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Design and Analysis Methods for Trials with AI-Based Diagnostic Devices for Breast Cancer.

Lu Liu1, Kevin J Parker2, Sin-Ho Jung1

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA.

Journal of Personalized Medicine
|November 27, 2021
PubMed
Summary

This study introduces statistical methods for clinical trials evaluating AI for breast cancer diagnosis. The proposed methods ensure accurate AI performance assessment, aiding radiologists and improving diagnostic efficiency.

Keywords:
artificial intelligence (AI)breast cancerclinical device trialconcordance rategeneralized estimating equationsample size calculationstatistical test

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Oncology

Background:

  • Accurate interpretation of diagnostic images is crucial for cancer diagnosis and requires extensive radiologist training.
  • Advancements in big data, machine learning, and AI are leading to the development of AI-based imaging devices for diagnostics.

Purpose of the Study:

  • To propose statistical design and analysis methods for clinical trials evaluating AI-based breast cancer diagnostic devices.
  • To compare the diagnostic concordance of AI devices with human radiologists.

Main Methods:

  • Development of statistical testing methods for evaluating AI diagnostic accuracy.
  • Design of sample size calculations for clinical trials to ensure adequate statistical power.
  • Conducting extensive numerical studies to validate proposed methods.

Main Results:

  • Proposed statistical testing methods accurately control the type I error rate.
  • Design methods provide required sample sizes with statistical powers close to nominal levels.
  • The methods were successfully applied to design and analyze a real-world device trial.

Conclusions:

  • The proposed statistical framework is effective for evaluating AI-based breast cancer diagnostic tools.
  • These methods can support the integration of AI into clinical practice to enhance radiologist performance and manage workloads.
  • The validated statistical approaches facilitate robust clinical trial design for AI medical devices.