Comparing two deep learning algorithms for acute infarct segmentation on diffusion-weighted imaging in routine clinical practice

Changing the main architecture of the segmentation model alone maintained segmentation performance within ischemic-stroke cohorts, while achieving better classification in broader disease populations. This study highlights the need for deep-learning models to be validated not only for segmentation performance within target disease cohorts but also across diverse clinical environments to ensure practical utility.

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Publication Details

Journal
Digital Health
Published
2025-05-01
DOI
https://doi.org/10.1177/20552076251396985
Primary Topic
Acute Ischemic Stroke Management
Type
article
Field-Weighted Citation Impact
0.00
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Comparing two deep learning algorithms for acute infarct segmentation on diffusion-weighted imaging in routine clinical practice

Hokyu Kim, Hosung Kim, Mi Sun Oh, Wi‐Sun Ryu et al.
Digital Health
Acute Ischemic Stroke Management
article

Comparing two deep learning algorithms for acute infarct segmentation on diffusion-weighted imaging in routine clinical practice

Hokyu Kim, Hosung Kim, Mi Sun Oh, Wi‐Sun Ryu, Tai Hwan Park, Keun‐Sik Hong, Man‐Seok Park, Soo Joo Lee, Byung‐Chul Lee, Chi Kyung Kim, Moses Lee, Sang‐Soon Park, Dong‐Eog Kim, Jae‐Kwan Cha, Hong‐Kyun Park, Yong‐Jin Cho, Sang‐Wuk Jeong, Jong‐Moo Park, Kyusik Kang, Dong-Seok Gwak, Jun Lee, Beom Joon Kim, Dae-Hyun Kim, Joon‐Tae Kim, Kyungbok Lee, Jae Guk Kim, Jinyong Chung, Hoyoun Lee, Hee-Joon Bae, Kyung-Ho Yu
article en

Abstract

Changing the main architecture of the segmentation model alone maintained segmentation performance within ischemic-stroke cohorts, while achieving better classification in broader disease populations. This study highlights the need for deep-learning models to be validated not only for segmentation performance within target disease cohorts but also across diverse clinical environments to ensure practical utility.

Digital HealthVol. 11
University of Southern California (US), Eulji University (KR), Korea University Medical Center (KR), Dong-A University Hospital (KR), Soonchunhyang University Hospital Seoul (KR), Seoul National University Bundang Hospital (KR), Seoul Medical Center (KR), Chonnam National University Hospital (KR), Dongguk University Ilsan Hospital (KR), Inje University Ilsan Paik Hospital (KR), Daejeon Eulji Medical Center, Eulji University (KR), Singapore Bioimaging Consortium (SG), Hallym University Sacred Heart Hospital (KR), Yeungnam University (KR)
Openalex Percentile: Top 55%
Acute Ischemic Stroke Management
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