Predictive Model for Breast Image Reading Time and Complexity
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Solution Overview
Problem
The exponential increase in radiological image data and variability in reading times among radiologists due to individual factors and image complexity make it challenging for breast care centers to optimally distribute workload among radiologists.
Innovation Solution
A system and method that utilize a predictive model trained on data from various sources, including image acquisition workstations and healthcare professional profiles, to estimate the reading time and complexity of mammographic exams, thereby enabling better workload distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If radiologists manually review radiological images without predictive assistance, then reading accuracy can be maintained, but reading time variability increases and workload distribution becomes inefficient
Solution Approach 1:
The system performs preliminary analysis by training predictive models on historical reading data before actual workload distribution. The models pre-calculate expected reading times and complexity scores for different image types, allowing the system to proactively optimize task assignment rather than reactively managing variability.
Solution Approach 2:
The predictive model acts as an intermediary between the radiological images and the radiologists. It processes image data and radiologist profile data to generate predictions about reading time and complexity, which then inform the workload distribution decisions, mediating between raw data and human decision-making.
2Reliability
If radiological imaging data volume increases exponentially, then diagnostic capability improves, but the time required to review images increases
Solution Approach 1:
The system changes the parameter of time estimation from a fixed value to a dynamic prediction based on multiple factors. By using machine learning models that analyze image characteristics, radiologist expertise levels, and historical performance data, the system adapts time estimates to match actual reading requirements, allowing better planning despite increasing data volumes.
3Ease of operation
If workload is distributed without predictive modeling, then simplicity of assignment is maintained, but optimization of radiologist utilization deteriorates
Solution Approach 1:
The system enables self-service workload optimization by automatically generating predictions and recommendations without requiring complex manual intervention. The predictive model autonomously analyzes data and provides actionable insights, allowing the system to serve itself in optimizing assignments while maintaining ease of operation for users.
Data Source
AI summary
Examples of the present disclosure describe systems and methods for predicting the reading time and/or reading complexity of a breast image. In aspects, a first set of data relating to the reading time of breast images may be collected from one or more data sources, such as image acquisition workstations, image review workstations, and healthcare professional profile data. The first set of data may be used to train a predictive model to predict/estimate an expected reading time and/or an expected reading complexity for various breast images. Subsequently, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. The trained predictive model may output an estimated reading time and/or reading complexity for the breast image. The output of the trained predictive model may be used to prioritize mammographic studies or optimize the utilization of available time for radiologists.