  Purpose: A fundamental problem in designing safe machine learning systems is
identifying when samples presented to a deployed model differ from those
observed at training time. Detecting so-called out-of-distribution (OoD)
samples is crucial in safety-critical applications such as robotically guided
retinal microsurgery, where distances between the instrument and the retina are
derived from sequences of 1D images that are acquired by an
instrument-integrated optical coherence tomography (iiOCT) probe.
  Methods: This work investigates the feasibility of using an OoD detector to
identify when images from the iiOCT probe are inappropriate for subsequent
machine learning-based distance estimation. We show how a simple OoD detector
based on the Mahalanobis distance can successfully reject corrupted samples
coming from real-world ex vivo porcine eyes.
  Results: Our results demonstrate that the proposed approach can successfully
detect OoD samples and help maintain the performance of the downstream task
within reasonable levels. MahaAD outperformed a supervised approach trained on
the same kind of corruptions and achieved the best performance in detecting OoD
cases from a collection of iiOCT samples with real-world corruptions.
  Conclusion: The results indicate that detecting corrupted iiOCT data through
OoD detection is feasible and does not need prior knowledge of possible
corruptions. Consequently, MahaAD could aid in ensuring patient safety during
robotically guided microsurgery by preventing deployed prediction models from
estimating distances that put the patient at risk.
