  Significance: Photoacoustic imaging (PAI) promises to measure
spatially-resolved blood oxygen saturation, but suffers from a lack of accurate
and robust spectral unmixing methods to deliver on this promise. Accurate blood
oxygenation estimation could have important clinical applications, from cancer
detection to quantifying inflammation.
  Aim: This study addresses the inflexibility of existing data-driven methods
for estimating blood oxygenation in PAI by introducing a recurrent neural
network architecture.
  Approach: We created 25 simulated training dataset variations to assess
neural network performance. We used a long short-term memory network to
implement a wavelength-flexible network architecture and proposed the
Jensen-Shannon divergence to predict the most suitable training dataset.
  Results: The network architecture can handle arbitrary input wavelengths and
outperforms linear unmixing and the previously proposed learned spectral
decolouring method. Small changes in the training data significantly affect the
accuracy of our method, but we find that the Jensen-Shannon divergence
correlates with the estimation error and is thus suitable for predicting the
most appropriate training datasets for any given application.
  Conclusions: A flexible data-driven network architecture combined with the
Jensen-Shannon Divergence to predict the best training data set provides a
promising direction that might enable robust data-driven photoacoustic oximetry
for clinical use cases.
