  Hyperspectral imaging (HSI) captures a greater level of spectral detail than
traditional optical imaging, making it a potentially valuable intraoperative
tool when precise tissue differentiation is essential. Hardware limitations of
current optical systems used for handheld real-time video HSI result in a
limited focal depth, thereby posing usability issues for integration of the
technology into the operating room. This work integrates a focus-tunable liquid
lens into a video HSI exoscope, and proposes novel video autofocusing methods
based on deep reinforcement learning. A first-of-its-kind robotic focal-time
scan was performed to create a realistic and reproducible testing dataset. We
benchmarked our proposed autofocus algorithm against traditional policies, and
found our novel approach to perform significantly ($p<0.05$) better than
traditional techniques ($0.070\pm.098$ mean absolute focal error compared to
$0.146\pm.148$). In addition, we performed a blinded usability trial by having
two neurosurgeons compare the system with different autofocus policies, and
found our novel approach to be the most favourable, making our system a
desirable addition for intraoperative HSI.
