Artificial intelligence, radiomics, and computational modeling in skull base surgery

Eric Suero Molina; Antonio Di Ieva

doi:10.1007/978-3-031-64892-2_16

Artificial intelligence, radiomics, and computational modeling in skull base surgery

Eric Suero Molina^*
, Antonio Di Ieva

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

3 Citations (Scopus)

Abstract

This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements are providing opportunities to improve diagnostic accuracy, surgical planning, and postoperative care. Currently, computational models can assist in diagnosis, simulate surgical scenarios, and improve safety during surgical procedures by identifying critical structures. AI-powered technologies, such as liquid biopsy, machine learning, radiomic analysis, computer vision, and label-free optical imaging, aim to revolutionize skull base surgery. AI-driven advancements promise safer, more precise, and effective surgeries, improving patient outcomes and preoperative assessment.

Original language	English
Title of host publication	Computational neurosurgery
Editors	Antonio Di Ieva, Eric Suero Molina, Sidong Liu, Carlo Russo
Place of Publication	Switzerland
Publisher	Springer
Chapter	16
Pages	265-283
Number of pages	19
ISBN (Electronic)	9783031648922
ISBN (Print)	9783031648915
DOIs	https://doi.org/10.1007/978-3-031-64892-2_16
Publication status	Published - 2024

Publication series

Name	Advances in Experimental Medicine and Biology
Publisher	Springer
Volume	1462
ISSN (Print)	0065-2598
ISSN (Electronic)	2214-8019

Keywords

Artificial intelligence
Computational modeling
Machine learning
Radiomics
Skull base surgery

Access to Document

10.1007/978-3-031-64892-2_16

Cite this

@inbook{60744a0bc9ee4371b470c49118553788,

title = "Artificial intelligence, radiomics, and computational modeling in skull base surgery",

abstract = "This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements are providing opportunities to improve diagnostic accuracy, surgical planning, and postoperative care. Currently, computational models can assist in diagnosis, simulate surgical scenarios, and improve safety during surgical procedures by identifying critical structures. AI-powered technologies, such as liquid biopsy, machine learning, radiomic analysis, computer vision, and label-free optical imaging, aim to revolutionize skull base surgery. AI-driven advancements promise safer, more precise, and effective surgeries, improving patient outcomes and preoperative assessment.",

keywords = "Artificial intelligence, Computational modeling, Machine learning, Radiomics, Skull base surgery",

author = "\{Suero Molina\}, Eric and \{Di Ieva\}, Antonio",

year = "2024",

doi = "10.1007/978-3-031-64892-2\_16",

language = "English",

isbn = "9783031648915",

series = "Advances in Experimental Medicine and Biology",

publisher = "Springer",

pages = "265--283",

editor = "\{Di Ieva\}, Antonio and \{Suero Molina\}, Eric and Sidong Liu and Carlo Russo",

booktitle = "Computational neurosurgery",

}

Artificial intelligence, radiomics, and computational modeling in skull base surgery. / Suero Molina, Eric ; Di Ieva, Antonio.
Computational neurosurgery. ed. / Antonio Di Ieva; Eric Suero Molina; Sidong Liu; Carlo Russo. Switzerland: Springer, 2024. p. 265-283 (Advances in Experimental Medicine and Biology; Vol. 1462).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Artificial intelligence, radiomics, and computational modeling in skull base surgery

AU - Suero Molina, Eric

AU - Di Ieva, Antonio

PY - 2024

Y1 - 2024

N2 - This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements are providing opportunities to improve diagnostic accuracy, surgical planning, and postoperative care. Currently, computational models can assist in diagnosis, simulate surgical scenarios, and improve safety during surgical procedures by identifying critical structures. AI-powered technologies, such as liquid biopsy, machine learning, radiomic analysis, computer vision, and label-free optical imaging, aim to revolutionize skull base surgery. AI-driven advancements promise safer, more precise, and effective surgeries, improving patient outcomes and preoperative assessment.

AB - This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements are providing opportunities to improve diagnostic accuracy, surgical planning, and postoperative care. Currently, computational models can assist in diagnosis, simulate surgical scenarios, and improve safety during surgical procedures by identifying critical structures. AI-powered technologies, such as liquid biopsy, machine learning, radiomic analysis, computer vision, and label-free optical imaging, aim to revolutionize skull base surgery. AI-driven advancements promise safer, more precise, and effective surgeries, improving patient outcomes and preoperative assessment.

KW - Artificial intelligence

KW - Computational modeling

KW - Machine learning

KW - Radiomics

KW - Skull base surgery

UR - https://www.scopus.com/pages/publications/85208920248

U2 - 10.1007/978-3-031-64892-2_16

DO - 10.1007/978-3-031-64892-2_16

M3 - Chapter

C2 - 39523271

AN - SCOPUS:85208920248

SN - 9783031648915

T3 - Advances in Experimental Medicine and Biology

SP - 265

EP - 283

BT - Computational neurosurgery

A2 - Di Ieva, Antonio

A2 - Suero Molina, Eric

A2 - Liu, Sidong

A2 - Russo, Carlo

PB - Springer

CY - Switzerland

ER -

Artificial intelligence, radiomics, and computational modeling in skull base surgery

Abstract

Publication series

Keywords

Access to Document

Other files and links

Fingerprint

Cite this