Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases

Abstract

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the “big data” revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

An Observational Study of Heart Rate Variability Using Wearable Sensors Provides a Target for Therapeutic Monitoring of Autonomic Dysregulation in Patients with Rett Syndrome

“…This method has allowed the accurate detection and classification of stress signals from bio-signals [ 65 ], and by using an E4 Empatica device, others have used BVP, EDA and accelerometer data to classify stress with a 93% accuracy using a neural network algorithm [ 66 ]. When looking through the lens of neurodevelopmental disabilities, machine learning approaches have guided researchers with opportunities unique to this population [ 67 ]. This allows strategies of machine learning such as feature extraction, model training and evaluation to be adopted that might help to automate classification of HRV indices in patient populations with global developmental delay.…”

Computer Vision-Based Assessment of Autistic Children: Analyzing Interactions, Emotions, Human Pose, and Life Skills

“…Technological advances and the availability of low-cost cloud infrastructure have motivated researchers to automate the creation and processing of video data by constructing data pipelines. Integrating data pipelines with ML technology has advanced the development of cost-effective ASD detection and assessment methods [31], [39], [49]. However, ASD diagnostic services are not always accessible, cost-effective, or data-driven.…”

“…Artificial intelligence (AI) technology especially ML and DL can address these limitations due to its unique facets such as increased processing power of computer hardware and multimodal data availability, thereby leading to faster ASD diagnosis [39]. Recently, the clinical study of multi-modular ML-based ASD diagnosis based on questionnaires and home videos has demonstrated a sensitivity of 90% towards ASD detection [40].…”

Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors

“…In computational biology, machine learning (ML) technologies have brought about a new paradigm shift [ 61 , 62 , 63 ]. Evidently, the ML-driven approach applied to clinical diagnosis has the potential to supplement traditional methods based on symptoms and external observations, intending to advance the individualized treatment plan [ 64 ]. ML approaches are fast expanding fields with applications in computational neuroscience as a result of improved neural data analysis efficiency and decoding brain function [ 17 , 61 , 64 , 65 , 66 , 67 , 68 ].…”

“…Evidently, the ML-driven approach applied to clinical diagnosis has the potential to supplement traditional methods based on symptoms and external observations, intending to advance the individualized treatment plan [ 64 ]. ML approaches are fast expanding fields with applications in computational neuroscience as a result of improved neural data analysis efficiency and decoding brain function [ 17 , 61 , 64 , 65 , 66 , 67 , 68 ]. In neuroscience, the issue substantially restricts the extent and depth to which neural signatures can be functionally associated with human behaviour.…”