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Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review
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Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review
by Prabal Datta Barua 1,2ORCID,Jahmunah Vicnesh 3ORCID,Raj Gururajan 1,Shu Lih Oh 3,Elizabeth Palmer 4,5,Muhammad Mokhzaini Azizan 6,*ORCID,Nahrizul Adib Kadri 7 andU. Rajendra Acharya 3,8,9ORCID
1
School of Business, University of Southern Queensland, Springfield 4300, Australia
2
Faculty of Engineering and Information Technology, University of Technology, Sydney 2007, Australia
3
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
4
School of Woman’s and Children’s Health, University of New South Wales, Sydney 2031, Australia
5
Centre for Clinical Genetics, Sydney Children’s Hospital, Randwick, New South Wales 2031, Australia
6
Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Bandar Baru Nilai, Nilai 71800, Malaysia
7
Department of Biomedical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia
8
School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
9
Department of Bioinformatics and Medical Engineering, Asia University, Taichung City 41354, Taiwan
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(3), 1192; https://doi.org/10.3390/ijerph19031192
Received: 7 December 2021 / Revised: 7 January 2022 / Accepted: 10 January 2022 / Published: 21 January 2022
(This article belongs to the Special Issue Artificial Intelligence Technologies for Healthcare)
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Abstract
Mental disorders (MDs) with onset in childhood or adolescence include neurodevelopmental disorders (NDDs) (intellectual disability and specific learning disabilities, such as dyslexia, attention deficit disorder (ADHD), and autism spectrum disorders (ASD)), as well as a broad range of mental health disorders (MHDs), including anxiety, depressive, stress-related and psychotic disorders. There is a high co-morbidity of NDDs and MHDs. Globally, there have been dramatic increases in the diagnosis of childhood-onset mental disorders, with a 2- to 3-fold rise in prevalence for several MHDs in the US over the past 20 years. Depending on the type of MD, children often grapple with social and communication deficits and difficulties adapting to changes in their environment, which can impact their ability to learn effectively. To improve outcomes for children, it is important to provide timely and effective interventions. This review summarises the range and effectiveness of AI-assisted tools, developed using machine learning models, which have been applied to address learning challenges in students with a range of NDDs. Our review summarises the evidence that AI tools can be successfully used to improve social interaction and supportive education. Based on the limitations of existing AI tools, we provide recommendations for the development of future AI tools with a focus on providing personalised learning for individuals with NDDs
Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022)
No full textBackground and objectives: Artificial intelligence (AI) has branched out to various applications in health-care, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community.Methods: Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. Results: In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others.Conclusion: We discovered that detecting abnormalities in 1D biosignals and identifying key text in clini-cal notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.(c) 2022 Elsevier B.V. All rights reserved
Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)
No full text: Uncertainty estimation in healthcare involves quantifying and understanding the inherent uncertainty or variability associated with medical predictions, diagnoses, and treatment outcomes. In this era of Artificial Intelligence (AI) models, uncertainty estimation becomes vital to ensure safe decision-making in the medical field. Therefore, this review focuses on the application of uncertainty techniques to machine and deep learning models in healthcare. A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our analysis revealed that Bayesian methods were the predominant technique for uncertainty quantification in machine learning models, with Fuzzy systems being the second most used approach. Regarding deep learning models, Bayesian methods emerged as the most prevalent approach, finding application in nearly all aspects of medical imaging. Most of the studies reported in this paper focused on medical images, highlighting the prevalent application of uncertainty quantification techniques using deep learning models compared to machine learning models. Interestingly, we observed a scarcity of studies applying uncertainty quantification to physiological signals. Thus, future research on uncertainty quantification should prioritize investigating the application of these techniques to physiological signals. Overall, our review highlights the significance of integrating uncertainty techniques in healthcare applications of machine learning and deep learning models. This can provide valuable insights and practical solutions to manage uncertainty in real-world medical data, ultimately improving the accuracy and reliability of medical diagnoses and treatment recommendations
Multi-modality approaches for medical support systems: A systematic review of the last decade
No full textHealthcare traditionally relies on single-modality approaches, which limit the information available for medical decisions. However, advancements in technology and the availability of diverse data sources have made it feasible to integrate multiple modalities and gain a more comprehensive understanding of patients' conditions. Multi-modality approaches involve fusing and analyzing various data types, including medical images, biosignals, clinical records, and other relevant sources.
This systematic review provides a comprehensive exploration of the multi-modality approaches in healthcare, with a specific focus on disease diagnosis and prognosis. The adoption of multi-modality approaches in healthcare is crucial for personalized medicine, as it enables a comprehensive profile of each patient, considering their genetic makeup, imaging characteristics, clinical history, and other relevant factors. The review also discusses the technical challenges associated with fusing heterogeneous multimodal data and highlights the emergence of deep learning approaches as a powerful paradigm for multimodal data integration
ShortNeXt: A novel method for accurate classification of colorectal cancer histopathology images
No full textCancer is a chaotic disease known as the plague of our age and there are many subtypes of the cancer. Cancer is commonly seen disorder and its mortality rate is very high. Therefore, many researchers have worked/studied on the cancer detection and treatment. To contribute cancer studies according to machine learning, we have presented a new generation convolutional neural network (CNN) termed ShortNeXt in this research. The presented ShortNeXt has inspired by ResNet, ConvNeXt and MobileNet architectures to use the advantages these CNNs together. This model, which aims to extract robust feature map using convolution-based residual blocks, is named ShortNeXt because it incorporates more than one shortcut. The ShortNeXt architecture has four main stages and these stages are: (i) an input/stem, (ii) ShortNeXt, (iii) downsampling, and (iv) output. In this CNN architecture, convolution, batch normalization and the Gaussian Error Linear Unit (GELU) activation functions have been utilized. In this aspect, the implementation of the recommended ShortNeXt is simple. The stem stage uses a 4 × 4 sized convolution with stride 4 like ConvNeXt and Swin Transformer and this operation is named patchify operation. Additionally, a 2 × 2 patchify block has been used in the downsampling block. In the ShortNeXt block, an inverted bottleneck has been used, and both 1 × 1 and 3 × 3 convolution blocks are employed in the expansion phase. The output layer has increased the number of filters from 768 to 1280 by using pixel-wise convolution, drawing inspiration from MobileNetV2 and a final feature map with a length of 1280 has been obtained by deploying global average pooling (GAP). In the classification phase, fully connected and softmax operators have been used.
To get comparative results about to the recommended ShortNeXt, a publicly available histopathological image dataset has been used and this dataset contains nine classes, and the proposed ShortNeXt has achieved 97.82% and 97.86% validation and test accuracy, respectively. The obtained results and findings openly showcases that ShortNeXt is an effective deep learning method for histopathological image classification for cancer detection/classification
A novel approach using deep belief network patterns and attention binary decomposition for automated community emotion detection
No full textSound-based community emotion detection (SCED) estimates community emotion from environmental
sounds. It has value for public safety and human–computer interaction. Current SCED models have limited
adaptivity on complex audio and often need manual tuning.
Objective: We aim to design an accurate and efficient automated SCED model for large-scale data.
Methods: We propose a feature extraction framework that combines DBNPat feature generation with ATT-BP
attention-driven binary compression. The framework adapts to signal characteristics with low computational
cost. We also introduce a new dataset of 10,017 environmental sound clips (three seconds) with negative (n =
1,729), neutral (n = 6,154), and positive (n = 2,134) classes.
Results: The proposed SCED model achieves 87.28% accuracy on three-class SCED. It yields 81.30% UAR, 84.71%
precision, 82.97% F1, and 80.59% geometric mean on the imbalanced dataset. Conclusion: The model links
classical feature design and deep pattern generation in one adaptive pipeline. It offers a practical solution for
digital sound forensics and other ambient-audio systems that need fine emotion cues
Issues and Limitations on the Road to Fair and Inclusive AI Solutions for Biomedical Challenges
No full textObjective: In this paper, we explore the correlation between performance reporting and the development of inclusive AI solutions for biomedical problems. Our study examines the critical aspects of bias and noise in the context of medical decision support, aiming to provide actionable solutions. Contributions: A key contribution of our work is the recognition that measurement processes introduce noise and bias arising from human data interpretation and selection. We introduce the concept of “noise-bias cascade” to explain their interconnected nature. While current AI models handle noise well, bias remains a significant obstacle in achieving practical performance in these models. Our analysis spans the entire AI development lifecycle, from data collection to model deployment. Recommendations: To effectively mitigate bias, we assert the need to implement additional measures such as rigorous study design; appropriate statistical analysis; transparent reporting; and diverse research representation. Furthermore, we strongly recommend the integration of uncertainty measures during model deployment to ensure the utmost fairness and inclusivity. These comprehensive recommendations aim to minimize both bias and noise, thereby improving the performance of future medical decision support systems
Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques
No full textThis study presents a comprehensive systematic review focusing on the applications of deep learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 scientific publications following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we selected 153 papers for an in-depth analysis. These papers were categorized based on imaging modality, deep learning model type, and practical applications in lung cancer, such as detection and survival prediction. We specifically emphasized deep learning models and examined their strengths and limitations for each application and imaging modality. Furthermore, we identified potential limitations within the field and proposed future research directions. This study serves as a pioneering resource, being the first comprehensive and systematic review of deep learning techniques, specifically in the context of lung cancer-related applications. Our primary objective was to provide a reference for future research, encouraging the advancement of deep learning techniques in the diagnosis and treatment of lung cancer. By suggesting the most effective deep learning tools for specific application areas, we offer a benchmark for future studies. In summary, this study consolidates and expands existing knowledge on deep learning and radiomics applications in lung cancer. It provides a foundation for further research and serves as a guide for developing and evaluating deep learning models in lung cancer-related applications
Application of spatial uncertainty predictor in CNN-BiLSTM model using coronary artery disease ECG signals
No full textThis study aims to address the need for reliable diagnosis of coronary artery disease (CAD) using
artificial intelligence (AI) models. Despite the progress made in mitigating opacity with
explainable AI (XAI) and uncertainty quantification (UQ), understanding the real-world predictive
reliability of AI methods remains a challenge. In this study, we propose a novel indicator
called the Spatial Uncertainty Estimator (SUE) to assess the prediction reliability of classification
networks in practical Electrocardiography (ECG) scenarios. SUE quantifies the spatial overlap of
critical Grad-CAM (Gradient-weighted Class Activation Mapping) features, offering a confidence
score for predictions.
To validate SUE, we designed a deep learning network that integrates Convolutional Neural
Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) mechanisms for precise
ECG signal classification of CAD. This network achieved high accuracy, sensitivity, and specificity
rates of 99.6%, 99.8%, and 98.2%, respectively. During test time, SUE accurately distinguishes
between correctly classified and misclassified ECG segments, demonstrating the superiority of the
proposed network over existing methods.
The study highlights the potential of combining XAI and UQ techniques to enhance ECG
analysis. The evaluation of spatial overlap among discriminative features provides quantitative
insights into the network’s robustness, encompassing both current prediction accuracy and the
repeatability of predictions
Automated detection of ADHD: Current trends and future perspective
No full textAttention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit combinations of inattention, impulsiveness, and hyperactivity. With early treatment and diagnosis, there is potential to modify neuronal connections and improve symptoms. However, the heterogeneous nature of ADHD, combined with its comorbidities and a global shortage of diagnostic clinicians, means diagnosis of ADHD is often delayed. Hence, it is important to consider other pathways to improve the efficiency of early diagnosis, including the role of artificial intelligence. In this study, we reviewed the current literature on machine learning and deep learning studies on ADHD diagnosis and identified the various diagnostic tools used. Subsequently, we categorized these studies according to their diagnostic tool as brain magnetic resonance imaging (MRI), physiological signals, questionnaires, game simulator and performance test, and motion data. We identified research gaps include the paucity of publicly available database for all modalities in ADHD assessment other than MRI, as well as a lack of focus on using data from wearable devices for ADHD diagnosis, such as ECG, PPG, and motion data. We hope that this review will inspire future work to create more publicly available datasets and conduct research for other modes of ADHD diagnosis and monitoring. Ultimately, we hope that artificial intelligence can be extended to multiple ADHD diagnostic tools, allowing for the development of a powerful clinical decision support pathway that can be used both in and out of the hospital
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