# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 3 Results  
### Recognizing Neurodevelopmental Disorders  
Neurodevelopmental disorders are common brain disorders in children, bringing a variety of challenges to the affected patients and causing great burdens to their families. Various genetic and environmental factors may perturb the developmental process and result in neurodevelopmental disorders [49]. Autism spectrum disorder (ASD) is one of the most common neurodevelopmental disorders [50]. ASD is characterized by early deficits in social interactions and communication accompanied by restricted and repetitive behaviors [49]. Review papers [7,17] summarized a selected number of studies using artificial intelligence approaches to classify ASD patients and healthy controls including both conventional machine learning methods and deep learning methods. This review listed the recent deep learning advancements using MLP, CNN, RNN, and auto-encoder models (Table 2). Rs-fMRI is widely utilized for ASD recognition. Connectomes derived from fMRI were used as inputs to MLP, CNN,  
Figure 6: Study selection procedure. and RNN for classification [51; 52; 53; 54]. A multimodal study [55] combined sMRI, rs-fMRI, and task fMRI. Attention deficit hyperactivity disorder (ADHD) is another common neurodevelopmental-mental disorder [50]. ADHD patients often suffer from hyperactivity, impulsivity, and inattention, and ADHD often continues to adulthood [56]. Previous ADHD recognition studies were summarized in the review paper [7] in conventional machine learning category and deep learning category. This review paper focuses on more recent studies utilizing deep learning approaches for ADHD detection (Table 2). Both rs-fMRI and sMRI are employed as inputs for deep learning networks. Neurodevelopmental disorders which are less common such as cerebellar dysplasia [57], dyslexic [58], epilepsy [59; 60], conduct disorder [61], disruptive behavior disorder [62], and post-traumatic stress disorder [63] are also reviewed in this study. We also include three studies for detection of posterior fossa tumors and tubers in tuberous sclerosis complex [64; 65; 66], and two studies for white matter pathway classification [67; 68]. This review aims to investigate the deep learning methods utilized in various pediatric topics in an overall manner and therefore includes multiple disorders. Structural imaging techniques such as sMRI and DTI are more commonly utilized in these studies. Overall, the selected studies are summarized in Table 2. Most studies conducted baseline comparisons using conventional machine learning approaches and reported the superior performance of deep learning approaches [53; 69]. CNN dominates in the image recognition tasks. A total of 41 out of 48 neurodevelopmental disorder classification studies in this review utilized CNN approaches. Advanced CNN architectures such as inception and residual modules were employed in 2D CNN models [70; 71; 72]. Several studies trained 3D CNN with a limited number of sample size [61; 69; 73; 74], bringing concerns on overfitting. Large-scale studies which involve thousands of training data were conducted using public datasets and repositories [75; 76; 77; 55]. Multimodal studies combined features from multiple MRI modalities showed better performance than single modality [62; 76].