Understanding metric-related pitfalls in image analysis validation

Area of Science:

• Artificial intelligence
• Biomedical image analysis
• Scientific validation

Background:

• Validation metrics are critical for scientific progress and AI translation.
• Inadequate metric selection, particularly in image analysis, is a growing concern.
• Existing knowledge on validation metric limitations is fragmented and difficult to access.

Purpose of the Study:

• To provide a centralized, reliable resource on pitfalls in validation metrics for image analysis.
• To enhance understanding and improve the selection of validation metrics in scientific research.
• To address the gap between AI research and its practical application through better validation.

Main Methods:

• A multistage Delphi process involving a multidisciplinary expert consortium.
• Extensive community feedback incorporated to refine findings.
• Development of a domain-agnostic taxonomy for categorizing pitfalls.

Main Results:

• Identification and categorization of common pitfalls in validation metrics for image analysis.
• Creation of a comprehensive resource for researchers.
• Generalizable pitfalls across various application domains, not limited to biomedical imaging.

Conclusions:

• Improved comprehension of validation metric pitfalls enhances scientific rigor.
• Accessible, reliable information on metric selection is vital for AI translation.
• Standardized understanding of validation is key for advancing image analysis research.