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Automated Interpretation of Blood Culture Gram Stains

Microscopic interpretation of stained smears is one of the most operator-dependent and time-intensive activities in the clinical microbiology laboratory. [...] With consolidation of hospital systems, increasing workloads, and the potential unavailability of highly trained microbiologists on site [...], automated image collection paired with computational interpretation of Gram stains to augment and complement manual testing would provide benefit. [...] Practically, automated Gram stain interpretation requires both automated slide imaging and automated image analysis. [...] A total of 468 deidentified Gram-stained slides from positive blood cultures were collected from the clinical microbiology laboratory at Beth Israel Deaconess Medical Center [...]. All slides were imaged without coverslips using a Metafer Slide Scanning and Imaging platform [...] with a 140-slide-capacity automated slide loader equipped with a 40 magnification Plan-Neofluar objective [...]. Slides were selected based on the presence of any of the three most common morphotypes observed in bloodstream infection: Gram-positive cocci in clusters, Gram-positive cocci in pairs and chains, and Gram-negative rods. [...] We performed image acquisition on a Metafer [...] based on a robust Gram stain-compatible autofocus system, ability to sample multiple distributed positions on a slide to account for variations in specimen distribution, and automated slide loading capability to enable high-throughput slide scanning. Clinically, Gram stains are read under oil immersion. However, semicontinuous addition of oil during automated microscopy was undesirable. In preliminary experiments performed with slides that were not coverslipped (data not shown), we determined that the 40 dry objective provided sufficient resolution for machine learning applications based on our prior experience [...]. Therefore, we selected use of the 40x air objective for image acquisition, thus avoiding the requirement for oil immersion and allowing us to capture a larger field of view in each image. [...] Here, we demonstrated that the Metafer Slide Scanning and Imaging platform provides a robust automated image acquisition system, capable of providing sufficient resolution for Gram stain analysis using a 40 dry objective. [...] Overall, we found that our trained model performed well on whole-slide image classification. Where cells were detected, we achieved an overall classification accuracy of 92.5% and a specificity of 93% for all classification labels with no human intervention. [...]

Cited From: Journal of clinical microbiology (2018) Read more...
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