Aphasia is a neurological disorder of language that precludes a person’s ability to speak, understand, read or write in any language. By virtue of this disorder being inextricably connected to language, there is a vast potential for the application of Natural Language Processing (NLP) for the diagnosis of the disorder. This paper surveys the automated machine-learning-based classification methodologies followed by an attempt to discuss a potential way in which an NLP-backed methodology could be implemented along with its accompanying challenges. It is seen that the need for standardized technology-based diagnostic solutions necessitates the exploration of such a methodology.

Autism Spectrum Disorder is a developmental disorder that may manifest in a myriad of ways such as difficulties in social interaction and a tendency to engage in repetitive patterns of behaviour. Over the years, several kinds of treatment protocols have been proposed and implemented. One such area that is attracting the attention of researchers in the field is a robot-based approach in the treatment of children diagnosed with the disorder. Here we propose a viable method via the integration of apex technological methods like Artificial Intelligence, Machine Learning and Medical Robotics, coupling it with problem specific algorithms in OpenCV along with principles of Applied Behavioural Analysis to help possibly alleviate a key symptom displayed by children in terms of level of social interaction - that of eye-contact. This would be achieved via an AI-integrated Robotic Framework. The project also considers the possibility of inclusion of the growing research field of Quantum Computing to realize the process and investigates its viability as a potential source of innovation in the future.

Background: Lower vitamin D levels have not only been associated with chronic obstructive pulmonary disease (COPD), exacerbations and lower lung functions, but also with anxiety and depression. We examined the associations of severity of anxiety and depression using HAM-A (Hamilton Anxiety Rating Scale) and HAM-D (Hamilton Depression Rating Scale) scores with COPD and vitamin D levels. Methods: Observational nested case control study was conducted in MUDHRA cohort. One hundred COPD subjects and 100 age- gender- matched non-COPD subjects (controls) underwent evaluation of socioeconomic status, respiratory symptoms, spirometry, severity of anxiety and depression, six minute walk test and estimation of serum vitamin D levels. Independent association of low vitamin D levels with severity of anxiety and depression was assessed by logistic regression. Results: COPD group had higher mean±SD anxiety and depression scores (HAM-A 8.0±3.5, HAM-D 8.72±4) compared to control group (HAM-A 4.51±2.2, HAM-D 4.3±2). The COPD group had 53 subjects with mild/moderate anxiety/depression whereas control group had 16 subjects with mild/moderate anxiety/depression. In COPD group, subjects with mild/moderate anxiety/depression had lower vitamin D levels compared to subjects with no/minimal anxiety/depression, while the difference in control group was not significant. In logistic regression, anxiety and depression levels had independent association with vitamin D levels, lung function variables, six-minute walk distance and presence of COPD. In COPD subgroup, anxiety and depression levels had independent association with breathlessness, GOLD FEV1 staging, CAT score, SGRQ-C Symptom score and exacerbation of COPD. Conclusions: Greater proportion of COPD subjects suffer from anxiety and depression as compared to subjects without COPD. Severity of anxiety and depression was greater in COPD subjects. Poorer lung functions, higher respiratory symptoms and lower vitamin D levels are associated with higher levels of anxiety and depression in COPD subjects.  There is an urgent need to recognise anxiety and depression in COPD patients.