COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings

Abstract

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, noninvasive, real-time, any-time, instantly distributable, largescale 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.

 

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Goal

- 특히 무증상을 포함한 COVID-19 피험자는 AI를 사용한 강제 기침 휴대 전화 녹음에서만 정확하게 구별할 수 있다는 가설을 세웠다. 

- MIT Open Voice model을 훈련시키기 위해 2020년 4월과 5월 사이에 웹사이트를 통해 COVID-19 기침 녹음 데이터 수집 파이프라인을 구축하고 5,320명의 피험자로 보고된 최대 오디오 COVID-19 기침 balanced dataset을 만들었다.

 

Methods

- 음향 바이오마커 feature extractor를 활용하여 기침 기록에서 COVID-19를 사전 선별하고, 실시간, 비침습적, 기본적으로 가변 비용 없이 AI 음성처리 프레임워크 feature를 개발했다. 기침 기록은 Mel Frequency Cepstral Coefficient를 사용하여 변환되고 1개의 포아송 바이오마커 layer와 3개의 pre-train 된 ResNet50으로 구성된 CNN 기반의 아키텍처에 병렬로 입력되어 binary 사전 선별 진단이 출력된다. 우리의 CNN 기반 모델은 4256명의 피험자에 대해 훈련되었으며 dataset의 남아있는 1064명의 피험자에 대해 테스트되었다. Transfer learning은 이전에 알츠하이머 연구소에서 성공적으로 테스트된 대규모 dataset의 바이오마커 특징을 학습하는데 사용되어 아키텍쳐의 COVID-19 식별 정확도를 크게 향상시켰습니다.