Cortese Samuele, Bellato Alessio, Gabellone Alessandra, Marzulli Lucia, Matera Emilia, Parlatini Valeria, Petruzzelli Maria Giuseppina, Persico Antonio M, Delorme Richard, Fusar-Poli Paolo, Gosling Corentin J, Solmi Marco, Margari Lucia
Developmental EPI (Evidence synthesis, Prediction, Implementation) Lab, Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK; Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK; Hampshire and Isle of Wight NHS Foundation Trust, Southampton, UK; Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York City, NY, USA; DiMePRe-J-Department of Precision and Rigenerative Medicine-Jonic Area, University of Bari "Aldo Moro", Bari, Italy.
Developmental EPI (Evidence synthesis, Prediction, Implementation) Lab, Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK; Institute for Life Sciences, University of Southampton, Southampton, UK; Mind and Neurodevelopment (MiND) Interdisciplinary Cluster, University of Nottingham, Malaysia, University of Nottingham Malaysia, Semenyih, Malaysia.
Cell Rep Med. 2025 Feb 18;6(2):101916. doi: 10.1016/j.xcrm.2024.101916. Epub 2025 Jan 28.
The diagnosis of autism is currently based on the developmental history, direct observation of behavior, and reported symptoms, supplemented by rating scales/interviews/structured observational evaluations-which is influenced by the clinician's knowledge and experience-with no established diagnostic biomarkers. A growing body of research has been conducted over the past decades to improve diagnostic accuracy. Here, we provide an overview of the current diagnostic assessment process as well as of recent and ongoing developments to support diagnosis in terms of genetic evaluation, telemedicine, digital technologies, use of machine learning/artificial intelligence, and research on candidate diagnostic biomarkers. Genetic testing can meaningfully contribute to the assessment process, but caution is required when interpreting negative results, and more work is needed to strengthen the transferability of genetic information into clinical practice. Digital diagnostic and machine-learning-based analyses are emerging as promising approaches, but larger and more robust studies are needed. To date, there are no available diagnostic biomarkers. Moving forward, international collaborations may help develop multimodal datasets to identify biomarkers, ensure reproducibility, and support clinical translation.
目前，自闭症的诊断基于发育史、行为直接观察和所报告的症状，并辅以评定量表/访谈/结构化观察评估（这会受到临床医生的知识和经验影响），且尚无既定的诊断生物标志物。在过去几十年里，为提高诊断准确性开展了越来越多的研究。在此，我们概述了当前的诊断评估过程以及近期和正在进行的进展，这些进展涉及基因评估、远程医疗、数字技术、机器学习/人工智能的应用以及候选诊断生物标志物的研究，以支持自闭症诊断。基因检测能够对评估过程做出有意义的贡献，但在解读阴性结果时需要谨慎，并且需要开展更多工作以加强基因信息向临床实践的转化。基于数字诊断和机器学习的分析正成为有前景的方法，但需要开展规模更大、更稳健的研究。迄今为止，尚无可用的诊断生物标志物。展望未来，国际合作可能有助于开发多模态数据集以识别生物标志物、确保可重复性并支持临床转化。
