Artificial intelligence prediction of nonenhancing brain tumor malignancy based on in vivo confocal laser endomicroscopic imaging
AI models that integrate both visual and temporal information from CLE digital imaging sequences can effectively classify brain tumor grade with accuracy comparable to that of expert neuropathologists, outperforming frame-based models. Such a system reduces interpretive subjectivity and holds promise as an intraoperative decision CLE support tool for nonenhancing brain tumor resection.
Authors
- baoxin Li
- Irakliy Abramov (ORCID: https://orcid.org/0000-0003-4644-8727)
- Jiuxu Chen
- Thomas J. On (ORCID: https://orcid.org/0009-0005-7449-4204)
- Carlos E. Calderón-Valero
- Mark C. Preul
- Jennifer M. Eschbacher
- Yuan Xu
Institutions
- Barrow Neurological Institute (US)
- St. Joseph's Hospital and Medical Center (US)
- Arizona State University (US)
Publication Details
- Journal
- Frontiers in Surgery
- Published
- 2026-01-05
- DOI
- https://doi.org/10.3389/fsurg.2025.1655374
- Citations
- 1
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 39.15
Funders
- Barrow Neurological Foundation