Application of Artificial Intelligence in Supporting Healthcare Professionals and Caregivers in Treatment of Autistic Children

• URL: http://arxiv.org/abs/2407.08902v1
• Date: Fri, 12 Jul 2024 00:34:40 GMT
• Title: Application of Artificial Intelligence in Supporting Healthcare Professionals and Caregivers in Treatment of Autistic Children
• Authors: Hossein Mohammadi Rouzbahani, Hadis Karimipour,
• Abstract summary: This paper investigates the potential of Artificial Intelligence to augment the capabilities of healthcare professionals and caregivers in managing Autism Spectrum Disorder (ASD) We have developed a sophisticated algorithm designed to analyze facial and bodily expressions during daily activities of both autistic and non-autistic children. This research highlights the transformative potential of AI in improving the diagnosis, treatment, and comprehensive management of ASD.
• Score: 0.552480439325792
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Autism Spectrum Disorder (ASD) represents a multifaceted neurodevelopmental condition marked by difficulties in social interaction, communication impediments, and repetitive behaviors. Despite progress in understanding ASD, its diagnosis and treatment continue to pose significant challenges due to the variability in symptomatology and the necessity for multidisciplinary care approaches. This paper investigates the potential of Artificial Intelligence (AI) to augment the capabilities of healthcare professionals and caregivers in managing ASD. We have developed a sophisticated algorithm designed to analyze facial and bodily expressions during daily activities of both autistic and non-autistic children, leading to the development of a powerful deep learning-based autism detection system. Our study demonstrated that AI models, specifically the Xception and ResNet50V2 architectures, achieved high accuracy in diagnosing Autism Spectrum Disorder (ASD). This research highlights the transformative potential of AI in improving the diagnosis, treatment, and comprehensive management of ASD. Our study revealed that AI models, notably the Xception and ResNet50V2 architectures, demonstrated high accuracy in diagnosing ASD.

Related papers

• Artificial intelligence techniques in inherited retinal diseases: A review [19.107474958408847]
  Inherited retinal diseases (IRDs) are a diverse group of genetic disorders that lead to progressive vision loss and are a major cause of blindness in working-age adults. Recent advancements in artificial intelligence (AI) offer promising solutions to these challenges. This review consolidates existing studies, identifies gaps, and provides an overview of AI's potential in diagnosing and managing IRDs.
  arXiv  Detail & Related papers  (2024-10-10T03:14:51Z)
• Evaluation of OpenAI o1: Opportunities and Challenges of AGI [112.0812059747033]
  o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance. The model excelled in tasks requiring intricate reasoning and knowledge integration across various fields. Overall results indicate significant progress towards artificial general intelligence.
  arXiv  Detail & Related papers  (2024-09-27T06:57:00Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Hear Me, See Me, Understand Me: Audio-Visual Autism Behavior Recognition [47.550391816383794]
  We introduce a novel problem of audio-visual autism behavior recognition. Social behavior recognition is an essential aspect previously omitted in AI-assisted autism screening research. We will release our dataset, code, and pre-trained models.
  arXiv  Detail & Related papers  (2024-03-22T22:52:35Z)
• Diagnosis driven Anomaly Detection for CPS [44.97616703648182]
  We propose a method for utilizing deep learning-based anomaly detection to generate inputs for Consistency-Based Diagnosis (CBD) We evaluate our approach on a simulated and a real-world CPS dataset, where our model demonstrates strong performance relative to other state-of-the-art models.
  arXiv  Detail & Related papers  (2023-11-27T15:34:40Z)
• Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
  We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting. DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas. Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
  arXiv  Detail & Related papers  (2023-10-11T02:47:21Z)
• A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism [3.062772835338966]
  Autism Spectrum Disorder (autism) is a neurodevelopmental delay which affects at least 1 in 44 children. There are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related delays. This review contains insights which are relevant to neurological behavior analysis and digital psychiatry.
  arXiv  Detail & Related papers  (2023-03-07T01:14:54Z)
• Proposing a System Level Machine Learning Hybrid Architecture and Approach for a Comprehensive Autism Spectrum Disorder Diagnosis [1.2691047660244335]
  Autism Spectrum Disorder (ASD) is a severe neuropsychiatric disorder that affects intellectual development, social behavior, and facial features. We propose to develop a hybrid architecture fully utilizing both social behavior and facial feature data to improve the accuracy of diagnosing ASD.
  arXiv  Detail & Related papers  (2021-09-18T04:33:09Z)
• AI-Augmented Behavior Analysis for Children with Developmental Disabilities: Building Towards Precision Treatment [2.0324247356209835]
  We present an AI-Augmented Learning and Applied Behavior Analytics (AI-ABA) platform to provide personalized treatment and learning plans. By defining systematic experiments along with automated data collection and analysis, AI-ABA can promote self-regulative behavior.
  arXiv  Detail & Related papers  (2021-02-21T16:15:40Z)
• Early Autism Spectrum Disorders Diagnosis Using Eye-Tracking Technology [62.997667081978825]
  Lack of money, absence of qualified specialists, and low level of trust to the correction methods are the main issues that affect the in-time diagnoses of ASD. Our team developed the algorithm that will be able to predict the chances of ASD according to the information from the gaze activity of the child.
  arXiv  Detail & Related papers  (2020-08-21T20:22:55Z)
• Explainable and Scalable Machine-Learning Algorithms for Detection of Autism Spectrum Disorder using fMRI Data [0.2578242050187029]
  Our proposed deep-learning model ASD-DiagNet exhibits consistently high accuracy for classification of ASD brain scans from neurotypical scans. Our method, called Auto-ASD-Network, uses a combination of deep-learning and Support Vector Machines (SVM) to classify ASD scans from neurotypical scans.
  arXiv  Detail & Related papers  (2020-03-02T18:20:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.