Detection of autism spectrum disorder (ASD) may soon become easy and convenient like never before, thanks to an epoch-making chatbot developed by Naga Simhadri Apparao Polireddi and his fellow researchers. Utilizing AI and ML, Polireddi's autism prediction chatbot looks to improve the quality of autism care by bridging the gap between patients, their parents, and physicians.
Autism spectrum disorder (ASD) is a neurodevelopmental condition that leads to difficulties with social interaction and communication. As a result of the social stigma associated with ASD, parents are often hesitant to talk about their children's condition and seek help from physicians. This dilemma leads to delayed autism diagnoses. Early detection is critical to the optimum development of ASD sufferers, and experts believe that prime clinical investigation time is lost because of these delays. According to a new study, there is an average delay of more than two years between initial autism screenings and a diagnosis.
Early State Detection of ASD with Fuzzy Logic
An artificial intelligence (AI) and machine learning (ML) technology, fuzzy logic is capable of identifying elements in a dataset that are not identical but are similar. It can help locate patterns within a larger text, allowing users to deal with incomplete or imprecise data. Therefore, in the field of medicine, doctors can use fuzzy logic to make accurate diagnoses, even if the symptoms are vague and the patient's data is incomplete.
Making use of fuzzy logic, fuzzy classification can group elements into classes referred to as fuzzy sets. In natural language processing (NLP), fuzzy classifiers are used to imitate an individual's decision-making process. Chatbots make use of the advanced machine-learning algorithms of NLP to learn human languages.
Along with his associates, Mr. Polireddi strongly believes that the extraordinary capabilities of artificial intelligence and machine learning can be utilized for early detection of autism spectrum disorder (ASD). To enable unobstructed integrations between patients and doctors, he proposes a chatbot based on fuzzy classifiers. The team has also evaluated the accessibility of private and government websites for people with disabilities using fuzzy classifiers.
"Chatbots provide a private and non-judgmental space for individuals to seek information and support. By promoting self-service through an AI-driven platform, I aim to reduce the stigma associated with seeking assistance for ASD-related concerns," Polireddi mentions.
Chatbot for Autism Prediction
In addition to ML algorithms, the novel autism prediction chatbot conceptualized by Polireddi uses a database created in an Excel spreadsheet. The conversational bot has been fine-tuned to ensure effective communication with the user.
A touchscreen tablet with a camera is required to interact with the dashboard. While chatting with patients, doctors can use images and store the entire conversation for future reference. Patients can also upload their children's videos in different situations, allowing experts to use these video evidence for more efficient ASD screening.
This ASD prediction chatbot can chat with the child even when the doctor isn't available. Moreover, while interacting with young kids, the interactive chatbot also conveys all human emotions. It utilizes the decision-tree architecture to provide personalized responses reflecting the moods of the patients. It can help doctors in their decision-making process by offering weekly summaries related to the emotional states and mental conditions of the patients.
Breaking the Web Accessibility Barrier
To make the most of Polireddi's chatbot, it is important to ensure unrestricted web accessibility for everyone, including those with cognitive or physical limitations. Unfortunately, despite web accessibility standards and principles such as the Web Content Accessibility Guidelines, web accessibility barriers are faced by disabled persons. Using the Web Accessibility Barrier (WAB) score, Polireddi has conducted a retrospective analysis of websites to understand these accessibility-related modifications and their impact.
"Web accessibility empowers people with disabilities, enabling them to participate in social activities, education, and employment, as well as accessing vital government and healthcare services. In this digital age, web designers have a responsibility to ensure that everyone, regardless of ability or disability, has equal access to information," Polireddi explains.
Leading the Way in Digital Transformation
In November 2023, Polireddi discussed web accessibility at the BioS-BioL 2023, a conference dedicated to improving patient access to life-saving therapies by advancing the development of biosimilars. His autism prediction chatbot was also showcased and highly appreciated at the Global Congress on Advanced Satellite Communications 2023 and IEEE Phoenix Tech Conferenceand Expo 2023. This year, he will be one of the distinguished speakers at the Autorobo Expo 2024 and the BioS-BioL Global Summit 2024.
A Master's Degree holder in Computer Science from Arizona State University, Polireddi is currently a Software Technical Lead at Ikon Tech. An expert in the field of AI/ML technologies, he has led important digital transformation initiatives across industries.Through his industrial proficiency and groundbreaking research, he has enhanced operational efficiencies as well as user experiences across industries.
Mr. Polireddi has also received acclaim in academia, with book chapters in Taylor and Francis as well as publications in Elsevier, Springer Nature, and IEEE Xplore. His article titled "Raising Awareness Among Educators on AI and ML Applications Utilizing Fuzzy Logic in the Education Sector in the USA" has been featured by the United States Government on the National Institute of Health (NIH) platform's website.
Polireddi has developed an ML chatbot GPT called APPRAO GPT for public use, leveraging his expertise in secure web development, web accessibility, RTK GPS technology, and AI/ML.
Future Plans
Polireddi's future plan is to further develop his chatbot by implementing continuous learning and adaptation mechanisms. This will involve the incorporation of user feedback, making use of new data to update the fuzzy classifier, and taking all possible measures so that the chatbot remains responsive to the changing needs and preferences of users. He also plans to conduct a detailed analysis of web accessibility standards and practices in the USA compared to international benchmarks.
To learn more about Mr. Polireddi and his latest research activities, please visit his Google Scholar profile or connect with him via LinkedIn.
