Purpose: The main aim of our study was to explore the utility of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD). The study primarily focused on using machine learning (ML) and deep learning (DL) models to detect ASD potential cases by analyzing text inputs, especially from social media platforms like Twitter. This is to overcome the ongoing challenges in ASD diagnosis, such as the requirement for specialized professionals and extensive resources. Timely identification, particularly in children, is essential to provide immediate intervention and support, thereby improving the quality of life for affected individuals. Methods: We employed natural language processing (NLP) techniques along with ML models like decision trees, extreme gradient boosting (XGB), k-nearest neighbors algorithm (KNN), and DL models such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), bidirectional encoder representations from transformers (BERT and BERTweet). We extracted a dataset of 404,627 tweets from Twitter users using the platform’s API and classified them based on whether they were written by individuals claiming to have ASD (ASD users) or by those without ASD (non-ASD users). From this dataset, we used a subset of 90,000 tweets (45,000 from each classification group) for the training and testing of these models. Results: The application of our AI models yielded promising results, with the predictive model reaching an accuracy of almost 88% when classifying texts that potentially originated from individuals with ASD. Conclusion: Our research demonstrated the potential of using AI, particularly DL models, in enhancing the accuracy of ASD detection and diagnosis. This innovative approach signifies the critical role AI can play in advancing early diagnostic techniques, enabling better patient outcomes and underlining the importance of early identification of ASD, especially in children.

The classification of clinical notes into specific diagnostic categories is critical in healthcare, especially for mental health conditions like anxiety and adjustment disorder. In this study, we compare the performance of various artificial intelligence models, including both traditional machine learning approaches (random forest, support vector machine, K-nearest neighbors, decision tree, and eXtreme Gradient Boost) and deep learning models (DistilBERT and SciBERT), to classify clinical notes into these two diagnoses. Additionally, we implemented three oversampling strategies: No Oversampling, Random Oversampling, and Synthetic Minority Over-sampling Technique (SMOTE), to assess their impact on model performance. Hyperparameter tuning was also applied to optimize model accuracy. Our results indicate that oversampling techniques had minimal impact on model performance overall. The only exception was SMOTE, which showed a positive effect specifically with Bidirectional Encoder Representations from Transformers (BERT)-based models. However, hyperparameter optimization significantly improved accuracy across the models, enhancing their ability to generalize and perform on the dataset. The decision tree and eXtreme Gradient Boost models achieved the highest accuracy among machine learning approaches, both reaching 96%, while the DistilBERT and SciBERT models also attained 96% accuracy in the deep learning category. These findings underscore the importance of hyperparameter tuning in maximizing model performance. This study contributes to the ongoing research on AI-assisted diagnostic tools in mental health by providing insights into the efficacy of different model architectures and data balancing methods.

This study aimed to evaluate whether voxel spacing-aware radiomic feature extraction improves reproducibility, variability, and discriminative performance compared to conventional preprocessing methods in anisotropic CT data. A curated cohort of 685 pulmonary nodules from the LIDC-IDRI dataset was analyzed. Three preprocessing strategies-no resampling, isotropic resampling, and voxel spacing-aware extraction-and one postprocessing approach, voxel spacing weighting, were systematically compared. A modified version of PyRadiomics was developed to compute texture and shape features directly from native images while incorporating physical voxel dimensions without interpolation. Among the 94 extracted features, spacingaware preprocessing improved reproducibility in 58 features, reduced variability in 48, and enhanced univariate discrimination in 37. An ensemble feature selection combining six statistical and machine learning methods identified between 18 and 20 robust features per method. Logistic Regression models trained with spacing-aware features achieved the highest composite performance score (1.498), balancing discrimination and generalizability. SHAP interpretability analysis confirmed the clinical relevance of selected geometric and texture features. Overall, voxel spacing-aware preprocessing preserved native spatial structure and mitigated the effects of voxel geometry and interpolation artifacts, yielding stable and clinically interpretable radiomic features. These findings support the adoption of spacing-aware pipelines in heterogeneous and multi-center CT radiomics studies to enhance feature robustness and reproducibility.