ML Algorithm Retraining via Parsed Radiology Report Labels
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Solution Overview
Problem
Traditional training methods for machine-learning algorithms in the healthcare field, particularly in radiology, fail to account for data heterogeneity, leading to inaccurate results and requiring time-consuming manual corrections by clinical experts.
Innovation Solution
A computer-implemented method that compares validated radiology report labels with algorithm predictions to assess and retrain machine-learning algorithms, using validated labels from parsed radiology reports to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional training methods are used for ML algorithms, then training speed is fast, but accuracy and adaptability to clinical heterogeneity deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically parsing radiology reports to extract validated diagnosis labels before the ML algorithm training process. These pre-extracted labels are stored and ready for use, eliminating the need for manual annotation during training preparation. This preliminary extraction of ground truth data from existing clinical reports enables faster training cycles while maintaining high accuracy through clinically validated labels.
Solution Approach 2:
The system implements self-service by enabling the ML algorithm to automatically evaluate its own performance through comparison with parsed radiology report labels. The algorithm undergoes continuous self-assessment and self-retraining using real-world clinical data from the deployment environment, adapting to heterogeneity without external manual intervention. This self-service mechanism improves accuracy over time while reducing training time through automated feedback loops.
2Measurement precision
If manual evaluation by clinical experts is performed, then assessment accuracy is high, but productivity and efficiency deteriorate
Solution Approach 1:
The system replaces the mechanical process of manual expert evaluation with an automated computational approach. Instead of clinicians manually reviewing and labeling cases, the system uses natural language processing to parse radiology reports and extract diagnosis labels automatically. This substitution maintains measurement precision by using validated clinical terminology while dramatically improving productivity through automated high-volume processing of evaluation cases.
Solution Approach 2:
The system introduces an intermediary automated evaluation layer between the ML algorithm and final performance assessment. This intermediary automatically compares algorithm predictions with parsed radiology report labels, generating performance metrics without requiring direct manual expert involvement for each case. The intermediary maintains accuracy by using clinically validated labels while improving throughput through automated comparison and metric generation.
3Reliability
If single case feedback is collected from experts, then implementation effort is reduced, but reliability and completeness of feedback deteriorate
Solution Approach 1:
The system implements a universal automated feedback mechanism that processes all evaluation cases uniformly through the same parsing and comparison pipeline. Instead of relying on selective expert feedback for individual cases, the system universally applies automated label extraction and performance comparison across the entire dataset. This universal approach ensures complete and reliable feedback coverage while managing complexity through standardized automated processes that handle diverse cases consistently.
Data Source
AI summary
A trained ML algorithm may be configured to process medical imaging data to generate a prediction of at least one diagnosis of a patient based on the medical imaging data. The prediction of the at least one diagnosis of the patient is compared with a validated label of the at least one diagnosis of the patient and the performance of the trained ML algorithm is determined based on the comparison. The validated label of the at least one diagnosis of the patient is obtained by parsing a validated radiology report of the patient and the medical imaging data is associated with the validated radiology report. If the performance of the trained ML algorithm is lower than a threshold, an update of parameters of the trained ML algorithm may be triggered based on the validated label.