Artificial neural networks and computer vision in medicine and surgery
Authors:
M. Jiřík 1,3; V. Moulisová 1; M. Hlaváč 3; M. Železný 3,4; V. Liška 1,2
Authors‘ workplace:
Biomedicínské centrum, Lékařská fakulta Univerzity Karlovy v Plzni
1; Chirurgická klinika, Fakultní nemocnice Plzeň a lékařská fakulta Univerzity Karlovy v Plzni
2; Výzkumné centrum NTIS, Fakulta aplikovaných věd, Západočeská univerzita v Plzni
3; Katedra kybernetiky, Fakulta aplikovaných věd, Západočeská univerzita v Plzni
4
Published in:
Rozhl. Chir., 2022, roč. 101, č. 12, s. 564-570.
Category:
Review
doi:
https://doi.org/10.33699/PIS.2022.101.12.564–570
Overview
Introduction: Artificial neural networks are becoming an essential technology in data analysis, and their influence is starting to permeate the field of medicine. Experimental surgery has been a long-term subject of study of our lab; this is naturally reflected in our interest in other areas of modern technologies including artificial neural networks and their advancements. In the current issue, we would like to explore this aspect of technical progress. The main goal is to critically evaluate the strengths and weaknesses of artificial neural network technology concerning its use in clinical and experimental surgery.
Methods: The article is focused on in-silico modeling, particularly on the potential of neural networks in terms of image data processing in medicine. The text briefly summarizes the historical development of deep learning neural networks and their basic principles. Furthermore, basic taxonomy tasks are presented. Finally, potential learning problems and possible solutions are also mentioned.
Results: The article points out various possible uses of artificial neural networks in biological applications. Several biomedical applications of artificial neural networks are used to describe the division and principles of the most common tasks of machine learning and deep learning such as classification, detection, and segmentation.
Conclusion: The application of artificial neural network methods in medicine and surgery offers a considerable potential; by learning directly from the data, they make it possible to avoid lengthy and subjective setting of parameters by an expert engineer. Nevertheless, the use of an unbalanced dataset can lead to unexpected, although traceable errors. The solution is to collect a dataset large enough to enable both learning and verification of proper functionality.
Keywords:
deep learning – machine learning – artificial neural network – dataset
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Labels
Surgery Orthopaedics Trauma surgeryArticle was published in
Perspectives in Surgery
2022 Issue 12
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