"Psychiatric assessment of deaf and mute patients - A case series"

Longitudinal Course of Illness in Congenitally Deaf Patient with Auditory Verbal Hallucination

Auditory verbal hallucination is one of the core symptoms of schizophrenia, same as delusions, and also occurs in many other psychiatric disorders. Significant numbers of people with congenital deafness experience auditory verbal hallucinations; however, there are only a few reports regarding the course of psychosis in people with congenital deafness. Herein, we report the case of a patient with congenital deafness and auditory verbal hallucinations whose diagnosis was changed from psychotic major depression to schizophrenia 7 years after the onset of the disease. His psychotic symptoms decreased when his primary medication was changed from an antidepressant to antipsychotic drugs, based on the change of diagnosis. In the treatment of congenitally deaf patients with auditory verbal hallucinations, the inability to communicate through spoken language may interfere with proper diagnosis and treatment. The ability to collect detailed information in ways other than through verbal language is imperative for psychiatrists to determine the appropriate diagnosis and treatment for these patients during the longitudinal course of illness.

Intelligent Malaysian Sign Language Translation System Using Convolutional-Based Attention Module with Residual Network

The deaf-mutes population always feels helpless when they are not understood by others and vice versa. This is a big humanitarian problem and needs localised solution. To solve this problem, this study implements a convolutional neural network (CNN), convolutional-based attention module (CBAM) to recognise Malaysian Sign Language (MSL) from images. Two different experiments were conducted for MSL signs, using CBAM-2DResNet (2-Dimensional Residual Network) implementing "Within Blocks" and "Before Classifier" methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time are recorded to evaluate the models' efficiency. The experimental results showed that CBAM-ResNet models achieved a good performance in MSL signs recognition tasks, with accuracy rates of over 90% through a little of variations. The CBAM-ResNet "Before Classifier" models are more efficient than "Within Blocks" CBAM-ResNet models. Thus, the best trained model of CBAM-2DResNet is chosen to develop a real-time sign recognition system for translating from sign language to text and from text to sign language in an easy way of communication between deaf-mutes and other people. All experiment results indicated that the "Before Classifier" of CBAMResNet models is more efficient in recognising MSL and it is worth for future research.