  Background: Accurate diagnosis of skull base tumors is essential for
providing personalized surgical treatment strategies. Intraoperative diagnosis
can be challenging due to tumor diversity and lack of intraoperative pathology
resources.
  Objective: To develop an independent and parallel intraoperative pathology
workflow that can provide rapid and accurate skull base tumor diagnoses using
label-free optical imaging and artificial intelligence.
  Method: We used a fiber laser-based, label-free, non-consumptive,
high-resolution microscopy method ($<$ 60 sec per 1 $\times$ 1 mm$^\text{2}$),
called stimulated Raman histology (SRH), to image a consecutive, multicenter
cohort of skull base tumor patients. SRH images were then used to train a
convolutional neural network (CNN) model using three representation learning
strategies: cross-entropy, self-supervised contrastive learning, and supervised
contrastive learning. Our trained CNN models were tested on a held-out,
multicenter SRH dataset.
  Results: SRH was able to image the diagnostic features of both benign and
malignant skull base tumors. Of the three representation learning strategies,
supervised contrastive learning most effectively learned the distinctive and
diagnostic SRH image features for each of the skull base tumor types. In our
multicenter testing set, cross-entropy achieved an overall diagnostic accuracy
of 91.5%, self-supervised contrastive learning 83.9%, and supervised
contrastive learning 96.6%. Our trained model was able to identify tumor-normal
margins and detect regions of microscopic tumor infiltration in whole-slide SRH
images.
  Conclusion: SRH with trained artificial intelligence models can provide rapid
and accurate intraoperative analysis of skull base tumor specimens to inform
surgical decision-making.
