Machine learning-based automatic estimation of cortical atrophy using brain computed tomography images

Jae Won Jang, Jeonghun Kim, Sang Won Park, Payam Hosseinzadeh Kasani, Yeshin Kim, Seongheon Kim, Soo Jong Kim, Duk L. Na, Seung Hwan Moon, Sang Won Seo, Joon Kyung Seong

Research output: Contribution to journalArticlepeer-review


Cortical atrophy is measured clinically according to established visual rating scales based on magnetic resonance imaging (MRI). Although brain MRI is the primary imaging marker for neurodegeneration, computed tomography (CT) is also widely used for the early detection and diagnosis of dementia. However, they are seldom investigated. Therefore, we developed a machine learning algorithm for the automatic estimation of cortical atrophy on brain CT. Brain CT images (259 Alzheimer’s dementia and 55 cognitively normal subjects) were visually rated by three neurologists and used for training. We constructed an algorithm by combining the convolutional neural network and regularized logistic regression (RLR). Model performance was then compared with that of neurologists, and feature importance was measured. RLR provided fast and reliable automatic estimations of frontal atrophy (75.2% accuracy, 93.6% sensitivity, 67.2% specificity, and 0.87 area under the curve [AUC]), posterior atrophy (79.6% accuracy, 87.2% sensitivity, 75.9% specificity, and 0.88 AUC), right medial temporal atrophy (81.2% accuracy, 84.7% sensitivity, 79.6% specificity, and 0.88 AUC), and left medial temporal atrophy (77.7% accuracy, 91.1% sensitivity, 72.3% specificity, and 0.90 AUC). We concluded that RLR-based automatic estimation of brain CT provided a comprehensive rating of atrophy that can potentially support physicians in real clinical settings.

Original languageEnglish
Article number14740
JournalScientific reports
Issue number1
Publication statusPublished - 2022 Dec

Bibliographical note

Funding Information:
This research was supported by the “National Institute of Health” research project (2021-ER1006-01). This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant Number: HI19C1132).

Publisher Copyright:
© 2022, The Author(s).

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