Commentary: Machine learning for autism spectrum disorder diagnosis – challenges and opportunities – a commentary on Schulte‐Rüther et al. (2022)

Cao, Xu; Cao, Jianguo

doi:10.1111/jcpp.13764

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…Traditionally, psychology has primarily focused on explanatory (interpretable) modeling, seeking to understand the causal underpinnings of behavior. However, this emphasis on explanation has often led to models that lack meaningful predictive capacity, raising questions about the robustness and generalizability of psychological research [18].…”

Section: Use Of Machine Learning Models To Differentiate Neurodevelop...mentioning

confidence: 99%

Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study

Grazioli,

Crippa,

Buo

et al. 2024

JMIR Form Res

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Background: Child and adolescent psychopathology diagnosis involves a multifaceted approach, which integrates clinical observations, behavioral assessments, medical history, cognitive testing, and familial context information. Digital technologies, including online platforms for administering caregiver-rated questionnaires, are increasingly utilized in this field, especially during the first screening phase. With the rise of digital platforms for data collection, advanced psychopathology classification methods such as supervised machine learning (ML) have gained prominence in both research and clinical settings. This shift, recently referred to as psycho-informatics, has been facilitated by the gradual incorporation of computational devices into clinical workflows. However, an integrated telemedicine and ML approach is yet to be developed.Objective: This study aims to develop ML models for the classification of Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) diagnoses using online parent-reported socio-anamnestic data, with a primary focus on accuracy over interpretability. The objective is therefore to address the need for an innovative computational psychometrics framework that harnesses the potential of digital platforms and ML to improve the assessment of neurodevelopmental conditions. Methods:In this retrospective, single-center observational study, socio-anamnestic data were collected from 1688 children and adolescents referred for suspected neurodevelopmental conditions. The data included information on sociodemographic, clinical, environmental, and developmental factors, collected remotely through the first Italian online screening tool for neurodevelopmental disorders, the Medea Information and Clinical Assessment On-Line (MedicalBIT). Random Forest (RF) and Logistic Regression (LR) models were developed and evaluated using classification accuracy, sensitivity, specificity, and importance of independent variables.Results: RF models demonstrated robust accuracy, achieving 84% (95% CI: 82%-85%, P < .001) for ADHD and 86% (95% CI: 84%-87%, P < .001) for ASD classifications, with high sensitivities of 93% for ADHD and of 95% for ASD. In contrast, the LR models exhibited both lower accuracy (61%, 95% CI: 57%-64%, P < .001 for ADHD, and 63%, 5% CI: 60%-67%, P < .001 for ASD) and sensitivities (62% for ADHD and 68% for ASD). Intriguingly, the independent variables considered for classification differed in importance between the two models, reflecting the distinct approaches and capabilities of RF and LR. Conclusions:The present study provides evidence for the potential of an ML approach, especially when built on RF models, in enhancing the diagnostic process of child and adolescent psychopathology. While interpretability remains crucial, the developed approach might therefore provide valuable screening tools for clinicians, highlighting the significance of embedding computational techniques in the diagnostic process. This paves the way for the integration of AI-driven tools in...

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Section: Use Of Machine Learning Models To Differentiate Neurodevelop...mentioning

confidence: 99%

Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study

Grazioli,

Crippa,

Buo

et al. 2024

JMIR Form Res

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“…Alfalasi 21 reported that in United States 1 out of 54 children is affected by autism. Detecting autism earlier in one life can make a big difference than treating it later 22 . According to World Health Organization (WHO) every year one among 160 children is diagnosed with ASD traits all over the world 23 .…”

Section: Related Workmentioning

confidence: 99%

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

Farooq

Tehseen

Sabir

et al. 2023

Sci Rep

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Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).

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“…Machine learning techniques have shown potential for assisting in the early diagnosis and detection of ASD [3]. These techniques make use of algorithms to find patterns and links in massive datasets [4], which can assist in pinpointing important characteristics that set ASD sufferers apart from those without the illness.…”

Section: Introductionmentioning

confidence: 99%

Power of Alignment: Exploring the Effect of Face Alignment on Asd Diagnosis Using Facial Images

Rashid,

Alam

2024

IIUMEJ

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Autism Spectrum Disorder (ASD) is a developmental disorder that impacts social communication and conduct. ASD lacks standard treatment protocols or medication, thus early identification and proper intervention are the most effective procedures to treat this disorder. Artificial intelligence could be a very effective tool to be used in ASD diagnosis as this is free from human bias. This research examines the effect of face alignment for the early diagnosis of Autism Spectrum Disorder (ASD) using facial images with the possibility that face alignment can improve the prediction accuracy of deep learning algorithms. This work uses the SOTA deep learning-based face alignment algorithm MTCNN to preprocess the raw data. In addition, the impacts of facial alignment on ASD diagnosis using facial images are investigated using state-of-the-art CNN backbones such as ResNet50, Xception, and MobileNet. ResNet50V2 achieves the maximum prediction accuracy of 93.97% and AUC of 96.33% with the alignment of training samples, which is a substantial improvement over previous research. This research paves the way for a data-centric approach that can be applied to medical datasets in order to improve the efficacy of deep neural network algorithms used to develop smart medical devices for the benefit of mankind. ABSTRAK: Gangguan Spektrum Autisme (ASD) adalah gangguan perkembangan yang memberi kesan kepada komunikasi dan tingkah laku sosial. Kelemahan dalam rawatan ASD adalah ianya tidak mempunyai protokol rawatan standard atau ubat. Oleh itu pengenalan awal dan campur tangan betul merupakan prosedur paling berkesan bagi merawat gangguan ini. Kecerdasan buatan boleh menjadi alat berkesan bagi diagnosis ASD kerana bebas campur tangan manusia. Penyelidikan ini mengkaji kesan penjajaran muka bagi diagnosis awal ASD menggunakan imej muka dengan kebarangkalian penjajaran muka dapat meningkatkan ketepatan ramalan algoritma pembelajaran mendalam. Kajian ini menggunakan algoritma penjajaran muka MTCNN berasaskan pembelajaran mendalam SOTA bagi pra-proses data mentah. Selain itu, kesan penjajaran muka pada diagnosis ASD menggunakan imej muka disiasat menggunakan CNN terkini seperti ResNet50, Xception dan MobileNet. ResNet50V2 mencapai ketepatan ramalan maksimum sebanyak 93.97% dan AUC 96.33% dengan sampel penjajaran latihan, yang merupakan peningkatan ketara berbanding penyelidikan terdahulu. Kajian ini membuka jalan bagi pendekatan data berpusat yang boleh digunakan pada set data perubatan bagi meningkatkan keberkesanan algoritma rangkaian saraf mendalam dan membangunkan peranti perubatan pintar bermanfaat untuk manusia.

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Commentary: Machine learning for autism spectrum disorder diagnosis – challenges and opportunities – a commentary on Schulte‐Rüther et al. (2022)

Cited by 9 publications

References 7 publications

Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study

Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

Power of Alignment: Exploring the Effect of Face Alignment on Asd Diagnosis Using Facial Images

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