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Preparing Medical Imaging Data for Machine Learning.

Martin J Willemink1, Wojciech A Koszek1, Cailin Hardell1

  • 1From the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, S-072, Stanford, CA 94305-5105 (M.J.W., D.F., D.L.R., M.P.L.); Segmed, Menlo Park, Calif (M.J.W., W.A.K., C.H., J.W.); School of Engineering, Stanford University, Stanford, Calif (J.W.); Institute of Cognitive Neuroscience, University College London, London, England (H.H.); Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (L.R.F.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health, Clinical Center, Bethesda, Md (R.M.S.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (D.L.R.); and Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford, Calif (M.P.L.).

Radiology
|February 19, 2020
PubMed
Summary
This summary is machine-generated.

Developing artificial intelligence (AI) for medical imaging requires large, curated datasets. This study outlines essential data preparation steps, highlights current limitations, and explores novel solutions for AI algorithm development and clinical implementation.

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Area of Science:

  • Medical imaging
  • Artificial intelligence
  • Machine learning

Background:

  • Artificial intelligence (AI) shows significant promise in medical imaging, spanning the entire workflow from image acquisition to outcome prediction.
  • Current AI development is hindered by the need for large, expertly labeled datasets, which are often limited in access and scope.
  • Existing supervised AI methods necessitate extensive data curation for optimal training, validation, and testing.

Purpose of the Study:

  • To detail fundamental steps for preparing medical imaging data for AI development.
  • To elucidate the limitations currently faced in the data curation process for AI in medical imaging.
  • To explore innovative approaches for overcoming challenges in medical imaging data availability for AI.

Main Methods:

  • Review of current practices in medical imaging data preparation for AI.
  • Analysis of limitations in data accessibility and curation processes.
  • Exploration of emerging strategies to enhance data availability for AI model training.

Main Results:

  • The preparation of medical imaging data for AI is a complex, resource-intensive process.
  • Limited data access and small sample sizes from restricted geographic areas impede AI algorithm generalization.
  • Current data curation methods result in algorithms with restricted utility and suboptimal performance.

Conclusions:

  • Addressing data availability and curation is critical for advancing AI in medical imaging.
  • New methodologies are required to create robust, generalizable AI algorithms for clinical use.
  • Overcoming data limitations will accelerate the integration of AI into the medical imaging lifecycle.