the system automatically retrieves patient data, applies appropriate templates and formatting rules, and generates finalized reports without requiring manual intervention for each report. This automation maintains adaptability through configurable templates while dramatically improving productivity.
Data Source
AI summary
In one embodiment, system for aggregating, analyzing, and reporting medical information includes a front end module for managing a user interface, a back end module for exchanging patient information with a clinic record system and obtaining one or more medical images therefrom, a machine learning/artificial intelligence (ML/AI) engine for analyzing said one or more medical images and generating analysis results, and a report generator for generating a report that includes the analysis results. The ML/AI engine can include an anatomical plane classifier such as a 20+2 classifier, and the anatomical structure classifier can apply sematic segmentation.