COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings.
COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings.
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DOI：
10.1109/ojemb.2020.3026928
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发表时间：
2020
影响因子：
5.8
通讯作者：
Subirana B
中科院分区：
文献类型：
--
作者：
Laguarta J;Hueto F;Subirana B
目标：我们假设新冠肺炎受试者，尤其是无症状者，只有使用人工智能从强迫咳嗽的手机录音中才能准确区分。为了训练我们的麻省理工学院开放语音模型，我们在2020年4月至5月通过我们的网站(Opensigma.mit.edu)建立了新冠肺炎咳嗽录音的数据收集渠道，并创建了迄今为止报告的最大音频新冠肺炎咳嗽平衡数据集，有5,320名受试者。方法：我们开发了一个人工智能语音处理框架，利用声学生物标记物特征提取程序从咳嗽录音中预筛选新冠肺炎，并提供个性化的患者显著地图，以实时、非侵入性的、基本上为零的可变成本对患者进行纵向监测。咳嗽记录经Mel频率倒谱系数变换后，输入到由1个泊松生物标志层和3个预先训练好的ResNet50‘S并行构成的卷积神经网络结构中，输出二值预筛选诊断。我们基于CNN的模型已经在4256名受试者上进行了训练，并在我们数据集的其余1064名受试者上进行了测试。迁移学习被用于在更大的数据集上学习生物标记物特征，这之前在我们的阿尔茨海默氏症实验室中进行了成功的测试，这显著提高了我们架构的新冠肺炎识别精度。结果：当通过官方测试对诊断对象进行验证时，该模型对新冠肺炎的敏感度为98.5%，特异度为94.2%(AuC：0.97)。对于无症状的受试者，其敏感性为100%，特异性为83.2%。结论：人工智能技术可以产生一种免费、无创、实时、随时、可即时分发的大规模新冠肺炎无症状筛查工具，以补充现有的方法来遏制新冠肺炎的传播。实际使用案例可以是在学校、工作和交通重新开放时对学生、工人和公众进行日常筛查，或者用于池测试，以快速警告群体暴发。可能存在覆盖几种疾病类别的通用语音生物标记物，正如我们在新冠肺炎和阿尔茨海默氏症中使用相同的语言生物标记物所展示的那样。
Goal: We hypothesized that COVID-19 subjects, especially including asymptomatics, could be accurately discriminated only from a forced-cough cell phone recording using Artificial Intelligence. To train our MIT Open Voice model we built a data collection pipeline of COVID-19 cough recordings through our website (opensigma.mit.edu) between April and May 2020 and created the largest audio COVID-19 cough balanced dataset reported to date with 5,320 subjects. Methods: We developed an AI speech processing framework that leverages acoustic biomarker feature extractors to pre-screen for COVID-19 from cough recordings, and provide a personalized patient saliency map to longitudinally monitor patients in real-time, non-invasively, and at essentially zero variable cost. Cough recordings are transformed with Mel Frequency Cepstral Coefficient and inputted into a Convolutional Neural Network (CNN) based architecture made up of one Poisson biomarker layer and 3 pre-trained ResNet50's in parallel, outputting a binary pre-screening diagnostic. Our CNN-based models have been trained on 4256 subjects and tested on the remaining 1064 subjects of our dataset. Transfer learning was used to learn biomarker features on larger datasets, previously successfully tested in our Lab on Alzheimer's, which significantly improves the COVID-19 discrimination accuracy of our architecture. Results: When validated with subjects diagnosed using an official test, the model achieves COVID-19 sensitivity of 98.5% with a specificity of 94.2% (AUC: 0.97). For asymptomatic subjects it achieves sensitivity of 100% with a specificity of 83.2%. Conclusions: AI techniques can produce a free, non-invasive, real-time, any-time, instantly distributable, large-scale COVID-19 asymptomatic screening tool to augment current approaches in containing the spread of COVID-19. Practical use cases could be for daily screening of students, workers, and public as schools, jobs, and transport reopen, or for pool testing to quickly alert of outbreaks in groups. General speech biomarkers may exist that cover several disease categories, as we demonstrated using the same ones for COVID-19 and Alzheimer's.