用于自闭症诊断的可解释人工智能：使用fMRI数据识别关键脑区
Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions Using fMRI Data
- University of Plymouth（普利茅斯大学）
- Cornwall Partnership NHS Foundation Trust（康沃尔合作国民保健信托基金会）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对ASD诊断缺乏客观生物标志物及现有深度学习模型缺乏可解释性的问题，本研究基于ABIDE数据集构建可解释深度学习模型，不仅实现了ASD的准确分类，还成功识别出ASD与典型对照组差异的关键脑区，推动了医学影像可解释AI的发展。
AI中文摘要：
孤独症谱系障碍（ASD）的早期诊断和干预已被证明能显著改善孤独症患者的生活质量。然而，ASD的诊断方法依赖于基于临床表现的评估，这种评估容易产生偏差，且难以实现早期诊断。因此需要客观的ASD生物标志物来帮助提高诊断准确性。深度学习（DL）在从医学影像数据中诊断疾病和健康状况方面取得了出色的表现。关于创建使用静息态功能磁共振成像（fMRI）数据对ASD进行分类的模型，已经进行了广泛的研究。然而，现有模型缺乏可解释性。本研究旨在通过创建一个DL模型来提高ASD诊断的准确性和可解释性，该模型不仅能准确分类ASD，还能提供其工作机制的可解释见解。使用的数据集是Autism Brain Imaging Data Exchange（ABIDE）的预处理版本，包含884个样本。我们的研究结果表明，该模型能够准确分类ASD，并突出了ASD与典型对照组之间存在差异的关键脑区，这对ASD的早期诊断和神经基础理解具有潜在意义。这些发现得到了文献中使用不同数据集和模态的研究的验证，证实该模型确实学习了ASD的特征，而不仅仅是记住了数据集。本研究通过提供一个稳健且可解释的模型，推进了医学影像领域的可解释AI发展，从而为未来实现客观可靠的ASD诊断做出了贡献。
英文摘要：
Early diagnosis and intervention for Autism Spectrum Disorder (ASD) has been shown to significantly improve the quality of life of autistic individuals. However, diagnostics methods for ASD rely on assessments based on clinical presentation that are prone to bias and can be challenging to arrive at an early diagnosis. There is a need for objective biomarkers of ASD which can help improve diagnostic accuracy. Deep learning (DL) has achieved outstanding performance in diagnosing diseases and conditions from medical imaging data. Extensive research has been conducted on creating models that classify ASD using resting-state functional Magnetic Resonance Imaging (fMRI) data. However, existing models lack interpretability. This research aims to improve the accuracy and interpretability of ASD diagnosis by creating a DL model that can not only accurately classify ASD but also provide explainable insights into its working. The dataset used is a preprocessed version of the Autism Brain Imaging Data Exchange (ABIDE) with 884 samples. Our findings show a model that can accurately classify ASD and highlight critical brain regions differing between ASD and typical controls, with potential implications for early diagnosis and understanding of the neural basis of ASD. These findings are validated by studies in the literature that use different datasets and modalities, confirming that the model actually learned characteristics of ASD and not just the dataset. This study advances the field of explainable AI in medical imaging by providing a robust and interpretable model, thereby contributing to a future with objective and reliable ASD diagnostics.
