Automatic identification of early ischemic lesions on non-contrast CT with deep learning approach

• Published in: Scientific reports Vol. 12; no. 1; pp. 18054 - 13
• Main Authors: Sahoo, Prasan Kumar, Mohapatra, Sulagna, Wu, Ching-Yi, Huang, Kuo-Lun, Chang, Ting-Yu, Lee, Tsong-Hai
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.10.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/61; Concept learning; Decision making; Deep Learning; Humanities and Social Sciences; Humans; Ischemia; Ischemic Stroke; Lesions; Magnetic resonance imaging; multidisciplinary; Neural networks; Patients; Retrospective Studies; Science; Science (multidisciplinary); Stroke; Stroke - diagnostic imaging; Tomography, X-Ray Computed - methods
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-22939-x

Early ischemic lesion on non-contrast computed tomogram (NCCT) in acute stroke can be subtle and need confirmation with magnetic resonance (MR) image for treatment decision-making. We retrospectively included the NCCT slices of 129 normal subjects and 546 ischemic stroke patients (onset < 12 h) with corresponding MR slices as reference standard from a prospective registry of Chang Gung Research Databank. In model selection, NCCT slices were preprocessed and fed into five different pre-trained convolutional neural network (CNN) models including Visual Geometry Group 16 (VGG16), Residual Networks 50, Inception-ResNet-v2, Inception-v3, and Inception-v4. In model derivation, the customized-VGG16 model could achieve an accuracy of 0.83, sensitivity 0.85, F-score 0.80, specificity 0.82, and AP 0.82 after using a tenfold cross-validation method, outperforming the pre-trained VGG16 model. In model evaluation, the customized-VGG16 model could correctly identify 53 in 58 subjects (91.37%) including 29 ischemic stroke patients and 24 normal subjects and reached the sensitivity of 86.95% in identifying ischemic NCCT slices (200/230), irrespective of supratentorial or infratentorial lesions. The customized-VGG16 CNN model can successfully identify the presence of early ischemic lesions on NCCT slices using the concept of automatic feature learning. Further study will be proceeded to detect the location of ischemic lesion.