  Novel approaches for predicting the outcomes of blood-brain barrier (BBB)
opening with focused ultrasound (FUS) and microbubbles are highly desired. This
study aims to explore machine learning-based methods for reliably predicting
the FUS-induced BBB opening efficacy and safety. Methods: Sixteen female rats
were used in this study. An acoustic feedback-controlled FUS system (f0:
0.5MHz) was used for the BBB opening with the infusion of custom-made
nanobubbles/Definity. Evans Blue was injected for the BBB opening efficacy
verification and the brain tissue was harvested for the safety assessment.
Acoustic emissions were recorded, preprocessed and fed into three machine
learning models for BBB opening outcomes prediction. Conventional stable and
inertial cavitation dose were also calculated. Results: Among the tested
machine learning models, a modified Support Vector Data Description (mSVDD)
model achieved the best performance in the BBB opening efficacy and safety
prediction with an accuracy of 85.0+/-16.6% and 62.5+/-12.8%, respectively.
Conventional stable and inertial cavitation dose-based prediction has a
prediction accuracy of 80.0% in efficacy and 34.3% in safety, respectively. The
mSVDD model trained with the overall bubble response (0-2 MHz) performed better
than that trained with the ultra-harmonic bubble response (0.7-0.8 MHz) in both
efficacy prediction (85.0+/-16.6% vs 76.0+/-8.0%, p=0.04) and safety prediction
(62.5+/-12.8% vs 55.0+/-10.7%, p>0.05). It is also found that the mSVDD model
trained with nanobubble data cannot be directly applied to Definity.
Conclusion: Our investigations demonstrated that it is feasible to achieve a
reliable prediction of FUS-induced BBB opening outcomes with machine learning
and acoustic signals from stimulated nanobubbles. This study provided a new
approach for the prediction of FUS-BBB opening outcomes with a clinical
translation potential.
