AI applications to medical images: From machine learning to deep learning. (March 2021)

• Record Type: Journal Article
• Authors: Castiglioni, Isabella; Rundo, Leonardo; Codari, Marina; Di Leo, Giovanni; Salvatore, Christian; Interlenghi, Matteo; Gallivanone, Francesca; Cozzi, Andrea; D'Amico, Natascha Claudia; Sardanelli, Francesco
• Is Part Of: Physica medica. Volume 83(2021)
• Page Start: 9
• Page End: 24
• Publication Date: 2021-03
• Subjects: Artificial intelligence -- Deep learning -- Machine learning -- Medical imaging -- Radiomics; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.02.006
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Strategies how to develop AI applications as clinical decision support systems are provided. We focus on differences between radiomic machine learning and deep learning application domains. Pros and cons, recommendations and references to software tools are provided. Abstract: Purpose: Artificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context. Methods: A narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections. Results: We first illustrate the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches. For ML/radiomics, the phases of feature selection and of training, validation, and testing are described. DL models are presented as multi-layered artificial/convolutional neural networks, allowing us to directly process images. The data curation section includes technical steps such as image labelling, image annotation (with segmentation as a crucial step in radiomics), data harmonization (enabling compensation for differences in imaging protocols that typically generate noise in non-AI imaging studies) and federated learning. Thereafter, we dedicate specific sections to: sample size calculation, considering multiple testing in AI approaches; procedures for data augmentation to work with limited and unbalanced datasets; and the interpretability of AI models (the so-called black box issue). Pros and cons for choosing ML versus DL to implement AI applications to medical imaging are finally presented in a synoptic way. Conclusions: Biomedicine and healthcare systems are one of the most important fields for AI applications and medical imaging is probably the most suitable and promising domain. Clarification of specific challenging points facilitates the development of such systems and their translation to clinical practice.