Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer

Cancers

Andreas Peintner¹  Leo Benning²  Lukas Peintner³ 

[1] Databases and Information Systems, Department of Computer Science, Leopold-Franzens University of Innsbruck, 6020 Innsbruck, Austria;Health Care Supply Research and Data Mining Working Group, Emergency Department, University Medical Center Freiburg, 79106 Freiburg, Germany;Institute of Molecular Medicine and Cell Research, Albert Ludwigs University of Freiburg, 79085 Freiburg, Germany;

关键词: cancer diagnostics;    machine learning;    artificial intelligence;    high throughput;    deep learning;    CNN;   

DOI  :  10.3390/cancers14030623

来源: DOAJ

【 摘 要 】

Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condition. These diagnostic approaches have, so far, mostly relied on conventional diagnostic methods and have made little use of the vast amounts of clinical and diagnostic data that are routinely being collected along the diagnostic pathway. Practitioners have lacked the tools to handle this ever-increasing flood of data. Only recently, the clinical field has opened up more for the opportunities that come with the systematic utilisation of high-dimensional computational data analysis. We aim to introduce the reader to the theoretical background of machine learning (ML) and elaborate on the established and potential use cases of ML algorithms in screening and early detection. Furthermore, we assess and comment on the relevant challenges and misconceptions of the applicability of ML-based diagnostic approaches. Lastly, we emphasise the need for a clear regulatory framework to responsibly introduce ML-based diagnostics in clinical practice and routine care.

【 授权许可】

Unknown