Editorial: Improving autism spectrum disorder diagnosis using machine learning techniques DOI Creative Commons
Mahmoud Elbattah, Osman Ali Sadek Ibrahim, Gilles Dequen

и другие.

Frontiers in Neuroinformatics, Год журнала: 2024, Номер 18

Опубликована: Дек. 6, 2024

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterised by challenges in social communication, repetitive behaviours, and restricted interests [1]. Early accurate diagnosis critical for effective intervention, enabling individuals with ASD to achieve better developmental outcomes an improved quality of life. However, traditional diagnostic methods, often reliant on subjective behavioural observations, remain timeintensive inconsistently accessible. This underscores urgent need innovative, scalable, objective tools [2,3].Machine Learning (ML) has emerged as transformative approach diagnosis, offering the ability analyse large, datasets uncover patterns that surpass human capability. For instance, eye-tracking technologies have been extensively utilised quantify gaze behaviours such fixations saccades, well-established markers autism. Studies employing Deep achieved high accuracy classifying from typically developing based data [3,7]. These technological advancements provide foundation are not only efficient but also potentially generalisable across diverse populations.Furthermore, approaches transforming scanpaths into visual representations classification simplified pipeline, automation traditionally laborious processes [4]. Additionally, unsupervised learning techniques, including clustering data, demonstrated potential extracting unique insights variability presentations [5]. developments illustrate growing synergy between AI-driven clinical practices.Beyond eye tracking, other modalities structural MRI (sMRI), resting-state functional connectivity (rsFC), multimodal integrating genetic, behavioural, imaging shown promise identifying robust biomarkers ASD. methodologies underscore importance leveraging multidimensional improve precision reliability [2,6]. Despite these promising innovations, persist. Standardisation methodologies, reproducibility results, translation research applicability significant barriers. special issue seeks address presenting cuttingedge integrates ML neuroinformatics enhance accuracy, efficiency, accessibility diagnostics. By bridging gap technology practice, this collection studies aims drive field toward more equitable solutions diagnosis.The articles included explore various aspects through ML, innovative findings:Eslami et al. comprehensive review models applied sMRI fMRI datasets, examining their efficacy diagnosing related disorders. The study highlights key deep architectures identifies limitations heterogeneity challenges. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2020.575999Vector Machine (SVM) models. Their uncovers discriminative within Default Mode Network (DMN), achieving reinforcing rsFC https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2022.761942Jia conduct bibliometric analysis, mapping global landscape AI applications findings highlight trends rise feature selection significance integration, providing roadmap future studies. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2023.1310400Ruan present exploratory using micro-expressions biomarkers. posed video quality, work emphasises combining neuroimaging data. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2024.1435091The contributions emphasise nature holistic framework. Challenges lack standardisation ethical considerations algorithm deployment, interpretability relevant. integration advanced computational methods expertise opens avenues personalised treatment strategies early intervention protocols.We envision should focus on:• Data Diversity Multimodal Integration: Combining imaging, model robustness.• Interpretable AI: Developing transparent algorithms clinicians can trust use effectively.

Язык: Английский

Early detection of autism spectrum disorder using explainable AI and optimized teaching strategies DOI
Sarah A. Alzakari, Arwa Allinjawi, Asma Aldrees

и другие.

Journal of Neuroscience Methods, Год журнала: 2024, Номер 413, С. 110315 - 110315

Опубликована: Ноя. 10, 2024

Язык: Английский

Процитировано

1
Editorial: Improving autism spectrum disorder diagnosis using machine learning techniques DOI Creative Commons
Mahmoud Elbattah, Osman Ali Sadek Ibrahim, Gilles Dequen

и другие.

Frontiers in Neuroinformatics, Год журнала: 2024, Номер 18

Опубликована: Дек. 6, 2024

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterised by challenges in social communication, repetitive behaviours, and restricted interests [1]. Early accurate diagnosis critical for effective intervention, enabling individuals with ASD to achieve better developmental outcomes an improved quality of life. However, traditional diagnostic methods, often reliant on subjective behavioural observations, remain timeintensive inconsistently accessible. This underscores urgent need innovative, scalable, objective tools [2,3].Machine Learning (ML) has emerged as transformative approach diagnosis, offering the ability analyse large, datasets uncover patterns that surpass human capability. For instance, eye-tracking technologies have been extensively utilised quantify gaze behaviours such fixations saccades, well-established markers autism. Studies employing Deep achieved high accuracy classifying from typically developing based data [3,7]. These technological advancements provide foundation are not only efficient but also potentially generalisable across diverse populations.Furthermore, approaches transforming scanpaths into visual representations classification simplified pipeline, automation traditionally laborious processes [4]. Additionally, unsupervised learning techniques, including clustering data, demonstrated potential extracting unique insights variability presentations [5]. developments illustrate growing synergy between AI-driven clinical practices.Beyond eye tracking, other modalities structural MRI (sMRI), resting-state functional connectivity (rsFC), multimodal integrating genetic, behavioural, imaging shown promise identifying robust biomarkers ASD. methodologies underscore importance leveraging multidimensional improve precision reliability [2,6]. Despite these promising innovations, persist. Standardisation methodologies, reproducibility results, translation research applicability significant barriers. special issue seeks address presenting cuttingedge integrates ML neuroinformatics enhance accuracy, efficiency, accessibility diagnostics. By bridging gap technology practice, this collection studies aims drive field toward more equitable solutions diagnosis.The articles included explore various aspects through ML, innovative findings:Eslami et al. comprehensive review models applied sMRI fMRI datasets, examining their efficacy diagnosing related disorders. The study highlights key deep architectures identifies limitations heterogeneity challenges. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2020.575999Vector Machine (SVM) models. Their uncovers discriminative within Default Mode Network (DMN), achieving reinforcing rsFC https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2022.761942Jia conduct bibliometric analysis, mapping global landscape AI applications findings highlight trends rise feature selection significance integration, providing roadmap future studies. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2023.1310400Ruan present exploratory using micro-expressions biomarkers. posed video quality, work emphasises combining neuroimaging data. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2024.1435091The contributions emphasise nature holistic framework. Challenges lack standardisation ethical considerations algorithm deployment, interpretability relevant. integration advanced computational methods expertise opens avenues personalised treatment strategies early intervention protocols.We envision should focus on:• Data Diversity Multimodal Integration: Combining imaging, model robustness.• Interpretable AI: Developing transparent algorithms clinicians can trust use effectively.

Язык: Английский

Процитировано

1