TY - JOUR
T1 - Medical deep learning—A systematic meta-review
AU - Egger, Jan
AU - Gsaxner, Christina
AU - Pepe, Antonio
AU - Pomykala, Kelsey L.
AU - Jonske, Frederic
AU - Kurz, Manuel
AU - Li, Jianning
AU - Kleesiek, Jens
PY - 2022/6
Y1 - 2022/6
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Data analysis
KW - Deep learning
KW - Detection
KW - Generative adversarial networks
KW - Image analysis
KW - Machine learning
KW - Medical image analysis
KW - Medical image processing
KW - Medical imaging
KW - Meta-review
KW - Meta-survey
KW - Pathology
KW - Patient data
KW - PubMed
KW - Registration
KW - Review
KW - Segmentation
KW - Survey
KW - Systematic
UR - http://www.scopus.com/inward/record.url?scp=85130318491&partnerID=8YFLogxK
U2 - 10.1016/j.cmpb.2022.106874
DO - 10.1016/j.cmpb.2022.106874
M3 - Review article
SN - 0169-2607
VL - 221
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
M1 - 106874
ER -