RESUMEN
HYPOTHESIS: Machine learning-derived algorithms are capable of automated calculation of vestibular schwannoma tumor volumes without operator input. BACKGROUND: Volumetric measurements are most sensitive for detection of vestibular schwannoma growth and important for patient counseling and management decisions. Yet, manually measuring volume is logistically challenging and time-consuming. METHODS: We developed a deep learning framework fusing transformers and convolutional neural networks to calculate vestibular schwannoma volumes without operator input. The algorithm was trained, validated, and tested on an external, publicly available data set consisting of magnetic resonance imaging images of medium and large tumors (178-9,598 mm 3 ) with uniform acquisition protocols. The algorithm was then trained, validated, and tested on an internal data set of variable size tumors (5-6,126 mm 3 ) with variable acquisition protocols. RESULTS: The externally trained algorithm yielded 87% voxel overlap (Dice score) with manually segmented tumors on the external data set. The same algorithm failed to translate to accurate tumor detection when tested on the internal data set, with Dice score of 36%. Retraining on the internal data set yielded Dice score of 82% when compared with manually segmented images, and 85% when only considering tumors of similar size as the external data set (>178 mm 3 ). Manual segmentation by two experts demonstrated high intraclass correlation coefficient (0.999). CONCLUSION: Sophisticated machine learning algorithms delineate vestibular schwannomas with an accuracy exceeding established norms of up to 20% error for repeated manual volumetric measurements-87% accuracy on a homogeneous data set, and 82% to 85% accuracy on a more varied data set mirroring real world neurotology practice. This technology has promise for clinical applicability and time savings.