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Disease Management Strategy for Direct and Immediate Implementation of Artificial Intelligence-Based MRI in Radiology

Background: Artificial intelligence (AI) implementation in medicine will increase the efficiency of medical services. Objective: To develop a disease management strategy for the direct and immediate implementation of AI MRI in radiology. Methods: Correlations between selected quantitative MRI parameters available in the literature and the corresponding physio-anatomy were made to build the human MRI physio-anatomical state chart (hMRI_PASC). Pathology can be assessed using the relative-to-normal (RN) values of each MRI parameter for corresponding control-normal (CN) and disease-affected (DA) regions, based on the equation: RN_Parameter(%) = multiply(100, divide(subtract(ParameterDA, ParameterCN), (ParameterCN))). The 50% RN_Parameter absolute value threshold for the selected MRI parameters was used to define a medical condition severity staging scale (MCSSS). The disease management strategy is presented for a scenario of DA human MRI organ model: the eye, using the hMRI_PASC, and MCSSS. Results: Inflammation, constriction, stiffness, and/or infiltration of blood or T1 and/or T2 lengthening or shortening agents, macromolecules, calcifications, and iron particles through broken blood vessels or broken blood vessels and blood-to-tissue barriers can be assessed based on the hMRI_PASC. Three levels: infiltration, dynamics and elastography (IDE), seven types, and eighteen stages are defined in the MCSSS. The disease management strategy introduced in this study shows that integrity of the seven affected ocular regions could be regained through therapeutical intervention, possibly followed by surgery targeted to one of the affected ocular regions. Conclusion: The hMRI_PASC, MCSSS, and disease management strategy presented in this study can be implemented immediately and directly in a software for AI-based MRI.