Transfer learning as an AI-based solution to address limited datasets in space medicine.

Journal: Life sciences in space research
Published Date: Jan 1, 2023

Abstract

The advent of artificial intelligence (AI) has a promising role in the future long-duration spaceflight missions. Traditional AI algorithms rely on training and testing data from the same domain. However, astronaut medical data is naturally limited to a small sample size and often difficult to collect, leading to extremely limited datasets. This significantly limits the ability of traditional machine learning methodologies. Transfer learning is a potential solution to overcome this dataset size limitation and can help improve training time and performance of a neural networks. We discuss the unique challenges of space medicine in producing datasets and transfer learning as an emerging technique to address these issues.

Authors

  • Ethan Waisberg
    University College Dublin School of Medicine, Belfield, Dublin, Ireland. Electronic address: ethan.waisberg@ucdconnect.ie.
  • Joshua Ong
  • Sharif Amit Kamran
    Department of Computer Science and Engineering, University of Nevada School of Medicine, Reno, NV 89557, USA.
  • Phani Paladugu
    Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA.
  • Nasif Zaman
    Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada, United States.
  • Andrew G Lee
    Center for Space Medicine, Baylor College of Medicine, Houston, Texas, United States; Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, Texas, United States; The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, Texas, United States; Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, New York, United States; Department of Ophthalmology, University of Texas Medical Branch, Galveston, Texas, United States; University of Texas MD Anderson Cancer Center, Houston, Texas, United States; Texas A&M College of Medicine, Texas, United States; Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States.
  • Alireza Tavakkoli
    Department of Computer Science and Engineering, University of Nevada School of Medicine, Reno, NV 89557, USA.

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