Automated Prediction of Bone Volume Removed in Mastoidectomy
This study establishes a foundational step in using deep learning to predict bone volume removal during mastoidectomy. The results indicate that learning-based methods can reasonably approximate the surgical endpoint of mastoidectomy. Further refinement with larger, more diverse data sets and improved model architectures will be essential for enhancing prediction accuracy.
Authors
- Russell H. Taylor (ORCID: https://orcid.org/0000-0001-6272-1100)
- Nimesh Nagururu (ORCID: https://orcid.org/0000-0001-5526-1425)
- Adnan Munawar (ORCID: https://orcid.org/0000-0002-3400-4558)
- Andy S. Ding (ORCID: https://orcid.org/0000-0002-8490-057X)
- Hisashi Ishida (ORCID: https://orcid.org/0000-0002-3391-8403)
- Francis X. Creighton (ORCID: https://orcid.org/0000-0003-3131-1162)
- Masaru Ishii (ORCID: https://orcid.org/0000-0002-4215-007X)
- Mathias Unberath (ORCID: https://orcid.org/0000-0002-0055-9950)
- Manish Sahu (ORCID: https://orcid.org/0000-0002-7749-979X)
Institutions
- Johns Hopkins University (US)
- Johns Hopkins Medicine (US)
Publication Details
- Journal
- Otolaryngology
- Published
- 2025-08-11
- DOI
- https://doi.org/10.1002/ohn.1365
- Primary Topic
- Cerebrovascular and Carotid Artery Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00