  Purpose: We sought to evaluate the feasibility of using machine learning
algorithms for multipoint plastic scintillator detector calibration in
high-dose-rate brachytherapy. Methods: The dosimetry system consisted of an
optimized 1-mm-core mPSD and a compact assembly of photomultiplier tubes
coupled with dichroic mirrors and filters. An $^{192}$Ir source was remotely
controlled and sent to various positions in a homemade PMMA holder. Dose
measurements covering a range of 0.5 to 12 cm of source displacement were
carried out according to TG-43 recommendations. Individual scintillator doses
were decoupled using a linear regression model, a random forest estimator, and
artificial neural network algorithms. The performance of the different
algorithms was evaluated using different sample sizes and distances to the
source for the mPSD system calibration. Results: The decoupling methods'
deviations from the expected TG-43 dose generally remained below 20%. However,
the dose prediction with the three algorithms was accurate to within 7%
relative to the dose predicted by the TG-43 formalism for measurements
performed in the same range of distances used for calibration. The performance
random forest was compromised when the predictions were done beyond the range
of distances used for calibration. The dose prediction by the linear regression
was less influenced by the calibration conditions than random forest, but with
more significant deviations. The number of available measurements for training
purposes influenced the random forest and neural network models the most. Their
accuracy tended to converge toward deviation values close to 1% from a number
of dwell positions greater than 100. Conclusions: In performing HDR
brachytherapy dose measurements with an optimized mPSD system, ML algorithms
are good alternatives for precise dose reporting and treatment assessment.
