Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Purpose: To evaluate the utility of deep learning-based automated attenuation measurements on contrast-enhanced CT (CECT) for diagnosing moderate-to-severe hepatic steatosis (HS), using histology as reference standard.
Methods: This retrospective study included 3,620 liver donors (2,393 men and 1,227 women; mean age, 31.7 ± 9.4 years), divided into the development (n = 2,714) and test (n = 906) cohorts. Attenuation values of the liver and spleen on CECT were measured both manually and using a deep learning algorithm (before and after radiologists' correction of segmentation errors). Performance of: (1) liver attenuation and (2) liver-spleen attenuation difference for diagnosing moderate-to-severe HS (> 33%) was assessed using the area under the receiver operating characteristic curve (AUC). Three different criteria targeting 95% sensitivity, 95% specificity, and the maximum Youden's index, respectively, for diagnosing moderate-to-severe HS, were developed and validated.
Results: The performance of deep learning-based measurements did not differ significantly, with or without radiologists' corrections (p = 0.13). Liver-spleen attenuation difference outperformed liver attenuation alone in diagnosing moderate-to-severe HS in both deep learning-based (AUC, 0.868 vs. 0.821; p = 0.001) and manual (AUC, 0.871 vs. 0.823; p = 0.001) measurements. In the test cohort, the criterion targeting 95% sensitivity for diagnosing moderate-to-severe HS (liver-spleen attenuation difference ≤ 2.8 HU) yielded 92.0% (69/75) sensitivity and 48.5% (403/831) specificity. The criterion targeting 95% specificity (liver-spleen attenuation difference ≤ -18.8 HU) yielded 53.3% (40/75) sensitivity and 95.7% (795/831) specificity. The criterion targeting the maximum Youden's index (liver-spleen attenuation difference ≤ -8.2 HU) yielded 82.7% (62/75) sensitivity and 80.7% (671/831) specificity.
Conclusion: Deep learning-based automated measurements of liver and spleen attenuation on CECT can be used reliably to detect moderate-to-severe HS.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s00261-025-04872-5 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Oral bioavailability property prediction based on task similarity transfer learning.
Mol Divers
September 2025
Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, 211198, China.
Drug absorption significantly influences pharmacokinetics. Accurately predicting human oral bioavailability (HOB) is essential for optimizing drug candidates and improving clinical success rates. The traditional method based on experiment is a common way to obtain HOB, but the experimental method is time-consuming and costly.
View Article and Find Full Text PDFClinical evaluation of motion robust reconstruction using deep learning in lung CT.
Phys Eng Sci Med
September 2025
Department of Radiology, Otaru General Hospital, Otaru, Hokkaido, Japan.
In lung CT imaging, motion artifacts caused by cardiac motion and respiration are common. Recently, CLEAR Motion, a deep learning-based reconstruction method that applies motion correction technology, has been developed. This study aims to quantitatively evaluate the clinical usefulness of CLEAR Motion.
View Article and Find Full Text PDFEditorial for "A Deep Learning-Based Fully Automated Cardiac MRI Segmentation Approach for Tetralogy of Fallot Patients".
J Magn Reson Imaging
September 2025
Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA.
A robust deep learning-driven framework for detecting Parkinson's disease using EEG.
Comput Methods Biomech Biomed Engin
September 2025
Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India.
Parkinson's disease (PD) is a neurodegenerative condition that impairs motor functions. Accurate and early diagnosis is essential for enhancing well-being and ensuring effective treatment. This study proposes a deep learning-based approach for PD detection using EEG signals.
View Article and Find Full Text PDFReducing motion artifacts in craniocervical background subtraction angiography with deformable registration and unsupervised deep learning.
Radiol Adv
September 2024
Department of Radiology, Northwestern University and Northwestern Medicine, Chicago, IL, 60611, United States.
Background: In clinical practice, digital subtraction angiography (DSA) often suffers from misregistration artifact resulting from voluntary, respiratory, and cardiac motion during acquisition. Most prior efforts to register the background DSA mask to subsequent postcontrast images rely on key point registration using iterative optimization, which has limited real-time application.
Purpose: Leveraging state-of-the-art, unsupervised deep learning, we aim to develop a fast, deformable registration model to substantially reduce DSA misregistration in craniocervical angiography without compromising spatial resolution or introducing new artifacts.