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Updated: Jul 21, 2026

Artificial Intelligence Approaches to Assessing Primary Cilia

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

Image analysis and artificial intelligence in infectious disease diagnostics.

K P Smith¹, J E Kirby¹

¹Department of Pathology, Beth Israel Deaconess Medical Center, USA; Harvard Medical School, Boston, MA, USA.

Clinical Microbiology and Infection : the Official Publication of the European Society of Clinical Microbiology and Infectious Diseases

|March 28, 2020

Summary

Artificial intelligence (AI) is revolutionizing clinical microbiology by automating image analysis for pathogen identification and colony classification. This technology promises to enhance diagnostic accuracy and laboratory efficiency through AI-microbiologist collaboration.

Keywords:

Artificiall intelligence Deep learning Gram stain Machine learning

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Last Updated: Jul 21, 2026

Artificial Intelligence Approaches to Assessing Primary Cilia

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Published on: May 1, 2021

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

Clinical Microbiology
Medical Imaging
Artificial Intelligence

Background:

Microbiologists possess critical image analysis skills for diagnosing infectious diseases.
Current methods involve manual identification of pathogens and colony morphology on various media.
Artificial intelligence (AI) offers potential for automating these image analysis tasks.

Purpose of the Study:

To review current applications of AI in clinical microbiology image analysis.
To discuss emerging trends and future directions in AI for microbiology.

Main Methods:

Literature review of peer-reviewed articles and preprints.
Searches conducted in PubMed and Google Scholar databases.
Focus on AI applications for infectious disease diagnostic imaging.

Main Results:

AI, particularly machine learning, is being applied to analyze diverse microbiologic image data.
Progress has been made in AI-driven interpretation of smears and microbial cultures.
AI demonstrates potential for widespread diagnostic applications in clinical microbiology labs.

Conclusions:

AI algorithms, combined with automation, will likely prescreen and preclassify image data.
This collaboration between AI and microbiologists is expected to boost productivity and diagnostic accuracy.
Image-based AI analysis is cost-effective and suitable for both local and remote diagnostic settings.