Abstract
Anatomically, oral cavity and central nervous system have a close relationship; the mouth and face are the location for 30–40% of the body’s sensory and motor nerves. The identification of orofacial manifestations of neurological disorders is usually in direct relation with the responsibilities of a dental surgeon. Therefore, familiarizing dental surgeons with theses manifestations is essential to have better recognition, diagnosis, and correct decisions upon treating their associated Neurological Disorders. These manifestations should be efficiently analyzed using novel effective techniques since their related neurological disorders need to be early identified to avoid serious consequences. Furthermore, preventive dental care for patients with neurological disorders and all kind of rehabilitative treatments necessitates well-planned and effective novel approaches. The Internet of Medical Thing (IoMT) is a relatively new technology that allows the transfer of medical data over a secure network of medical sensors and wearable devices. The data transferred are of utmost importance in diseases diagnosis and treatment. In this paper, an IoMT-based Intelligent Guided Particle Local Search with Optimized Neural Networks (IGPLONN) approach is proposed. Firstly, dental data are collected from the International Collaboration on Cancer Reporting (ICCR) oral cavity and central nervous system. Secondly, features are extracted from data and IGPLONN algorithm is utilized to select the effective features by minimizing the feature dimension that helps improve the overall prediction rate. Finally, the obtained features are transferred to the central health application through the IoMT platform where they can be analyzed by dental practitioners for neurological disorders prediction. The hybrid optimized technique improves the overall oral-linked neurological diseases detection rate. Moreover, it efficiently manages the forecast parameters that are used to predict the dental metastasis with minimum computational complexity. The performance of the proposed system has been experimentally evaluated on MATLAB to verify its excellence. The results revealed that proposed IoMT-based IGPLONN method attains the maximum accuracy of 98.3% compared to other methods.
Highlights
Internet of Things (IoT) refers to the connections of different devices around the world to transfer, process, and store theThe associate editor coordinating the review of this manuscript and approving it for publication was Vishal Srivastava.data [1], [2]
The results revealed that proposed Internet of Medical Things (IoMT)-based IOGPLSNN method attains the maximum accuracy of 98.3% compared to other methods such as Genetic optimized back propagation neural network (GABPNN) [42], Particle swarm optimized radial basis function network (PSO-RBNN) [43] and Bee colony optimized convolution neural network (BCCNN) [44]
The proposed IoMT-based intelligent guided particle local search algorithm with optimized neural network (IGPLONN) was executed on dental images, which can effectively and quickly recognize the tooth diseases, this offers the diagnostic basis for dentists and saves treatment time
Summary
Internet of Things (IoT) refers to the connections of different devices around the world to transfer, process, and store the. One of the dangerous neurological diseases called neurofibromatosis [14], which is created by the mutation of the NF1 gene which has been analyzed using IoMT sensor as shown in the figure 2 with blue and red patches These patches which indicate the sensor data movement to the brain nerve have been analyzed with 50 ms interval based on the response curve. It can be clearly shown the strong relationship between dental disease and many neurological disorders, dentists should be able to identify orofacial manifestations of neurological disorders efficiently To achieve this goal, we propose an IoMT-based Intelligent Guided Particle Local Search with Optimized Neural Networks (IGPLONN) approach.
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