Purpose: We developed an app for Internet of Things (IoT) device such as smartphone or tablet to calculate the monitor unit in superficial and orthovoltage skin therapy. The app can run both on the Windows and Android operation system. Methods: The IoT app was created based on the formula to calculate the monitor unit in skin therapy using kV photon beams. The calculation was based on databases of dose variables such as relative exposure factor and backscatter factor. The calculation also considered the stand-off and stand-in correction according to the inverse-square and inverse-cube law. Verification of the app was carried out by comparing the monitor unit results with those from hand calculations. Results: The frontend window of the app provided a user-friendly interface to the user for inputting prescription dose, beam and treatment setup variables. The user could save the calculation record electronically, generate a printout or send it to other radiation staff using the IoT. Verification of the app showing that deviation between the monitor units calculated by the app and by hand is insignificant. Conclusion: The verified IoT app can effectively calculate the monitor unit in superficial and orthovoltage skin therapy. The app takes advantages of all innate features of IoT such as real time communication, Internet access, data transfer and sharing.
Purpose: We created two applications, one for use on Android and one for use on Windows, to carry out monitor unit (MU) calculations. These applications carried out these long calculations quickly, while avoiding the potential for human error. Methods: A general formula for calculating MU for an orthovoltage x‐ray machine was used and implemented in two programs created for Android and Windows. The formula relies on the prescribed dose and fractionation. Other values the formula relies upon are relative exposure factor (REF) and backscatter factor (BSF). These factors are specific to each unit and head in use, and are measured and tabulated prior to use. In the case of a BSF not falling on already known values, the programs were made to automatically use linear interpolation to find an appropriate BSF. The programs also allow for a stand‐off correction, calculated using the inverse‐square law, and an attenuator value that can be arbitrarily input by the user. Results: The two programs were built using C#, and use essentially the same types of backends to do their calculations. Differences in the programs are mainly in the graphical user interfaces. The backend was made to mimic the way a human does the same calculations. Previously measured REF and BSF tables for three different x‐ray energies and field size types were loaded into the program beforehand for testing. Both applications were easy to use, and produced the exact same results in all situations. These results matched the same calculations done by hand. Differences were only found when hand calculations use shortcuts to find BSF values that lie outside of the pre‐measured values. Conclusion: Our programs can efficiently and effectively calculate MU as well as or better than by hand. The Android version can be useful in the future for emergency situations.
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