NeuroLens: organ localization using natural language commands for anatomical recognition in surgical training
NeuroLens integrates multimodal inputs for accurate anatomical detection and localization, addressing limitations of traditional training. Its strong usability and technical performance make it a valuable tool for enhancing anatomical learning in surgical training. While NeuroLens shows strong usability and performance, its small sample size limits generalizability. Further evaluation with more students and enhancements like 3D visualization will strengthen its effectiveness.
Authors
- Waseem Masalha (ORCID: https://orcid.org/0000-0001-5581-8764)
- Franziska Mathis-Ullrich (ORCID: https://orcid.org/0000-0001-5239-5305)
- Daniel Delev (ORCID: https://orcid.org/0000-0001-9444-0178)
- Ramy A. Zeineldin (ORCID: https://orcid.org/0000-0002-8630-9046)
- Nevin M. Matasyoh (ORCID: https://orcid.org/0009-0004-2253-2371)
Institutions
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Universitätsklinikum Erlangen (DE)
- Menoufia University (EG)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1007/s11548-025-03463-5
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Friedrich-Alexander-Universität Erlangen-Nürnberg