Age estimates from brain magnetic resonance images of children younger than two years of age using deep learning

Kawaguchi, Masahiro; Kidokoro, Hiroyuki; Ito, Rintaro; Shiraki, Anna; Suzuki, Takeshi; Maki, Yuki; Tanaka, Masaharu; Sakaguchi, Yoko; Yamamoto, Hiroyuki; Takahashi, Y; Naganawa, Shinji; Natsume, Jun

doi:10.1016/j.mri.2021.03.004

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Kawaguchi et al's model estimated a younger age than the corrected chronological age in six of the seven cases of myelination delay. 20 Hong et al did not validate their model with myelination abnormalities. 19 Another proposed method of comparison is to evaluate the general performance with external data.…”

Section: Discussionmentioning

confidence: 97%

“…The previous two studies have reported age estimation from brain MRI using deep learning in infants and children up to 2 years of age. 19,20 Hong et al constructed an optimized 10-layer 3D CNN with whole-brain sagittal T1WI and reported MAE of 67.6 days (2.3 months), RMSE of 96.1 days (3.2 months), and CC of 0.9685, and the mean difference and the upper and lower limits of 95% agreement (mean difference ± 1.96 SD) between the estimated age and the corrected chronological age were 4.16 ± 73.5 days (0.14 ± 2.45 months), respectively, in Bland-Altman analysis. 19 Moreover, Kawaguchi et al constructed a multilayer CNN consisting of six convolutional layers and fully connected layers with independent inputs of single T1-and T2WI at the level of the corpus callosum splenium.…”

Section: Discussionmentioning

confidence: 99%

“…19 Moreover, Kawaguchi et al constructed a multilayer CNN consisting of six convolutional layers and fully connected layers with independent inputs of single T1-and T2WI at the level of the corpus callosum splenium. 20 They reported an MAE of 8.2 weeks (1.9 months), RMSE of 12.6 weeks (2.9 months), and CC of 0.94, and Bland-Altman analysis was only adopted to compare the performance of humans and artificial intelligence. The current results revealed an MAE of 0.98 months, RMSE of 1.27 months, CC of 0.99, and a mean difference ± 1.96 SD of −0.15 ± 2.48 months, exhibiting a higher CC and smaller error values compared with the previous studies.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding age estimation of infants and children, two studies have reported a deep learning approach for brain MRI. 19 , 20 One study used sagittal T1WIs of the whole brain, and the other used single T1- and T2WIs at the level of the corpus callosum. The former only uses T1WI features, and the latter only uses one section of the brain.…”

Section: Introductionmentioning

confidence: 99%

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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

Wada

Saito

Fujita

et al. 2023

MRMS

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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 T1and 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 age.

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Section: Discussionmentioning

confidence: 97%