Quantifying the Efficacy of Magnetic Nanoparticles for MRI and Hyperthermia Applications via Machine Learning Methods

A machine learning-based approach is demonstrated for prediction of the specific absorption rate for hyperthermia and r1/r2 relaxivities in magnetic resonance imagining (MRI), with descriptors including parameters of nanoparticles and experimental conditions. DiMag, an open access resource, is developed to guide the synthesis of novel nanosized magnets for MRI and hyperthermia treatment to boost the development of new biomedical agents. AbstractMagnetic nanoparticles are a prospective class of materials for use in biomedicine as agents for magnetic resonance imagining (MRI) and hyperthermia treatment. However, synthesis of nanoparticles with high efficacy is resource-intensive experimental work. In turn, the use of machine learning (ML) methods is becoming useful in materials design and serves as a great approach to designing nanomagnets for biomedicine. In this work, for the first time, an ML-based approach is developed for the prediction of main parameters of material efficacy, i.e., specific absorption rate (SAR) for hyperthermia and r1/r2 relaxivities in MRI, with parameters of nanoparticles as well as experimental conditions as descriptors. For that, a unique database with more than 980 magnetic nanoparticles collected from scientific articles is assembled. Using this data, several tree-based ensemble models are trained to predict SAR, r1 and r2 relaxivity. After hyperparameter optimization, models reach performances of R2 = 0.86, R2 = 0.78, and R2 = 0.75, respectively. ...

Source: Small - August 11, 2023 Category: Nanotechnology Authors: Pavel Kim, Nikita Serov, Aleksandra Falchevskaya, Ilia Shabalkin, Andrei Dmitrenko, Daniil Kladko, Vladimir Vinogradov Tags: Research Article Source Type: research