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External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review.

Alice C Yu¹, Bahram Mohajer¹, John Eng¹

¹Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287.

Radiology. Artificial Intelligence

|June 2, 2022

Summary

This summary is machine-generated.

Deep learning (DL) algorithms for radiologic diagnosis often show decreased performance on external datasets. Most studies found performance drops, with many experiencing substantial decreases, highlighting generalizability challenges.

Keywords:

Computer Applications–Detection/Diagnosis Computer Applications–General (Informatics)Diagnosis Epidemiology Informatics Meta-Analysis Neural Networks Technology Assessment

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Area of Science:

Radiology
Artificial Intelligence
Medical Informatics

Background:

Deep learning (DL) algorithms are increasingly used for radiologic diagnosis.
Assessing the generalizability of these algorithms is crucial for clinical adoption.

Purpose of the Study:

To systematically review and assess the generalizability of published deep learning algorithms for image-based radiologic diagnosis.
To evaluate the performance decrease of DL algorithms when applied to external datasets.

Main Methods:

Systematic review of PubMed-indexed studies (Jan 2015-Apr 2021) on DL algorithms for radiologic diagnosis with external validation.
Exclusion of studies using nonimaging features or non-DL methods.
Extraction of internal and external performance measures and study characteristics.

Main Results:

Eighty-three studies (86 algorithms) were included.
81% of algorithms showed decreased external performance compared to internal performance.
49% had at least a modest decrease (≥0.05), and 24% had a substantial decrease (≥0.10).

Conclusions:

The majority of published DL algorithms for radiologic diagnosis exhibit diminished performance on external datasets.
A significant portion of algorithms experience substantial performance decreases, indicating generalizability issues.
No specific study characteristics were found to predict the performance difference between internal and external validation.