Histopathology Slide Screening Using a Normal Tissue Anomaly Model
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Solution Overview
Problem
Current histopathology screening methods require significant human intervention and are inefficient in identifying subtle abnormalities in tissue samples, necessitating a more automated and accurate prescreening process.
Innovation Solution
A system utilizing an anomaly detection model trained on normal tissue images to generate a deviation score, employing techniques like isolation forests and latent feature extraction, enabling automated identification and localization of abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review of histopathology slides is performed by pathologists, then diagnostic accuracy is maintained, but time consumption and labor costs increase significantly
Solution Approach 1:
The patent creates a digital copy (image data) of the physical histopathology slide and uses this copy for automated analysis by AI algorithms. This allows the original slide to be preserved while enabling rapid digital processing, thus increasing productivity without compromising diagnostic accuracy. The system captures an image of the slide and processes this digital representation instead of requiring manual examination of the physical slide.
Solution Approach 2:
The patent replaces the manual mechanical process of pathologist review with an automated computer-based system. The AI model automatically analyzes the image data, identifies regions of interest, and generates preliminary diagnoses, substituting the mechanical human review process with an automated computational system that operates faster and at lower cost.
2Reliability
If full manual review of all slides is conducted, then diagnostic accuracy is ensured, but resource utilization becomes inefficient
Solution Approach 1:
The patent applies local quality by focusing computational resources only on specific regions of the slide that contain suspicious or abnormal areas. The AI model identifies regions of interest and directs detailed analysis only to these local areas rather than uniformly processing the entire slide, thus maintaining diagnostic accuracy while reducing overall computational complexity and resource requirements.
Solution Approach 2:
The patent performs preliminary analysis using the trained AI model to identify suspicious regions before conducting detailed review. This preliminary action filters out normal slides that don't require full manual review, allowing pathologists to focus only on cases that need their expertise, thereby maintaining diagnostic accuracy while optimizing resource utilization.
3Productivity
If AI-based automated screening is implemented, then productivity increases, but risk of false positives and false negatives increases
Solution Approach 1:
The patent uses the AI model as an intermediary between the slide image and the final diagnostic decision. Rather than replacing the pathologist completely, the AI serves as an assistant that highlights suspicious regions and provides preliminary findings, which the pathologist then verifies. This intermediary role allows the system to maintain high throughput while preserving diagnostic accuracy through human oversight.
Solution Approach 2:
The system incorporates feedback mechanisms where pathologist reviews of AI-generated findings are fed back into the system to continuously improve the AI model's accuracy. This feedback loop allows the system to learn from errors and reduce false positives and false negatives over time, maintaining productivity while improving reliability through iterative refinement.
Data Source
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AI summary
Systems and methods are provided for screening histopathology tissue samples. An anomaly detection system is trained on a plurality of training images. Each of the plurality of training images represents a tissue sample that is substantially free of abnormalities. A test image, representing a tissue sample, is provided to the anomaly detection system. A deviation from normal score is generated for at least a portion of the test image. The deviation from normal score represents a degree of abnormality in the tissue sample represented by the test image.