研究目的
The aim of this paper is to present the design of the IoT laser and to investigate if the multilayer perceptron (MLP) can be used as a tool to recognize the biological effect that would be caused by the chosen therapy parameter values on the bases of the data collected from all the practitioners who contribute to the system knowledge by submitting the applied therapy protocols along with the treatment outcome.
研究成果
The multilayer perceptron, trained with properly selected datasets, could be successfully used to classify the therapy parameter values into protocols. When all 4 therapy parameters were specified, the MLP managed to classify all testing protocols correctly with 100% accuracy.
研究不足
The therapy recommendations were not persistent among the authors and sometimes even contradicting. The MLP accuracy ranged from 67-100% when one of the therapy parameters was not specified.
1：Experimental Design and Method Selection:
The system is composed of many laser probes connected over the USB ports to the computers, which are further connected, over the Internet, to the application server. The MLP is used to predict the biological effect of the chosen therapy parameter values.
2：Sample Selection and Data Sources:
Data are collected from practitioners who submit applied therapy protocols along with the treatment outcome.
3：List of Experimental Equipment and Materials:
Laser probes with GaAlAs laser diodes emitting infrared 820nm light, controlled by a microcontroller.
4：Experimental Procedures and Operational Workflow:
Practitioners use a desktop application to select diagnosis and define laser parameter values. Data are transferred to an application server and database.
5：Data Analysis Methods:
The MLP is trained using supervised learning with data from successful outcomes and tested with randomly generated therapy protocols.
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