# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 2 Methods
### Review Parameters  
The paper selection and review procedure in this study follows the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines [46; 47]. The search terms employed were <deep learning brain MRI neurodevelopment> or <deep learning pediatric brain MRI> or <deep learning child brain MRI> or <deep learning adolescent brain MRI> to include the deep learning studies based on MRI for pediatric neurodevelopment studies. The initial search was performed on PubMed and Web of Science databases on 26 October 2022. Search engines ScienceDirect and Google Scholar were excluded due to the large number of search results returned (thousands of results). The initial search yielded 412 papers from PubMed and 252 papers from Web of science. Following the PRISMA protocols, we performed selection and review steps in Figure 6. A total of 304 duplicate records was removed in the first step. Secondly, we examined the keywords, titles, and abstracts of the remaining 360 papers and excluded review papers, case reports, papers with foreign language (French), and animal studies. Furthermore, we identified studies with topics on adult population, genetics, maternity, and non-deep learning approaches as irrelevant and excluded them. We retrieved the full paper for 184 out of the remaining 185 studies. The full papers were further examined for eligibility and 67 studies with non-pediatric population, non-MRI modality or non-deep learning methods were removed. Then, 120 Studies were carefully reviewed and 113 of them are categorized and reported in the next chapter. The remaining 7 studies on gender prediction, functional connectivity estimation, and fascicles detection are not reported. Three researchers independently examined the eligibility of the studies and conflict decisions were resolved by discussion. Data extracted from selected studies include but are not limited to the year of the study, clinical questions, study population, imaging techniques, preprocessing protocols and tools, deep learning approach, training and validation settings, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **No. of** & **Population** & **Technique** & **Citation** \\ \hline National Database for & hundreds of & 117,573 subjects by age & & \\ Autism Research (NDAR) & research projects & (57,510 affected subjects and 59,763 control subjects) & sMRI, rs-fMRI, DTI & [36] \\ \hline Open fMRI & 95 datasets & 337 subjects across all & sMRI, rs-fMRI, task & [37] \\ \hline International Consortium & - & 853 subjects & & \\ for Brain Mapping (ICBM) & - & (age 18–89 years) & sMRI, rs-fMRI, DTI & [38] \\ \hline
1000 funtional connectome & \
\begin{tabular}{c} 33 independent \\ imaging sites \\ \end{tabular} &
\begin{tabular}{c} 1355 subjects \\ (age 13–80 years) \\ \end{tabular} & rs-fMRI & [39] \\ \hline The Adolescent Brain & & 12,000 subjects & sMRI, rs-fMRI, task & [40] \\ Cognitive Development & - & (age 9–10 years) & fMRI & [41] \\ (ABCD) Study & & & & \\ \hline ENIGMA ADHD working & 34 cohorts & over 4000 subjects & sMRI, rs-fMRI, DTI & [41] \\ group & & & & \\ \hline Philadelphia & & 9500 subjects & sMRI, rs-fMRI, task & [42] \\ Neurotappmental & - & (age 8–21 years) & fMRI, DTI & [43] \\ Cohort (PNC) & & & & \\ \hline Healthy Brain Network & 10,000 subjects & sMRI, rs-fMRI, task & [43] \\ (HBN) & - & (age 5–21 years) & fMRI, DTI & [44] \\ \hline Human Connectome Project & - & 1350 subjects & sMRI, rs-fMRI, task & [44] \\ Development (dHCP) & - & (age 5–21 years) & fMRI & [45] \\ \hline The UNC/UMN Baby & 2 sites & 500 subjects & & \\ Connectome Project (BCP) & & (age 0–5 years ) & sMRI, rs-fMRI, DTI & [46] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._results, results interpretation, and limitations. Extracted information is presented and discussed in the following chapters.