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ホリ マサアキ
Hori Masaaki
堀 正明 所属 東邦大学 医学部 医学科（大森病院） 職種 教授 |
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| 論文種別 | 原著 |
| 言語種別 | 英語 |
| 査読の有無 | 査読あり |
| 表題 | Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning. |
| 掲載誌名 | 正式名：Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine ISSNコード：18802206/13473182 |
| 掲載区分 | 国内 |
| 巻・号・頁 | 22(1),pp.57-66 |
| 著者・共著者 | Wada A, Saito Y, Fujita S, Irie R, Akashi T, Sano K, Kato S, Ikenouchi Y, Hagiwara A, Sato K, Tomizawa N, Hayakawa Y, Kikuta J, Kamagata K, Suzuki M, Hori M, Nakanishi A, Aoki S |
| 発行年月 | 2023/01/01 |
| 概要 | PURPOSE: Myelination-related MR signal changes in white matter are helpful for assessing normal development in infants and children. A rule-based myelination evaluation workflow regarding signal changes on T1-weighted images (T1WIs) and T2-weighted images (T2WIs) has been widely used in radiology. This study aimed to simulate a rule-based workflow using a stacked deep learning model and evaluate age estimation accuracy. METHODS: The age estimation system involved two stacked neural networks: a target network-to extract five myelination-related images from the whole brain, and an age estimation network from extracted T1- and T2WIs separately. A dataset was constructed from 119 children aged below 2 years with two MRI systems. A four-fold cross-validation method was adopted. The correlation coefficient (CC), mean absolute error (MAE), and root mean squared error (RMSE) of the corrected chronological age of full-term birth, as well as the mean difference and the upper and lower limits of 95% agreement, were measured. Generalization performance was assessed using datasets acquired from different MR images. Age estimation was performed in Sturge-Weber syndrome (SWS) cases. RESULTS: There was a strong correlation between estimated age and corrected chronological age (MAE: 0.98 months; RMSE: 1.27 months; and CC: 0.99). The mean difference and standard deviation (SD) were -0.15 and 1.26, respectively, and the upper and lower limits of 95% agreement were 2.33 and -2.63 months. Regarding generalization performance, the performance values on the external dataset were MAE of 1.85 months, RMSE of 2.59 months, and CC of 0.93. Among 13 SWS cases, 7 exceeded the limits of 95% agreement, and a proportional bias of age estimation based on myelination acceleration was exhibited below 12 months of age (P = 0.03). CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of ag |