Automated Whole-Slide Scanner for Gram Stain Pathogen Detection
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Solution Overview
Problem
Current methods for diagnosing blood pathogens are time-consuming and often fail to detect low concentrations of pathogens, leading to delayed treatment and potential misdiagnosis due to reliance on manual microscopic evaluation and lengthy culturing processes.
Innovation Solution
A digital, computer-assisted system for whole-slide stain analysis that rapidly images and processes stained samples with high resolution, using machine learning classifiers to detect pathogens simultaneously with imaging, thereby reducing culture time and improving diagnostic efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual microscopic evaluation is used to detect pathogens, then diagnostic accuracy can be achieved with expert analysis, but the process is time-consuming and requires lengthy culturing periods
Solution Approach 1:
The system performs preliminary staining and automated imaging of the sample before traditional culture completion. By preparing stained slides in advance and using automated whole-slide scanning with machine learning classification, the system can detect pathogens earlier than waiting for culture results, reducing the overall diagnostic time while maintaining accuracy
Solution Approach 2:
The patent replaces manual microscopic evaluation with an automated digital system comprising a whole-slide scanner, image processing unit, and machine learning classifier. This substitution eliminates the need for expert manual analysis, enabling rapid automated detection and classification of pathogens without the time constraints of traditional manual methods
2Measurement precision
If manual microscopic evaluation is performed by trained staff, then pathogen detection can be achieved, but staff shortage and fatigue reduce sensitivity and increase processing time
Solution Approach 1:
The system performs self-service by using machine learning classifiers to automatically detect and classify pathogens without requiring human experts. The automated system processes slides continuously without fatigue, maintaining consistent sensitivity and throughput, and eliminates the need for trained microbiology staff to perform manual evaluation
Solution Approach 2:
The patent replaces the manual evaluation system with an automated digital system that uses machine learning algorithms to analyze stained slides. This substitution resolves the limitations of staff shortage and fatigue by providing an always-available, consistent, and scalable automated analysis capability that maintains high detection sensitivity regardless of workload
3Measurement precision
If high optical magnification is used to evaluate microbiological organisms, then detection sensitivity can be improved, but the evaluation time increases and low concentration organisms may still be missed
Solution Approach 1:
The system segments the slide into multiple regions of interest and processes them in parallel using distributed computing. The whole-slide scanner captures images at high magnification, and the machine learning classifier divides the analysis into manageable segments that can be processed simultaneously, maintaining detection sensitivity while reducing total evaluation time through parallel processing
4Measurement precision
If traditional culture methods are used to diagnose blood pathogens, then comprehensive pathogen identification can be achieved, but treatment is delayed while waiting for culture results
Solution Approach 1:
The system performs preliminary detection using automated whole-slide scanning and machine learning classification on stained samples. This preliminary action provides early pathogen detection before traditional culture completion, enabling earlier treatment initiation while maintaining identification accuracy through the classifier's ability to distinguish pathogenic organisms from normal flora
Data Source
AI summary
An apparatus configured to process a sample to detect a pathogen receives a slide with the sample on the slide, in which the sample has been stained with one or more of a Gram stain, an Acid-Fast stain, or a Giemsa stain. An area of at least 5 mm2 of the sample is imaged at a rate of at least 15 mm2 per minute and a resolution of 0.3 pm or better to generate one or more images. The one or more images of the sample are processed with a classifier configured to detect the pathogen in the sample. In some embodiments, a plurality of cultured and stained samples on a plurality of slides are imaged at the resolution and processed with the classifier, which can increase the area processed and analyzed in order to decrease the culture time and corresponding time to diagnose the patient.