aceMachine learning-assisted intraoperative hyperspectral imaging in cranial meningioma surgery: interim results of an ongoing study
Objective Hyperspectral imaging (HSI) is a label-free optical technique that might extend conventional intraoperative visualization by providing quantitative spectral information from the operative field. The aim of this study was to evaluate whether intraoperative in vivo HSI combined with machine learning (ML)-based analysis can distinguish cranial meningioma from adjacent intracranial tissues, particularly cortex and dura, during meningioma surgery. Methods This interim analysis of an ongoing prospective obse…
In an interim study of 70 meningioma surgery patients, a machine learning classifier applied to hyperspectral imaging distinguished tumour from cortex with 0.928 accuracy, and from cortex and dura with 0.773 accuracy.
This is an interim analysis from a single center, and the authors say external validation, patient-level analyses, WHO grade prediction and spatially matched histopathological confirmation are needed before clinical implementation.
Sources
- Peer-reviewedNeurosurgical Focus2026-11-01
What do you make of this one?
Truvace Impact Record TRV-2026-2432, v1: “Machine learning-assisted intraoperative hyperspectral imaging in cranial meningioma surgery: interim results of an ongoing study.” Truvace, 2026-11-08. /record/TRV-2026-2432 (accessed on the day you copy it). sha256 4513d62a7d9db7e7…
Change history
Every change made to this record since it was first published, and why. Nothing has changed since it went up.
Certified into the record
How to check it yourself, without trusting this page
Download any version from /api/record/TRV-2026-2432 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.