Runde Benjamin S, Alapati Ajit, Bazan Nicolas G
Science Engineering Research Center, The Potomac School.
Neuroscience Center of Excellence, School of Medicine, Louisiana State University.
medRxiv. 2024 Jan 16:2024.01.14.24301297. doi: 10.1101/2024.01.14.24301297.
As the impact of Alzheimer's disease (AD) is projected to grow in the coming decades as the world's population ages, the development of noninvasive and cost-effective methods of detecting AD is essential for the early prevention and mitigation of the progressive disease, alleviating its expected global impact. This study analyzes audio processing techniques and transcription methodologies to optimize the detection of AD through the natural language processing (NLP) of spontaneous speech. We enhanced audio fidelity using Boll Spectral Subtraction and evaluated the transcription accuracy of state-of-the-art AI services-locally-based Wav2Vec and Whisper, alongside cloud-based IBM Cloud and Rev AI-against traditional manual transcription methods. The choice between local and cloud-based solutions hinges on a trade-off between privacy, ongoing costs, and computational requirements. Leveraging OpenAI's GPT for word embeddings, we enhanced the training of Support Vector Machine (SVM) classifiers, which were crucial in analyzing transcripts and refining detection accuracy. Our findings reveal that AI-driven transcriptions significantly outperform manual counterparts when classifying AD and Control samples, with Wav2Vec using enhanced audio exhibiting the highest accuracy and F-1 scores (0.99 for both metrics) for locally based systems and Rev AI using unenhanced audio leading cloud-based methods with comparable precision (0.96 for both metrics). The study also uncovers the detrimental effect of including interviewer speech in recordings on model performance, advocating for the exclusion of such interactions to improve data quality for AD classification algorithms. Our comprehensive evaluation demonstrates that AI transcription (both Cloud and Local) and NLP technologies in their current forms can classify AD, as well as probable AD and mild cognitive impairment (MCI), a prodromal stage of AD, accurately but suffer from a lack of available training data. The insights garnered from this research lay the groundwork for future advancements in the noninvasive monitoring and early detection of cognitive impairments through linguistic analysis.
随着全球人口老龄化，预计在未来几十年阿尔茨海默病（AD）的影响将不断扩大，因此开发无创且经济高效的AD检测方法对于早期预防和缓解这种渐进性疾病、减轻其预期的全球影响至关重要。本研究分析了音频处理技术和转录方法，以通过对自发语音的自然语言处理（NLP）来优化AD的检测。我们使用博尔谱减法增强了音频保真度，并将基于本地的Wav2Vec和Whisper以及基于云的IBM Cloud和Rev AI等最先进的人工智能服务的转录准确性与传统手动转录方法进行了评估。本地和基于云的解决方案之间的选择取决于隐私、持续成本和计算要求之间的权衡。利用OpenAI的GPT进行词嵌入，我们增强了支持向量机（SVM）分类器的训练，这对于分析转录本和提高检测准确性至关重要。我们的研究结果表明，在对AD和对照样本进行分类时，人工智能驱动的转录明显优于手动转录，对于基于本地的系统，使用增强音频的Wav2Vec表现出最高的准确性和F-1分数（两个指标均为0.99），而对于基于云的方法，使用未增强音频的Rev AI以相当的精度领先（两个指标均为0.96）。该研究还揭示了在录音中包含采访者语音对模型性能的不利影响，主张排除此类交互以提高AD分类算法的数据质量。我们的综合评估表明，当前形式的人工智能转录（包括基于云和本地的）和NLP技术可以准确地对AD以及可能的AD和轻度认知障碍（MCI，AD的前驱阶段）进行分类，但缺乏可用的训练数据。这项研究获得的见解为未来通过语言分析对认知障碍进行无创监测和早期检测的进展奠定了基础。
