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Spectrum of errors in nodule detection and characterization using machine learning: A pictorial essay.

Jabi E Shriki¹, Ted Selker², Kristina Crothers³

¹University of Washington School of Medicine, United States; Department of Radiology, Puget Veterans Administration Healthcare System, United States.

Current Problems in Diagnostic Radiology

|December 19, 2024

Summary

Computer-aided nodule detection (CAD) software improves accuracy but can cause errors. Radiologists must understand these potential pitfalls in nodule detection, localization, and characterization for safe clinical use.

Keywords:

AI lung nodule detection ClearRead lung nodule detection lung nodule machine learning

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence in Medicine

Background:

Computer-aided nodule detection (CAD) software enhances diagnostic performance in academic settings.
Widespread clinical adoption necessitates understanding CAD system limitations and potential errors.

Purpose of the Study:

To review the spectrum of errors associated with computer-aided nodule detection (CAD) systems.
To familiarize radiologists with potential pitfalls encountered in routine clinical practice.

Main Methods:

Review of clinical practice cases involving CAD software.
Categorization of errors into detection, localization, and characterization issues.
Illustrative case examples to demonstrate specific error types.

Main Results:

Identified errors in nodule detection, including false positives and negatives.
Observed inaccuracies in nodule localization, affecting precise measurement.
Encountered challenges in nodule characterization, potentially impacting diagnostic classification.

Conclusions:

Despite advancements, CAD software can generate errors in detection, localization, and characterization.
Radiologists must maintain critical review of images, being aware of AI-generated errors.
Mindful oversight is crucial for safe and effective integration of CAD systems in clinical workflows.