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Gram staining decipherment using an artificial intelligence-powered smartphone-based application.

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Summary

An artificial intelligence system accurately interprets Gram-stained urine images captured by smartphones, aiding in rapid bacterial identification for empirical antimicrobial selection. Accuracy depends on the staining method, not the imaging device.

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artificial intelligencecomputer-aided diagnosisgram stainingurinary tract infection

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Area of Science:

  • Microbiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Gram staining offers rapid microbiological insights crucial for empirical antimicrobial selection.
  • Interpretation of Gram stains often requires specialized expertise, which may not be readily available.
  • Existing methods lack accessibility and speed for immediate clinical decision-making.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence (AI)-based computer-aided diagnosis system for interpreting Gram-stained urine samples.
  • To assess the system's accuracy in classifying bacterial morphology and inferring species from images captured using smartphones.
  • To compare the performance of different imaging devices and Gram staining methods for AI-driven urinalysis.

Main Methods:

  • Developed an AI system trained on microscopic images of Gram-stained urine captured via iPhone using the Bartholomew and Mittwer method.
  • Collected and analyzed urine specimens from two hospitals, generating validation images using five devices (four smartphones, one microscopic camera).
  • Compared two staining methods (Bartholomew and Mittwer, Favor) and evaluated the impact of using a micrometer on image accuracy.

Main Results:

  • The AI system achieved an overall accuracy of 0.804 for bacterial morphology (Class 1) and 0.640 for species-level classification (Class 2).
  • Images captured without a micrometer demonstrated higher accuracy (0.885 for Class 1, 0.666 for Class 2).
  • The Bartholomew and Mittwer staining method yielded better accuracy and kappa coefficients compared to the Favor method; accuracy was primarily influenced by the staining method, not the imaging device.

Conclusions:

  • An AI-powered diagnostic support system trained on smartphone-captured Gram-stained urine images can effectively classify bacterial morphology and species.
  • Consistent use of the same staining method as employed during AI model training is critical for optimal performance.
  • The study highlights the potential of accessible imaging devices and AI for improving the speed and accuracy of microbiological diagnostics in resource-limited settings.