Unlocking glioma genetics with deep learning

Daniel A Orringer; Todd C Hollon

doi:10.1016/j.medj.2023.07.008

Unlocking glioma genetics with deep learning

Med. 2023 Aug 11;4(8):493-494. doi: 10.1016/j.medj.2023.07.008.

Authors

Daniel A Orringer¹, Todd C Hollon²

Affiliations

¹ Departments of Neurosurgery and Pathology, Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA. Electronic address: daniel.orringer@nyulangone.org.
² Departments of Neurosurgery, Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

PMID: 37572648
DOI: 10.1016/j.medj.2023.07.008

Abstract

The AI era in medicine has ushered in new opportunities to improve the diagnosis and treatment of human disease. CHARM, an AI algorithm described in this issue,¹ has the potential to streamline molecular classification, intraoperative diagnosis, surgical decision making, and trial enrollment for glioma patients.

Publication types

Research Support, N.I.H., Extramural
Comment

MeSH terms

Algorithms
Clinical Decision-Making
Deep Learning*
Diagnosis, Computer-Assisted
Glioma* / diagnosis
Glioma* / genetics
Glioma* / therapy
Humans

Abstract

Publication types

MeSH terms

Grants and funding