Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F25%3A00603248" target="_blank" >RIV/68081731:_____/25:00603248 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00159816:_____/25:00082492 RIV/00216224:14110/25:00140603

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1388245724003304" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1388245724003304</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.clinph.2024.11.007" target="_blank" >10.1016/j.clinph.2024.11.007</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology

Popis výsledku v původním jazyce

Introduction: Precise localization of the epileptogenic zone is critical for successful epilepsy surgery. However, imbalanced datasets in terms of epileptic vs. normal electrode contacts and a lack of standardized evaluation guidelines hinder the consistent evaluation of automatic machine learning localization models. Methods: This study addresses these challenges by analyzing class imbalance in clinical datasets and evaluating common assessment metrics. Data from 139 drug-resistant epilepsy patients across two Institutions were analyzed. Metric behaviors were examined using clinical and simulated data. Results: Complementary use of Area Under the Receiver Operating Characteristic (AUROC) and Area Under the Precision-Recall Curve (AUPRC) provides an optimal evaluation approach. This must be paired with an analysis of class imbalance and its impact due to significant variations found in clinical datasets. Conclusions: The proposed framework offers a comprehensive and reliable method for evaluating machine learning models in epileptogenic zone localization, improving their precision and clinical relevance. Significance: Adopting this framework will improve the comparability and multicenter testing of machine learning models in epileptogenic zone localization, enhancing their reliability and ultimately leading to better surgical outcomes for epilepsy patients.

Název v anglickém jazyce

Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology

Popis výsledku anglicky

Introduction: Precise localization of the epileptogenic zone is critical for successful epilepsy surgery. However, imbalanced datasets in terms of epileptic vs. normal electrode contacts and a lack of standardized evaluation guidelines hinder the consistent evaluation of automatic machine learning localization models. Methods: This study addresses these challenges by analyzing class imbalance in clinical datasets and evaluating common assessment metrics. Data from 139 drug-resistant epilepsy patients across two Institutions were analyzed. Metric behaviors were examined using clinical and simulated data. Results: Complementary use of Area Under the Receiver Operating Characteristic (AUROC) and Area Under the Precision-Recall Curve (AUPRC) provides an optimal evaluation approach. This must be paired with an analysis of class imbalance and its impact due to significant variations found in clinical datasets. Conclusions: The proposed framework offers a comprehensive and reliable method for evaluating machine learning models in epileptogenic zone localization, improving their precision and clinical relevance. Significance: Adopting this framework will improve the comparability and multicenter testing of machine learning models in epileptogenic zone localization, enhancing their reliability and ultimately leading to better surgical outcomes for epilepsy patients.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30210 - Clinical neurology

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Clinical Neurophysiology

ISSN

1388-2457

e-ISSN

1872-8952

Svazek periodika

169

Číslo periodika v rámci svazku

January

Stát vydavatele periodika

IE - Irsko

Počet stran výsledku

14

Strana od-do

33-46

Kód UT WoS článku

001370248100001

EID výsledku v databázi Scopus

2-s2.0-85210128489