Abstract

Jayashree Rajesh Prasad62279*, Rajesh S. Prasad62280 and Nanasaheb D. Thorat62281

The medical community is concerned about cancer treatment with high precision. Hyperthermia, using energy-absorbing nanoparticles, or heat mediated therapy has become increasingly important. Cancer is one of the most serious problems. The medical world is concerned about accurately treating cancer, which is responsible for over 8.8 million deaths each year. There is a rising need to develop state-of-the-art diagnostic procedures to overcome the adverse effects of unpleasant radiation therapy, chemotherapy, and surgery, such as damage to healthy tissues, weariness, baldness, and Multidrug Resistance (MDR). Magnetically produced hyperthermia is a near-term milestone in medical nanoscience and is currently being tested in phase III cancer treatment trials. Because it relies on the heat generated by magnetic Nanoparticles (NPs) when they are exposed to an external alternating magnetic field, their heating ability, as well as their synthesis is critical. The goal of this review is to investigate what makes magnetic NFs effective heating agents in magnetic hyperthermia. We propose Artificial Intelligence (AI), machine learning, and deep learning methods to evaluate the impact of various polyol synthesis parameters on the size, shape, chemical composition, number of cores, and crystallinity of the final NFs. The parameters of the model estimated by the system are represented in the study, using time series modeling of hyperthermia data to discover correlations between their structure, attributes, and function.