Automaton based detection of affected cells in three dimensional biological system

TL;DR

This paper proposes an automaton-based framework utilizing AI and nanotechnology to detect and potentially treat affected cells in 3D biological systems, validated through simulations showing advantages over existing models.

Contribution

The paper introduces a novel automaton model for detecting affected cells in 3D systems, integrating AI and nanotech, with successful simulation validation.

Findings

Automaton model successfully detects affected cells in complex environments.

The proposed framework outperforms obstacle avoidance models in simulations.

Simulation results support potential for non-invasive cellular detection and treatment.

Abstract

The aim of this research review is to propose the logic and search mechanism for the development of an artificially intelligent automaton (AIA) that can find affected cells in a 3-dimensional biological system. Research on the possible application of such automatons to detect and control cancer cells in the human body are greatly focused MRI and PET scans finds the affected regions at the tissue level even as we can find the affected regions at the cellular level using the framework. The AIA may be designed to ensure optimum utilization as they record and might control the presence of affected cells in a human body. The proposed models and techniques can be generalized and used in any application where cells are injured or affected by some disease or accident. The best method to import AIA into the body without surgery or injection is to insert small pill like automata, carrying material viz drugs or leukocytes that is needed to correct the infection. In this process, the AIA can be compared to nano pills to deliver or support therapy. NanoHive simulation software was used to validate the framework of this paper. The existing nanomedicine models such as obstacle avoidance algorithm based models (Hla K H S et al 2008) and the framework in this model were tested in different simulation based experiments. The existing models such as obstacle avoidance based models failed in complex environmental conditions (such as changing environmental conditions, presence of semi-solid particles, etc) while the model in this paper executed its framework successfully.Come systems biology, this field of automatons deserves a bigger leap of understanding especially when pharmacogenomics is at its peak. The results also indicate the importance of artificial intelligence and other computational capabilities in the proposed model for the successful detection of affected cells.