# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 3 Results
### Recognizing Neurodevelopmental Disorders
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Study** & **Year** & **Disorder** & **Population** & **Technique** & **Preprocessing** & **Method** & **Results** \\ \hline \multirow{9}{*}{[55]} & \multirow{9}{*}{2020} & \multirow{9}{*}{Autism} & multi datasets: ABCD, & & & & & \\  & & & ABIDE I, II, BioBank, & & & & & \\  & & & NDAR, ICBM, Open fMRI, & & & & & \\  & & & 1000 Functional & rs-fMRI, & SPT, AFNI, & & & \\  & & & Connectomes & task-fMRI & SpeddyPP & CNN & AUROC & 0.6774 \\  & & & 43,838 total connectomes & & & & \\  & & & 1711 ASD & & & & \\  & & & (age 0.42–78 years) & & & & \\ \hline \multirow{9}{*}{[82]} & \multirow{9}{*}{2020} & \multirow{9}{*}{Autism} & YUM dataset & & & & & \\  & & & 40 ASD & & & & \\  & & & (age 29.4 \(\pm\) 11.6 years) & & & & \\  & & & 33 HC (age 30.1 \(\pm\) 5.3 years) & & & & \\  & & & ABIDE I dataset & sMRI & SPM8 & 3D CNN & Accuracy 88\% (YUM) 64\% \\  & & & 521 ASD & & & & (ABIDE) \\  & & & (age 29.4 \(\pm\) 11.6 years) & & & & \\  & & & 593 HC & & & & \\  & & & (age 30.1 \(\pm\) 5.3 years) & & & & \\ \hline \multirow{9}{*}{[69]} & \multirow{9}{*}{2021} & \multirow{9}{*}{Autism} & ABIDE I dataset & & & & & \\  & & 55 ASD & & Pipeline for & & & \\  & & & (age 14.52 \(\pm\) 6.97 years) & rs-fMRI & the Analysis & 3D CNN & Accuracy 77.74\% \\  & & & & 55 HC & & & \\  & & & (age 15.81 \(\pm\) 6.25 years) & & & & \\ \hline \multirow{9}{*}{[74]} & \multirow{9}{*}{2021} & \multirow{9}{*}{Autism} & 50 ASD 50 HC & & & & \\  & & (age 12–40 months) & task-fMRI & FSL, FEAT & 3D CNN & Accuracy 80\% \\ \cline{1-1} \cline{3-6}  & & & ABIDE I \& II dataset & & & \\  & & & 1060 ASD 1146 HC & rs-fMRI & In-house & CNN & Accuracy 89.5\% \\ \cline{1-1} \cline{3-6}  & & & (age 5–64 years) & & & & \\ \hline \multirow{9}{*}{[84]} & \multirow{9}{*}{2021} & \multirow{9}{*}{Autism} & ABIDE I dataset & & & & \\  & & 506 ASD 532 HC & & rs-fMRI & DPABI & MLP & Accuracy 78.07 \(\pm\) 4.38\% \\ \cline{1-1}  & & & (age 10–28 years) & & & & \\ \hline \multirow{9}{*}{[85]} & \multirow{9}{*}{2021} & \multirow{9}{*}{Autism} & 52 ASD 195 HC & & & & \\  & & & infants (age 24 months) & MRI & iBEAT & CNN & Accuracy 92\% \\ \cline{1-1} \cline{3-6}  & & & multi datasets: ABCD, & & & & \\  & & & ABIDE I, II, BioBank, & & & & \\  & & NDAR, Open fMRI & sMRI, & & & \\  & & & 29,288 total connectomes & rs-fMRI & AFNI, & & \\  & & & 1555 ASD & task-fMRI & SpeddyPP & CNN & AUROC 0.7354 \\ \cline{1-1} \cline{3-6}  & & & (age 0.42–78 years) & & & & \\ \hline \multirow{9}{*}{[54]} & \multirow{9}{*}{2022} & \multirow{9}{*}{Autism} & ABIDE \& UM dataset & & & \\  & & 411 HC for offline learning & & & & \\  & & 48 ASD 65 HC for testing & rs-fMRI & & & \\  & & & (age 13.8 \(\pm\) 2 years) & & & \\ \hline \multirow{9}{*}{[73]} & \multirow{9}{*}{2022} & \multirow{9}{*}{Autism} & Preschool dataset & & & & AUROC 0.787 \\  & & & 110 subjects & & & (preschool) \\  & & ABIDE I dataset & & & & 0.856 \\  & & & 1099 subjects & & & & (ABIDE) \\ \hline \multirow{9}{*}{[86]} & \multirow{9}{*}{2022} & \multirow{9}{*}{Autism} & 151 ASD 151 HC & & & & \\  & & (age 1–6 years) & & & & \\ \cline{1-1} \cline{3-6}  & & & IMPAC dataset & & & \\ \cline{1-1} \cline{3-6}  & & & 418 ASD 497 hc & sMRI, & In-house & & \\ \cline{1-1} \cline{3-6}  & & & rs-fMRI & pipeline & & \\ \cline{1-1} \cline{3-6}  & & & (age 17 \(\pm\) 9.6 years) & & & \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._