(White Paper)

Introduction: Why Antimicrobial Susceptibility Testing (AST) Matters

Antimicrobial susceptibility testing (AST) plays a central role in modern infectious disease management. It allows clinicians to determine whether a specific pathogen is susceptible, intermediate, or resistant to a given antimicrobial agent, ensuring that patients receive the most appropriate therapy. In the face of growing antimicrobial resistance (AMR), AST is not only essential for individual patient outcomes but also critical for public health, antimicrobial stewardship, and surveillance.

Among the various AST techniques, the disk diffusion method (Kirby-Bauer) remains widely used due to its simplicity and cost-effectiveness. The key output of this method is the diameter of the Zone of Inhibition (ZOI)—a clear area around an antibiotic-impregnated disk that indicates bacterial growth suppression. However, traditional ZOI measurement using rulers or calipers is subject to human variability and lacks scalability, especially under high-throughput demands.

Recent advances in artificial intelligence (AI) and automation are redefining how laboratories perform ZOI analysis. These innovations bring new levels of precision, speed, and consistency to a decades-old methodology, ensuring that AST keeps pace with the demands of precision medicine and global AMR challenges.

Solve the Challenges of Manual Zone of Inhibition (ZOI) Measurement

Feedback from users across clinical and research environments highlights several persistent challenges with traditional, manual ZOI measurement:

❌ Time-consuming – Manual measurements require significant time and effort.
❌ Lack of accuracy and consistency – Human errors lead to inconsistent results.
❌ Difficult data recording & management – Keeping track of measurements can be cumbersome.
❌ High cost of automation tools – Traditional solutions are expensive and inaccessible to many institutions.

In response to these concerns, tools that leverage automation and AI—such as those developed by third-party providers—have emerged as cost-effective solutions:

✅ Automated measurements – AI eliminates manual errors and accelerates the process.
✅ Consistent & accurate results – Ensures reproducibility across users and sites.
✅ Easy data management – Digital workflows enable simplified storage, tracking, and analysis.
✅ Affordable & efficient – Lower-cost platforms make automation more accessible to labs of all sizes.

AI/Tech Deep Dive: Automating the Zone of Inhibition

The process of measuring ZOI is deceptively simple but critically important. Minor inconsistencies in diameter readings can result in incorrect susceptibility categorizations, impacting clinical decisions. Manual measurement is vulnerable to inter-operator variability, inconsistent lighting, and angle-dependent errors. To address these issues, modern AI and imaging technologies now provide automated solutions that ensure reliable ZOI interpretation aligned with international standards like CLSI and EUCAST.

Key Technological Innovations

• Computer Vision & Image Analysis: High-resolution imaging combined with edge-detection algorithms allows accurate identification of inhibition zone boundaries.
• Machine Learning: AI models trained on annotated AST datasets can recognize and measure ZOIs with high reproducibility, even in challenging or borderline cases.
• Integration with Laboratory Information Systems (LIS): Automated ZOI data can be fed directly into LIS for rapid reporting and surveillance integration.
• Mobile and Offline Capabilities: Lightweight AI apps, including those used in low-resource settings, allow ZOI analysis with smartphones—expanding access to quality AST in rural or remote areas.

These technologies not only reduce measurement time but also eliminate common human errors, helping laboratories standardize reporting across shifts, sites, and even continents.

Expected Use Cases: Clinical and Research Applications

AI-powered ZOI measurement technologies can transform both routine and specialized settings:

Clinical Microbiology Labs

• High-throughput AST workflows can benefit from automation, enabling faster turnaround and reduced workload.
• Integration with antimicrobial stewardship systems ensures immediate action on resistance trends.

Point-of-Care or Field Settings

• Mobile apps running on smartphones allow ZOI analysis in low-infrastructure environments.
• Ideal for remote clinics, humanitarian missions, and LMICs where conventional AST infrastructure is limited.

Research and Surveillance

• Automated ZOI measurement tools generate structured data for resistance trend analysis.
• Useful for national and international AMR surveillance programs and outbreak investigations.

Future Outlook: What Comes Next in AST Innovation?

The future of AST is shaped by convergence—combining phenotypic analysis like ZOI measurement with genotypic data from whole-genome sequencing or rapid molecular diagnostics. AI will likely play a central role in integrating these diverse data streams to offer faster, more nuanced interpretations.

Key anticipated developments include:

• Real-time AST Platforms: Using AI-enabled incubators with continuous imaging to estimate susceptibility in hours instead of days.
• Microfluidic Lab-on-a-Chip Devices: Paired with AI, these allow AST to be miniaturized and multiplexed.
• Predictive Analytics: Leveraging AI to forecast emerging resistance patterns from aggregated ZOI data across geographies.

As automation and AI become integral to AST workflows, we move closer to the vision of rapid, reliable, and globally accessible susceptibility testing.

Conclusion & Call to Action

Key Takeaways:

• AST remains a cornerstone in the fight against infectious diseases and AMR.
• Disk diffusion and ZOI analysis are widely used but historically manual and variable.
• AI and automation now offer scalable, accurate, and efficient alternatives for ZOI measurement.
• These technologies are already impacting clinical, field, and research environments.

Call to Action: At CarbConnect, we’re exploring and integrating these transformative technologies into tools that serve the needs of healthcare professionals and researchers worldwide. One such example is ZOI Pro, our AI-powered application for zone of inhibition measurement that combines speed, accuracy, and ease of use to support daily microbiology work. Whether you’re working in a high-volume clinical lab, a rural clinic, or a university lab—AI-powered ZOI analysis can elevate your microbiology workflow.

🔬 Learn more at https://www.carb-connect.com/applications/ZOI_PRO

Selected References:

• CLSI. Performance Standards for Antimicrobial Susceptibility Testing. CLSI Supplement M100. 34th ed. 2024.
• EUCAST. Breakpoint Tables for Interpretation of MICs and Zone Diameters. Version 13.0. 2023.
• Jorgensen JH, Ferraro MJ. Antimicrobial susceptibility testing: general principles and contemporary practices. Clin Infect Dis. 2009;49(11):1749–1755.
• Sundqvist M, et al. AI-powered antimicrobial susceptibility testing: A rapid solution for AMR. J Clin Microbiol. 2022.
• Van Belkum A, et al. Next-generation antimicrobial susceptibility testing: a new paradigm for resistance detection and surveillance. Nat Rev Microbiol. 2020.

Disclaimer: The content in this white paper is intended solely for informational purposes. CarbGeM Inc. disclaims any liability for any direct or indirect damages arising from the use or reliance on the information provided. The opinions expressed are those of the authors and do not necessarily reflect the official stance or policies of CarbGeM Inc.