Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls.

Area of Science:

• Medical Image Analysis
• Machine Learning in Healthcare
• Computational Pathology

Background:

• Machine learning models are increasingly used in medical image analysis for diagnosis and prognosis.
• However, methodological pitfalls can compromise model generalizability and lead to inaccurate predictions.
• Common pitfalls include violating independence assumptions, inappropriate performance evaluation, and batch effects.

Purpose of the Study:

• To investigate the impact of three key methodological pitfalls on the generalizability of machine learning models.
• To quantitatively illustrate how these pitfalls affect model performance and reliability.
• To emphasize the importance of avoiding these pitfalls for developing robust medical AI.

Main Methods:

• Retrospective datasets including CT, histopathologic analysis, and radiography were utilized.
• Machine learning models were developed with and without the identified methodological pitfalls.
• Performance was measured using the F1 score, with statistical comparisons using the Wilcoxon rank sum test where applicable.

Main Results:

• Violating independence assumptions (e.g., oversampling before splitting data) inflated F1 scores by up to 71.2% in cancer prediction tasks.
• Inappropriate data splitting improved F1 scores superficially by 21.8% but did not guarantee high-quality segmentation.
• Batch effects severely impacted model performance, with a pneumonia detection model misclassifying 96.14% of new healthy patient samples.

Conclusions:

• Methodological pitfalls, often undetectable during internal validation, lead to overoptimistic performance metrics and inaccurate real-world predictions.
• Understanding and actively avoiding these pitfalls is essential for developing trustworthy and generalizable machine learning models in medical imaging.
• This study underscores the need for rigorous methodology in developing AI for diagnosis and prognosis.