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We propose the BayesDose-Framework, a Bayesian approach for fast and accuratedose prediction in proton therapy. Our framework is based on a previouslypublished deterministic LSTM model and is trained and evaluated on simulatedbeamlet doses from water phantoms and patient geometries. We parameterize thenetwork's weights using 2D Gaussian mixture models and use ensemble predictionsto quantify mean dose predictions and their standard deviation. The BayesDosemodel performs similarly to the deterministic variant. The uncertaintypredictions are conservative but correlate well spatially and in magnitude withdose differences. This correlation is reduced when applied to patient data withunseen relative stopping power value ranges, which could be successfullyaddressed by re-training. We parallelize predictions and presample networkweights to reduce runtime overhead. Bayesian models like BayesDose can providefast predictions with quality equal to deterministic models and may supportdecision making and quality assurance in clinical settings in the future.

BayesDose: Comprehensive proton dose prediction with model uncertainty using Bayesian LSTMs