Purpose: Our study explored the use of artificial intelligence (AI) to diagnose autism spectrum disorder (ASD). It focused on machine learning (ML) and deep learning (DL) to detect ASD from text inputs on social media, addressing challenges in traditional ASD diagnosis. Methods: We used natural language processing (NLP), ML, and DL models (including decision trees, XGB, KNN, RNN, LSTM, Bi-LSTM, BERT, and BERTweet) to analyze 404,627 tweets, classifying them based on ASD or non-ASD authors. A subset of 90,000 tweets was used for model training and testing. Results: Our AI models showed high accuracy, with an 88% success rate in identifying texts from individuals with ASD. Conclusion: The study demonstrates AI's potential in improving ASD diagnosis, especially in children, highlighting the importance of early detection.

翻译：目的：本研究探索了利用人工智能（AI）诊断自闭症谱系障碍（ASD），重点运用机器学习（ML）和深度学习（DL）方法从社交媒体文本输入中检测ASD，以应对传统ASD诊断面临的挑战。方法：采用自然语言处理（NLP）、ML和DL模型（包括决策树、XGB、KNN、RNN、LSTM、Bi-LSTM、BERT和BERTweet）分析404,627条推文，根据作者是否为ASD患者进行分类。使用其中90,000条推文子集进行模型训练和测试。结果：我们的AI模型展现出高准确率，识别ASD个体文本的成功率达到88%。结论：该研究证明了AI在改善ASD诊断方面的潜力，尤其对于儿童患者，凸显了早期检测的重要性。