Medical deep learning—A systematic meta-review. (June 2022)

• Record Type: Journal Article
• Authors: Egger, Jan; Gsaxner, Christina; Pepe, Antonio; Pomykala, Kelsey L.; Jonske, Frederic; Kurz, Manuel; Li, Jianning; Kleesiek, Jens
• Is Part Of: Computer methods and programs in biomedicine. Volume 221(2022)
• Publication Date: 2022-06
• Subjects: Deep learning -- Artificial neural networks -- Machine learning -- Data analysis -- Image analysis -- Medical image analysis -- Medical image processing -- Medical imaging -- Patient data -- Pathology -- Detection -- Segmentation -- Registration -- Generative adversarial networks -- PubMed -- Systematic -- Review -- Survey -- Meta-review -- Meta-survey; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106874
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: providing an overview of current deep learning reviews where a medical application plays the key role and arranging the researched works chronologically for a historical common thread and picture over the years. extracting the overall number of referenced works and citations to give an impression of the research influence and footprints of the respective field. analyzing, exploring and highlighting the main reasons for the massive research efforts on this topic. conducting a comprehensive discussion of the current state-of-the-art in the deep learning area with achievements but also failures from other domains that should be avoided and not be repeated in the medical area. providing a critical expert opinion and pointing out further controversies. Abstract: Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts. There are even instances where deep learning outperformed humans, for example with object recognition and gaming. Deep learning is also showing vast potential in the medical domain. With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information. Patient data is not only collected in clinical centers, like hospitals and private practices, but also by mobile healthcare apps or online websites. The abundance of collected patient data and the recent growth in the deep learning field has resulted in a large increase in research efforts. In Q2/2020, the search engine PubMed returned already over 11, 000 results for the search term 'deep learning', and around 90% of these publications are from the last three years. However, even though PubMed represents the largest search engine in the medical field, it does not cover all medical-related publications. Hence, a complete overview of the field of 'medical deep learning' is almost impossible to obtain and acquiring a full overview of medical sub-fields is becoming increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been published within the last few years. They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as a foundation, the aim of this article is to provide the first high-level, systematic meta-review of medical deep learning surveys.