Transfer learning for non-image data in clinical research: A scoping review

Andreas Ebbehoj, Mette Østergaard Thunbo, Ole Emil Andersen, Michala Vilstrup Glindtvad, Adam Hulman


Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature.

There is no doubt that most clinicians will use technologies integrating artificial intelligence (AI) to automate routine clinical tasks in the future. In recent years, the U.S. Food and Drug Administration has been approving an increasing number of AI-based solutions, dominated by deep learning algorithms [1]. Examples include atrial fibrillation detection via smart watches, diagnosis of diabetic retinopathy based on fundus photographs, and other tasks involving pattern recognition [1]. In the past, the development of such algorithms would have taken an enormous effort, both regarding computational capacity and technical expertise. Nowadays, computational tools, including cloud computing, free software, and training materials are more easily accessible than ever before [2]. This means that more researchers, with more diverse backgrounds, have access to machine learning, and that they can focus more on the subject matter when developing AI-based solutions for clinical practice.

Scoping reviews identify available evidence, examine research practices, and characterize attributes related to a concept (i.e. transfer learning in our case) [12]. This format fits better with our research objective than a traditional systematic review and meta-analysis, as the latter require a more well-defined research question. During the process, we followed the ‘PRISMA for Scoping Reviews’ guidelines and the manual for conducting scoping reviews by the Joanna Briggs Institute [13, 14].

The search resulted in 4,902 records, of which 2,097 were duplicates (Fig 1). After screening the remaining 2,805 records, 2,528 were excluded as irrelevant. Of the 277 records included for full-text review, 194 were excluded, mainly because they focused on basic, animal, or other non-clinical research (n = 107) or did not use transfer learning (n = 43). Another 64 records were identified in reviews or on Twitter, but none of the papers were eligible for inclusion. In total, 83 studies were included in this review.

Applications of transfer learning were identified in a variety of clinical studies and demonstrated the potential in reusing models across different prediction tasks, data types, and even species. Improvements in predictive performance were especially striking when transfer learning was applied to smaller datasets, as compared to training machine learning algorithms from scratch. Image-based models seemed to have a large impact outside of computer vision and were reused for the analysis of time series, audio, and tabular data. Many studies utilized publicly available datasets and models, but surprisingly few shared their code.

Transfer learning via fine-tuning and feature-representation learning has almost been unknown in the clinical literature until 2019, when the number of articles started to increase rapidly. This development is lagging a few years behind the trend seen in medical image analysis [11], which might be explained partly by the impact that computer vision models have on non-image applications too.

Citation: Ebbehoj A, Thunbo MØ, Andersen OE, Glindtvad MV, Hulman A (2022) Transfer learning for non-image data in clinical research: A scoping review. PLOS Digit Health 1(2): e0000014.

Editor: Matthew Chua Chin Heng, National University of Singapore, SINGAPORE

Received: November 5, 2021; Accepted: December 15, 2021; Published: February 17, 2022

Copyright: © 2022 Ebbehoj et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data extracted from the identified articles and then presented in the Results section is available as an electronic supplement along with the code used for the analysis.

Funding: OEA, MGV and AH are employed at Steno Diabetes Center Aarhus that is partly funded by a donation from the Novo Nordisk Foundation. AE and MØT are supported by PhD scholarships from Aarhus University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

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