Acoustic Epidemiology of Pulmonary Tuberculosis (TB) & Covid19 Leveraging explainable AI/ML
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Abstract
Involuntary cough is a prominent symptom for many a Lung Ailments ranging from Infectious to non-Infectious diseases. Early research around human cough established that the spectral signatures do not vary between Involuntary and Voluntary coughs. The study aimed at evaluating voluntary human cough sounds recorded under a stringent clinical protocol. India’s ambitious goal to eliminate and eradicate TB by 2025 shall be facilitated by Machine Learning tools that address subjectivity in that the healthcare worker can now take the solution as a screening modality to the last mile as a part of outreach programs without having to rely on infrastructure & connectivity. In this paper we present the findings of Clinical Trials for Pulmonary TB registered at CTRI/2019/02/017672 conducted independently and included Covid19 during the pandemic as a part of Bi-Directional screening modality. The reference standards used were CBNAAT (Cartridge based nucleic acid amplification test) & CXR (Chest X-Ray) for TB while for Covid19; RT-PCR was used as the reference standard. As a non-invasive and contactless screening modality, a sophisticated third-party Microphone Array was used to record the cough under a stringent infection control protocol. Sensitivity achieved across the sites for TB ranged between 80% - 83% and Specificity was to the tune of 92% while using CBNAAT as a reference standard. CXR when used as a reference standard for TB achieved a sensitivity and specificity of 59% and 60% respectively. Covid19 achieved a sensitivity & specificity of 92% and 96% while using RT-PCR as the reference standard. The study was primarily focused on the Frequency domain that paved way for feature extraction and explainable Machine Learning Models operating upon lossless WAV files hypothesizing acoustic theory and demographic inputs. The solution titled “TimBre” can now be added to the healthcare workers arsenal in situations where a RT-PCR or CXR is not available and seamlessly conduct bidirectional screening with a single recording of cough and also offer insights into Non-Communicable diseases as a part of differential diagnosis
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SciScore for 10.1101/2022.02.05.22269707: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: Informed consent was obtained via a traditional method to obtain signatures on paper available in English, Kannada and Bengali languages for Site2 that was conducted at NH, Bangalore while subsequent sites leveraged the digital screen on the TimBre app that is available as a private APK as of today. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources The app is not available on Google Playstore given the fact that without a Microphone Array, the results could get compromised and cause unnecessary anxiety among users. Google Playstoresuggested: NoneThe TimBre app … SciScore for 10.1101/2022.02.05.22269707: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: Informed consent was obtained via a traditional method to obtain signatures on paper available in English, Kannada and Bengali languages for Site2 that was conducted at NH, Bangalore while subsequent sites leveraged the digital screen on the TimBre app that is available as a private APK as of today. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources The app is not available on Google Playstore given the fact that without a Microphone Array, the results could get compromised and cause unnecessary anxiety among users. Google Playstoresuggested: NoneThe TimBre app would facilitate data transfer of the WAV file and demographics to the cloud (Azure, IBM, GCP, and AWS) for further processing that included the following order: Spectrograms were not considered due to the fact that the MFCC (mel frequency cepestral coefficients) components were already included as a part of feature extraction & the sheer volume of data required for Deep Learning models was not available with an added risk of misrepresentation of data when augmented using tools & techniques such as VTLP – vocal tract length perturbation or MATLAB augmentation techniques. MATLABsuggested: (MATLAB, RRID:SCR_001622)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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