Evidence map›Paper›PMID 41910323›Full record

ArticleJournal of clinical microbiology2026

Digital holographic microscopy: use of artificial intelligence to enable rapid blood culture Gram stain digitization and interpretation.

Kenneth P Smith, Erin Boulanger, Michael Elkan, Jeffrey Fink, Sofia Cagina, Alena Shamsheyeva, Kyle Spafford, Richard Stahl, Oleg Gusyatin

Abstract readEvaluation Study
In one paragraph

Article in Journal of clinical microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Kenneth P SmithInfectious Disease Diagnostics Laboratory, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.ORCID 0009-0007-7175-1652
Erin BoulangerInfectious Disease Diagnostics Laboratory, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Michael ElkanInfectious Disease Diagnostics Laboratory, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Jeffrey FinkInfectious Disease Diagnostics Laboratory, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Sofia CaginaMacula Vision Systems, Tucson, Arizona, USA.
Alena ShamsheyevaMacula Vision Systems, Tucson, Arizona, USA.
Kyle SpaffordMacula Vision Systems, Tucson, Arizona, USA.
Richard StahlMacula Vision Systems, Tucson, Arizona, USA.
Oleg GusyatinMacula Vision Systems, Tucson, Arizona, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gram staining is one of the most commonly performed tests in the clinical microbiology laboratory. Results are used to both guide empiric antimicrobial therapy as well as downstream laboratory procedures, including culture workup. Despite its ubiquity, Gram staining is time-intensive, operator-dependent, and demands a rapid turnaround time. As such, it is an ideal target for automation. However, currently available automated microscopes struggle to rapidly and accurately collect image data due to physical challenges posed by focusing on micron-sized bacteria on slides with variable stain thickness and intensity. Here, we present the development of digital holographic microscopy, an optical system that scans slides using a multispectral coherent light instead of traditional lenses. This allows for the collection of all image data from a large portion (50%) of a slide. These data are then reconstructed and displayed as full color images. We additionally demonstrate that images collected by this method are qualitatively equivalent to those collected with optical microscopy and can be interpreted by individuals with no previous exposure to automated microscopy.IMPORTANCEGram staining is a commonly performed test that demands significant time and experience. Automation of this task is therefore attractive but has been hindered by physical limitations of traditional optics as currently implemented in automated microscopes. Here, we present the development of digital holographic microscopy, which can collect and display image data without the need for optical focusing. This technology may therefore provide benefit to clinical microbiology laboratories by increasing efficiency of the Gram staining process.

Indexed as

Artificial IntelligenceBacteriological TechniquesBloodGentian VioletHolographyImage Processing, Computer-AssistedMicroscopyPhenazinesStaining and LabelingAutomation, LaboratoryHumansQuantitative Phase ImagingGentian VioletGram's stainPhenazinesblood cultureGram stainslide scanning

Identifiers

PMID41910323
PMCPMC13170159

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.