# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 3 Results
### Predicting Brain Age
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Year** & **Structure** & **Population** & **Technique** & **Preprocessing** & **Method** & **Results** \\ \hline
[117] & 2020 & Cerebral & 48 subjects & \
\begin{tabular}{c} In-house \\ pipeline \\ \end{tabular} & U-net &
\begin{tabular}{c} Testing dice \\ score 0.75 \\ \end{tabular} \\ \hline
[118] & 2021 & Cerebral & \begin{tabular}{c} 200 patients with \\ obstructive hydrocephalus \\ (age 0–22 years) \\ 199 HC (age 0–19 years) \\ \end{tabular} & sMRI & \
\begin{tabular}{c} In-house \\ pipeline \\ \end{tabular} & U-net &
\begin{tabular}{c} Dice \\ score 0.901 \\ \end{tabular} \\ \hline
[103] & 2021 & Cortical & \
\begin{tabular}{c} dHCP dataset: 403 infants, \\ ePRIME dataset: 486 infants \\ (gestation age 23–42 weeks, \\ scanned at term-equivalent \\ age) \\ \end{tabular} & sMRI & -MRITK & GAN &
\begin{tabular}{c} Dice score \\ 0.96–0.99 \\ \end{tabular} \\ \hline
[119] & 2020 & Cortical Plate & \begin{tabular}{c} 52 fetuses (gestation age \\ 22.9–31.4 weeks) \\ \end{tabular} & sMRI & \
\begin{tabular}{c} In-house \\ pipeline \\ \end{tabular} & CNN &
\begin{tabular}{c} Testing dice \\ score \\ 0.907 \(\pm\) 0.027 \\ \end{tabular} \\ \hline
[120] & 2021 & Cortical Plate & \begin{tabular}{c} 12 fetuses (gestation age \\ 16–39 weeks) \\ \end{tabular} & sMRI & \
\begin{tabular}{c} -AutoNet, \\ ITK-SNAP \\ \end{tabular} & CNN &
\begin{tabular}{c} Dice \\ score 0.87 \\ \end{tabular} \\ \hline
[121] & 2019 & \begin{tabular}{c} Intracranial \\ Volume \\ (age 30–44 weeks) \\ \end{tabular} & \
\begin{tabular}{c} 80 scans of fetuses \\ (gestation age 22.9–34.6 \\ weeks) 101 scans of infants \\ (age 30–44 weeks) \\ \end{tabular} & sMRI & - & U-net &
\begin{tabular}{c} Dice \\ score 0.976 \\ \end{tabular} \\ \hline
[122] & 2022 & \begin{tabular}{c} Limbic \\ Structure \\ \end{tabular} & \
\begin{tabular}{c} dHCPdataset: 473 subjects \\ (40.65 \(\pm\) 2.19) \\ \end{tabular} & sMRI & - & CNN &
\begin{tabular}{c} Dice \\ score 0.87 \\ \end{tabular} \\ \hline
[123] & 2022 & \begin{tabular}{c} Posterior \\ Limb of \\ Internal \\ Capsule \\ \end{tabular} & \begin{tabular}{c} 450 preterm infants \\ gestation age \(\leq\) 32 weeks, \\ scan at term-equivalent age) \\ \end{tabular} & sMRI & \
\begin{tabular}{c} In-house \\ pipeline \\ \end{tabular} & U-net &
\begin{tabular}{c} Dice \\ score 0.690 \\ \end{tabular} \\ \hline
[124] & 2022 & Tuber & \
\begin{tabular}{c} 29 subjects \\ (age 9.96 \(\pm\) 7.16 years) \\ \end{tabular} & sMRI & - & U-net &
\begin{tabular}{c} Testing dice \\ score \\ 0.59 \(\pm\) 0.23 \\ \end{tabular} \\ \hline
[125] & 2022 & Tumor & 311 pediatric subjects & sMRI & - & U-net &
\begin{tabular}{c} Dice \\ score 0.773 \\ \end{tabular} \\ \hline
[126] & 2022 & Tumor & \begin{tabular}{c} 177 patients \\ (age 0.27–17.87 years) \\ \end{tabular} & sMRI & \
\begin{tabular}{c} CaPTk \\ software \\ \end{tabular} & CNN &
\begin{tabular}{c} Dice \\ score 0.910 \\ \end{tabular} \\ \hline
[100] & 2022 & Tumor & \
\begin{tabular}{c} 122 patients \\ (age 0.2–17.9 years) \\ \end{tabular} & sMRI & ANTs & 3D U-net &
\begin{tabular}{c} Dice \\ score 0.724 \\ \end{tabular} \\ \hline
[97] & 2022 & Tumor & \begin{tabular}{c} BraTS 2020 Dataset: 369 \\ patients local dataset: \\ 22 patients (average \\ age 7.5–9 years) \\ \end{tabular} & sMRI & \
\begin{tabular}{c} In-house \\ pipeline \\ \end{tabular} & U-net &
\begin{tabular}{c} Dice \\ score 0.896 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Study [128] using 2D CNN on DTI achieved comparison results with human experts. Study [127] demonstrated superior performance of 3D CNN compared to conventional machine learning approaches and 2D CNN. Multimodal study [129] combined sMRI, rs-fMRI, and DTI features and yielded a mean absolute error of 0.381 years for children and adolescents aged 8-21 years old. The age difference for the study population varies and thus reporting of the relative error rate is necessary for comparing different methods in different studies.