Clinical Prediction Modeling in Intramedullary Spinal Tumor Surgery

Elie Massaad, Yoon Ha, Ganesh M. Shankar, John H. Shin

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

Artificial intelligence is poised to influence various aspects of patient care, and neurosurgery is one of the most uprising fields where machine learning is being applied to provide surgeons with greater insight about the pathophysiology and prognosis of neurological conditions. This chapter provides a guide for clinicians on relevant aspects of machine learning and reviews selected application of these methods in intramedullary spinal cord tumors. The potential areas of application of machine learning extend far beyond the analyses of clinical data to include several areas of artificial intelligence, such as genomics and computer vision. Integration of various sources of data and application of advanced analytical approaches could improve risk assessment for intramedullary tumors.

Original languageEnglish
Title of host publicationActa Neurochirurgica, Supplementum
PublisherSpringer Science and Business Media Deutschland GmbH
Pages333-339
Number of pages7
DOIs
Publication statusPublished - 2022

Publication series

NameActa Neurochirurgica, Supplementum
Volume134
ISSN (Print)0065-1419
ISSN (Electronic)2197-8395

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Surgery
  • Clinical Neurology

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