Evaluation of the AudibleHealth Dx AI/ML-Based Dx SaMD Using FCV-SDS in the Diagnosis of COVID-19 Illness
Study Details
Study Description
Brief Summary
The AudibleHealth Dx is a diagnostic software as a medical device (Dx SaMD) consisting of an ensemble of software subroutines that interacts with a proprietary database of Signal Data Signatures (SDS), using Artificial Intelligence/Machine Learning (AI/ML) to analyze forced cough vocalization signal data signatures (FCV-SDS) for diagnostic purposes.
This study will evaluate the performance of the AudibleHealth Dx in comparison to a standard of care Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) test for the diagnosis of COVID-19. Bidirectional Sanger sequencing will be used to reduce the rate of false negative and false positive results.
A secondary purpose of the study will be usability testing of the device for participants and providers.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Detailed Description
The study is a prospective, multi-site, non-inferiority trial comparing the AudibleHealth Dx to Emergency Use Authorization (EUA) approved COVID-19 RT-PCR testing to demonstrate non-inferiority of the PPA and NPA when using this device to diagnose COVID-19 illness. The AudibleHealth Dx test, the "Xpert Xpress SARS-CoV-2 RT-PCR" (brand name) test, and bidirectional Sanger sequencing will be performed for each participant during a single encounter. Participants and staff will be blinded to AudibleHealth Dx results and the RT-PCR status at the time of testing. No one will know both results in real-time except for the Site Coordinators and unblinded statistician specifically authorized to have these results for enrollment, audit, data tracking, and data compiling purposes. Unblinding of the results will occur after the AudibleHealth Dx, RT-PCR, and variant sequencing results have been obtained. Results for the RT-PCR test will be received by the participant according to the clinical site's protocol. Variant sequencing results will be handled by each site according to their protocol.
Target enrollment for this trial will be 65 COVID-19 positive cases and 247 COVID-19 negative cases, presuming a prevalence of 0.17 for a total of 312 subjects meeting all inclusion criteria.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Trial Population The trial population will be enrolled from adults presenting for elective, outpatient COVID-19 testing at a single center, potentially with multiple testing locations (subject to local needs at the time of the trial). The investigational device will be provided to Participants via a cell phone preloaded with Common off-the-shelf original equipment manufacturer (COTS OEM) software and the investigational Dx SaMD. The investigational device will be evaluated during a single encounter in which an FCV-SDS will be collected. No follow-up visits or participant contacts will be involved in this trial. |
Diagnostic Test: Diagnostic Software as Medical Device
AudibleHealth Dx is an investigational Dx SaMD consisting of an ensemble of software subroutines that interacts with a proprietary database of signal data signatures (SDS) using Artificial Intelligence/Machine Learning (AI/ML) to analyze forced cough vocalization signal data signatures (FCV-SDS) for diagnostic purposes. The intended use for the AudibleHealth Dx AI/ML-based Dx SaMD using FCV-SDS is for the diagnosis of acute and chronic illnesses.
The AudibleHealth Dx is a cloud-based AI/ML (locked ML) diagnostic software as medical device (Dx SaMD) with a mobile app based graphical user interface (GUI) designed to operate with COTS Android Operating System (OS) and Apple OS based mobile devices. The AudibleHealth Dx system uses a forced cough vocalization (FCV) signal data signature (SDS) to diagnose COVID-19 illness in ambulatory adults. Results are sent to ordering physicians, State Health Departments, and participants using Health Level 7 (HL7) compliant communication protocols.
Other Names:
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Outcome Measures
Primary Outcome Measures
- Non-inferiority of the positive percent agreement (PPA) [Participants will have a single encounter lasting less than one hour; anticipated study duration is 6 weeks. Target enrollment is 65 positive and 247 negative participants. (Interim analysis will be conducted at the halfway point.)]
To demonstrate non-inferiority of the positive percent agreement (PPA) of the AudibleHealth Dx when compared to EUA approved COVID-19 RT-PCR testing (specifically the Xpert Xpress SARS-CoV-2 RT-PCR test for the diagnosis of COVID-19 illness.)
- Non-inferiority of the negative percent agreement (NPA) [Participants will have a single encounter lasting less than one hour; anticipated study duration is 6 weeks. Target enrollment is 65 positive and 247 negative participants. (Interim analysis will be conducted at the halfway point.)]
To demonstrate non-inferiority of the negative percent agreement (NPA) of the AudibleHealth Dx when compared to EUA approved COVID-19 RT-PCR testing (specifically the Xpert Xpress SARS-CoV-2 RT-PCR test for the diagnosis of COVID-19 illness.)
Eligibility Criteria
Criteria
Inclusion Criteria:
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Male or Female, 18 years of age or older
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Present for elective, outpatient COVID-19 RT-PCR testing
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Meet the FDA EUA approved indications for use for the RT-PCR nasal swab test for COVID-19
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Stated willingness to comply with all trial procedures and availability for the duration of the trial
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Informed consent must be obtained prior to testing
Exclusion Criteria:
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Less than 18 years of age
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Unable to cough voluntarily
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Present with acute traumatic injury to the head, neck, throat, chest, abdomen or trunk
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Patent tracheostomy stoma
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Recent chest/abdomen/trunk trauma or surgery, recent/persistent neurovascular injury or recent intracranial surgery
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Medical history of cribriform plate injury or cribriform plate surgery, diaphragmatic hernia, external beam neck/throat/maxillofacial radiation, phrenic nerve injury/palsy, radical neck/throat/maxillofacial surgery, vocal cord trauma or nodules
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Since persons with aphasia may have difficulty in producing an FCV-SDS in the time allotted by the app, this population also will be excluded from the current trial
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | University of South Florida | Tampa | Florida | United States | 33612 |
Sponsors and Collaborators
- AudibleHealth AI, Inc.
- University of South Florida
- R. P. Chiacchierini Consulting, LLC
- Analytical Solutions Group, Inc.
- Renaissance Worldwide Solutions, LLC
- Medical & Regulatory Affairs Specialists, LLC
Investigators
- Principal Investigator: Karl Kelley, MD, RAIsonance, Inc.
Study Documents (Full-Text)
None provided.More Information
Publications
- Amoh J, Odame K. Deep Neural Networks for Identifying Cough Sounds. IEEE Trans Biomed Circuits Syst. 2016 Oct;10(5):1003-1011. doi: 10.1109/TBCAS.2016.2598794. Epub 2016 Sep 16.
- Arevalo-Rodriguez I, Buitrago-Garcia D, Simancas-Racines D, Zambrano-Achig P, Del Campo R, Ciapponi A, Sued O, Martinez-García L, Rutjes AW, Low N, Bossuyt PM, Perez-Molina JA, Zamora J. False-negative results of initial RT-PCR assays for COVID-19: A systematic review. PLoS One. 2020 Dec 10;15(12):e0242958. doi: 10.1371/journal.pone.0242958. eCollection 2020.
- Assandri R, Canetta C, Viganò G, Buscarini E, Scartabellati A, Montanelli A. Laboratory markers included in the Corona Score can identify false negative results on COVID-19 RT-PCR in the emergency room. Biochem Med (Zagreb). 2020 Oct 15;30(3):030402. doi: 10.11613/BM.2020.030402. Epub 2020 Aug 5.
- Bahreini F, Najafi R, Amini R, Khazaei S, Bashirian S. Reducing False Negative PCR Test for COVID-19. Int J MCH AIDS. 2020;9(3):408-410. doi: 10.21106/ijma.421. Epub 2020 Oct 8.
- Chaimayo C, Kaewnaphan B, Tanlieng N, Athipanyasilp N, Sirijatuphat R, Chayakulkeeree M, Angkasekwinai N, Sutthent R, Puangpunngam N, Tharmviboonsri T, Pongraweewan O, Chuthapisith S, Sirivatanauksorn Y, Kantakamalakul W, Horthongkham N. Rapid SARS-CoV-2 antigen detection assay in comparison with real-time RT-PCR assay for laboratory diagnosis of COVID-19 in Thailand. Virol J. 2020 Nov 13;17(1):177. doi: 10.1186/s12985-020-01452-5.
- Chen YH, DeMets DL, Lan KK. Increasing the sample size when the unblinded interim result is promising. Stat Med. 2004 Apr 15;23(7):1023-38.
- Imran A, Posokhova I, Qureshi HN, Masood U, Riaz MS, Ali K, John CN, Hussain MI, Nabeel M. AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Inform Med Unlocked. 2020;20:100378. doi: 10.1016/j.imu.2020.100378. Epub 2020 Jun 26.
- Katz AP, Civantos FJ, Sargi Z, Leibowitz JM, Nicolli EA, Weed D, Moskovitz AE, Civantos AM, Andrews DM, Martinez O, Thomas GR. False-positive reverse transcriptase polymerase chain reaction screening for SARS-CoV-2 in the setting of urgent head and neck surgery and otolaryngologic emergencies during the pandemic: Clinical implications. Head Neck. 2020 Jul;42(7):1621-1628. doi: 10.1002/hed.26317. Epub 2020 Jun 12.
- Khomsay, S., Vanijjirattikhan, R., & Suwatthikul, J. (2019). Cough detection using PCA and Deep Learning. Paper presented at the 2019 International Conference on Information and Communication Technology Convergence (ICTC)
- Kosasih K, Abeyratne UR, Swarnkar V, Triasih R. Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis. IEEE Trans Biomed Eng. 2015 Apr;62(4):1185-94. doi: 10.1109/TBME.2014.2381214. Epub 2014 Dec 18.
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
- Kucirka LM, Lauer SA, Laeyendecker O, Boon D, Lessler J. Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction-Based SARS-CoV-2 Tests by Time Since Exposure. Ann Intern Med. 2020 Aug 18;173(4):262-267. doi: 10.7326/M20-1495. Epub 2020 May 13. Review.
- Laguarta J, Hueto F, Subirana B. COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings. IEEE Open J Eng Med Biol. 2020 Sep 29;1:275-281. doi: 10.1109/OJEMB.2020.3026928. eCollection 2020.
- Liu JM, You M, Wang Z, Li GZ, Xu X, Qiu Z. Cough event classification by pretrained deep neural network. BMC Med Inform Decis Mak. 2015;15 Suppl 4:S2. doi: 10.1186/1472-6947-15-S4-S2. Epub 2015 Nov 25.
- Mehta CR, Pocock SJ. Adaptive increase in sample size when interim results are promising: a practical guide with examples. Stat Med. 2011 Dec 10;30(28):3267-84. doi: 10.1002/sim.4102. Epub 2010 Nov 30.
- Moore NM, Li H, Schejbal D, Lindsley J, Hayden MK. Comparison of Two Commercial Molecular Tests and a Laboratory-Developed Modification of the CDC 2019-nCoV Reverse Transcriptase PCR Assay for the Detection of SARS-CoV-2. J Clin Microbiol. 2020 Jul 23;58(8). pii: e00938-20. doi: 10.1128/JCM.00938-20. Print 2020 Jul 23.
- Nemati E, Rahman MM, Nathan V, Vatanparvar K, Kuang J. A Comprehensive Approach for Classification of the Cough Type. Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:208-212. doi: 10.1109/EMBC44109.2020.9175345.
- Sharan RV, Abeyratne UR, Swarnkar VR, Porter P. Automatic Croup Diagnosis Using Cough Sound Recognition. IEEE Trans Biomed Eng. 2019 Feb;66(2):485-495. doi: 10.1109/TBME.2018.2849502. Epub 2018 Jun 21. Erratum in: IEEE Trans Biomed Eng. 2019 May;66(5):1491.
- Yu F, Yan L, Wang N, Yang S, Wang L, Tang Y, Gao G, Wang S, Ma C, Xie R, Wang F, Tan C, Zhu L, Guo Y, Zhang F. Quantitative Detection and Viral Load Analysis of SARS-CoV-2 in Infected Patients. Clin Infect Dis. 2020 Jul 28;71(15):793-798. doi: 10.1093/cid/ciaa345.
- Pro00057996