Computer aided diagnosis of neurodevelopmental disorders and genetic syndromes based on facial images – A systematic literature review

Rosindo Daher de Barros, Fábio; Novais F. da Silva, Caio; de Castro Michelassi, Gabriel; Brentani, Helena; Nunes, Fátima L.S.; Machado-Lima, Ariane

doi:10.1016/j.heliyon.2023.e20517

Cited by 3 publications

(2 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Autism Spectrum enhance accuracy, and also facilitate comprehension of the techniques and algorithms employed for various types of data. Multiple studies have been conducted on ASD [22][23][24][25][26][27][28], ADHD [29][30][31], ID [9,10,32], SLD [33,34], CD [35], and NDs [4,19,20,31,[36][37][38], providing evidence that ML algorithms can enhance diagnostic strategies for NDs. Further, more research efforts that seek to investigate ML approaches for early detection and diagnosis of NDs in real-life situations are crucial for ensuring timely intervention and optimizing lifelong outcomes [17,19,20,[39][40][41].…”

Section: Introductionmentioning

confidence: 99%

“…Multiple studies have been conducted on ASD [22][23][24][25][26][27][28], ADHD [29][30][31], ID [9,10,32], SLD [33,34], CD [35], and NDs [4,19,20,31,[36][37][38], providing evidence that ML algorithms can enhance diagnostic strategies for NDs. Further, more research efforts that seek to investigate ML approaches for early detection and diagnosis of NDs in real-life situations are crucial for ensuring timely intervention and optimizing lifelong outcomes [17,19,20,[39][40][41]. This way, early intervention can help children with NDs develop their skills and abilities to the fullest extent possible.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution

Toki,

Tatsis,

Pange

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Developmental domains refer to different areas of a child’s growth and maturation, including physical, language, cognitive, and social–emotional skills. Understanding these domains helps parents, caregivers, and professionals track a child’s progress and identify potential areas of concern. Nevertheless, due to the high level of heterogeneity and overlap, neurodevelopmental disorders may go undiagnosed in children for a crucial period. Detecting neurodevelopmental disorders at an early stage is fundamental. Digital tools like artificial intelligence can help clinicians with the early detection process. To achieve this, a new method has been proposed that creates artificial features from the original ones derived from the SmartSpeech project, using a feature construction procedure guided by the Grammatical Evolution technique. The new features from a machine learning model are used to predict neurodevelopmental disorders. Comparative experiments demonstrated that using the feature creation method outperformed other machine learning methods for predicting neurodevelopmental disorders. In many cases, the reduction in the test error reaches up to 65% to the next better one.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution

Toki,

Tatsis,

Pange

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

Early Diagnosis of Vascular Ehlers-Danlos Syndrome Through AI-Powered Facial Analysis: Results from the Montalcino Aortic Consortium

Murdock,

Suresh,

Martinez

et al. 2024

Preprint

View full text Add to dashboard Cite

Purpose: Vascular Ehlers-Danlos syndrome (vEDS), caused by COL3A1 pathogenic variants, is a rare heritable aortic and arterial disorder associated with early mortality, mainly due to spontaneous vascular dissections and ruptures. Improved methods for diagnosing vEDS are needed so that guideline-based management can be initiated to prevent deadly complications and differentiate vEDS from overlapping conditions like hypermobile EDS (hEDS). Methods: We implemented an AI facial recognition model based on the PhenoScore framework using a support vector machine (SVM) trained on facial images of thirty individuals, aged 6-65 years, with vEDS from the Montalcino Aortic Consortium (MAC), control images from the Chicago Face Database (CFD), and publicly available images of individuals with hEDS. Cross-validation was used to train the SVM, and statistical measures to evaluate the model performance were calculated. Local Interpretable Model-agnostic Explanations (LIME) was used to generate facial heatmaps highlighting the features driving the model's predictions. Results: The AI classifier showed excellent performance with as few as thirteen vEDS training images and distinguished vEDS from both controls and individuals with hEDS with high accuracy, achieving an area under the receiver operating characteristic curve (AUC) ≥ 0.97. LIME highlighted facial regions already established to characterize the facial features of vEDS patients (e.g., prominent eyes). Conclusion: Our results demonstrate the potential of AI-based facial analysis for diagnosing vEDS. This method democratizes the early diagnosis of vEDS by reducing dependence on genetic testing, enabling optimal management and improved outcomes, particularly in resource-limited areas.

show abstract

Machine Learning for Predicting Neurodevelopmental Disorders in Children

Toki,

Tsoulos,

Santamato

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Developmental domains like physical, verbal, cognitive, and social-emotional skills are crucial for monitoring a child’s growth. However, identifying neurodevelopmental deficiencies can be challenging due to the high level of variability and overlap. Early detection is essential, and digital procedures can assist in the process. This study leverages the current advances in artificial intelligence to address the prediction of neurodevelopmental disorders through a comprehensive machine learning approach. A novel and recently developed serious game dataset, collecting various data on children’s speech and linguistic responses, was used. The initial dataset comprised 520 instances, reduced to 473 participants after rigorous data preprocessing. Cluster analysis revealed distinct patterns and structures in the data, while reliability analysis ensured measurement consistency. A robust prediction model was developed using logistic regression. Applied to a subset of 184 participants with an average age of 7 years, the model demonstrated high accuracy, precision, recall, and F1-score, effectively distinguishing between instances with and without neurodevelopmental disorders. In conclusion, this research highlights the effectiveness of the machine learning approach in diagnosing neurodevelopmental disorders based on cognitive features, and offers new opportunities for decision making, classification, and clinical assessment, paving the way for early and personalized interventions for at-risk individuals.

show abstract

Computer aided diagnosis of neurodevelopmental disorders and genetic syndromes based on facial images – A systematic literature review

Cited by 3 publications

References 78 publications

Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution

Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution

Early Diagnosis of Vascular Ehlers-Danlos Syndrome Through AI-Powered Facial Analysis: Results from the Montalcino Aortic Consortium

Machine Learning for Predicting Neurodevelopmental Disorders in Children

Contact Info

Product

Resources

About