Role of Machine Learning and Artificial Intelligence in Interventional Oncology

Use of MALDI-TOF mass spectrometry for virus identification: a review

“…ML uses algorithms to identify patterns in data, and these patterns are then used to create a data model that can formulate predictions. 169 A similar principle of MALDI-TOF-based SARS-CoV-2 detection was reported by Nachtigall et al , 5 Rocca et al , 17 Tran et al 18 and Deulofeu et al 19 (Fig. 5).…”

A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer

“…In recent years, AI-guided risk stratification and precision treatment for cancer have been promising methods for personized medicine. 4 , 6 , 53 , 54 , 55 , 56 Considering the special topological pattern of CCIM shown by the Voronoi plot, we developed a workflow for building an scRNA-seq-assisted and CCIM-based precision chemotherapy model. We applied cutting-edge deep learning technology to train 8 AI models and finally built a CCIM-Net, which showed high prediction accuracy in the testing cohort.…”

Imaging in interventional oncology, the better you see, the better you treat

“…62 Although society tends to overestimate new technologies, it is likely that the use of AI in the field of medicine will increase in the years to come 63 Multiple literature reviews have summarized which research has been done so far regarding AI in interventional oncology (IO), showing the expanding interest in this specific field. [64][65][66][67][68][69][70] In general, research on AI in IO can be categorized in three functions: periprocedural assistance; patient selection, classification and outcome prediction; and finally patient follow-up.…”