Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection

• Published in: Cancers Vol. 14; no. 3; p. 606
• Main Authors: Allegra, Alessandro, Tonacci, Alessandro, Sciaccotta, Raffaele, Genovese, Sara, Musolino, Caterina, Pioggia, Giovanni, Gangemi, Sebastiano
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25-01-2022; MDPI
• Subjects: Algorithms; Artificial intelligence; Bone lesions; Bone marrow; Chemoresistance; Choice learning; Computational neuroscience; Deep learning; Diagnosis; Hematology; Learning algorithms; Machine learning; Medical prognosis; Multiple myeloma; Neural networks; Neurons; Plasma cells; Prognosis; Review; Serology; Support vector machines; Tumors; deep learning; multiple myeloma; diagnosis; machine learning; chemotherapy; prognosis; artificial intelligence; bone disease
• DOI: 10.3390/cancers14030606
• EISSN: 2072-6694

Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. Machine learning is a branch of artificial intelligence that involves algorithms that analyse information, learn from that information, and then employ their discoveries to make abreast choice, while deep learning is a field of machine learning basically represented by algorithms inspired by the organization and function of the brain, named artificial neural networks. In this review, we examine the possibility of the artificial intelligence applications in multiple myeloma evaluation, and we report the most significant experimentations with respect to the machine and deep learning procedures in the relevant field. Multiple myeloma is one of the most common haematological malignancies in the world, and among them, it is one of the most difficult ones to cure due to the high occurrence of relapse and chemoresistance. Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery and therapy selection and by a better evaluation of its relapse and survival.