Image analysis and artificial intelligence in infectious disease diagnostics

Abstract

Background: Microbiologists are valued for their time-honed skills in image analysis, including identification of pathogens and inflammatory context in Gram stains, ova and parasite preparations, blood smears and histopathologic slides. They also must classify colony growth on a variety of agar plates for triage and assessment. Recent advances in image analysis, in particular application of artificial intelligence (AI), have the potential to automate these processes and support more timely and accurate diagnoses.

Objectives: To review current AI-based image analysis as applied to clinical microbiology; and to discuss future trends in the field.

Sources: Material sourced for this review included peer-reviewed literature annotated in the PubMed or Google Scholar databases and preprint articles from bioRxiv. Articles describing use of AI for analysis of images used in infectious disease diagnostics were reviewed.

Content: We describe application of machine learning towards analysis of different types of microbiologic image data. Specifically, we outline progress in smear and plate interpretation as well as the potential for AI diagnostic applications in the clinical microbiology laboratory.

Implications: Combined with automation, we predict that AI algorithms will be used in the future to prescreen and preclassify image data, thereby increasing productivity and enabling more accurate diagnoses through collaboration between the AI and the microbiologist. Once developed, image-based AI analysis is inexpensive and amenable to local and remote diagnostic use.

Keywords: Artificiall intelligence; Deep learning; Gram stain; Machine learning.

Figure 1.. Two-dimensional representation of Gram-stain classifications highlights ability to further subcategorize data.

Here, a t-statistic nearest neighbor embedding (tSNE) algorithm was used to visualize Gram-stain crop classification data in two-dimensions. Clusters of Gram-positive cocci in long (left, lower) or short chains (left, upper) are readily recognizable. Similarly, Gram-negative rods (right, lower) can be distinguished from coccobacilli (right, upper). This information could later be used to provide additional probabilistic subclassification of organisms.

Figure 2.. Technologist Assist.

This platform is envisioned as a way for AI and clinical laboratory scientists to collaborate. After analysis of a smear by a trained AI, diagnostic image crops are displayed for technologist review along with a probabilistic differential Gram stain diagnosis. The technologist can then review images and select one or more diagnoses, which would then cross over from the laboratory information system to the patient report. The microbiologist also has the option of reviewing the smear directly in the presumptively rare instances where there is discordance between their assessment of the image crops and the offered AI interpretations or if there were diagnostic uncertainty.

Figure 3.. Example of probabilistic AI-assisted Gram stain reports.

The probabilistic report provides a weighted differential diagnosis that can draw attention to infections that require special therapeutic intervention.