COVID19NCT05175690
Evaluation of the AudibleHealth Dx AI/ML-Based Dx SaMD Using FCV-SDS in the Diagnosis of COVID-19 Illness
An observational study of COVID19, run by AudibleHealth AI, Inc. at 1 site in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- COVID19
- What is being tested?
- Diagnostic Software as Medical Device
- How many people
- 1,126 participants
- Where
- 1 siteUnited States
- Study dates
- 10 Jan 2022to 3 May 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Adults seeking elective, outpatient COVID RT-PCR testing will be included.
Who may be able to join
- Male or Female, 18 years of age or older
- Present for elective, outpatient COVID-19 RT-PCR testing
- Meet the FDA EUA approved indications for use for the RT-PCR nasal swab test for COVID-19
- Stated willingness to comply with all trial procedures and availability for the duration of the trial
- Informed consent must be obtained prior to testing
Who cannot join
- Less than 18 years of age
- Unable to cough voluntarily
- Present with acute traumatic injury to the head, neck, throat, chest, abdomen or trunk
- Patent tracheostomy stoma
- Recent chest/abdomen/trunk trauma or surgery, recent/persistent neurovascular injury or recent intracranial surgery
- 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
- 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
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Evaluation of the Artificial Intelligence/Machine Learning-Based Diagnostic Software as a Medical Device Using Forced Cough Vocalization Signal Data Signatures in the Diagnosis of COVID-19 Illness
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.
Read the 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.
- Conditions
- COVID19
- Being tested
- Diagnostic Software as Medical Device
- Keywords
- 2019 Novel Coronavirus Disease, 2019 Novel Coronavirus Infection, 2019-nCoV Disease, 2019-nCoV Infection, COVID-19 Pandemic, COVID-19 Pandemics, COVID-19 Virus Disease, COVID-19 Virus Infection, Coronavirus Disease 2019, Coronavirus Disease-19, SARS Coronavirus 2 Infection, SARS-CoV-2 Infection, Severe Acute Respiratory Syndrome Coronavirus 2 Infection, SARS-CoV-2, Software as Medical Device, Diagnostic Software as Medical Device, SaMD, Dx SaMD, Forced Cough Vocalization, Signal Data Signature
- Medical subject headings
- COVID-19, Cardiomyopathy, Dilated, 1C
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Diagnostic Software as Medical DeviceDiagnostic test
Also called Dx SaMD, AudibleHealth Dx
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Diagnostic Software as Medical Device |
What is being measured
The primary outcome is the main question the study is designed to answer.
Non-inferiority of the positive percent agreement (PPA)
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.)
Measured: 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.)
Non-inferiority of the negative percent agreement (NPA)
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.)
Measured: 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.)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Jan 2022
- Study start10 Jan 2022
- Primary completion3 May 2022
- Study completion3 May 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (19)Papers the sponsor has linked to this study.
- Other trials for COVID19Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 1,126 participants (actual)
Sponsor and investigators
- Lead sponsor
- AudibleHealth AI, Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- University of South Florida, R. P. Chiacchierini Consulting, LLC, Analytic Solutions Group, Renaissance Worldwide Solutions, LLC, Medical & Regulatory Affairs Specialists, LLC
- Responsible party
- Sponsor
- Investigators
- Karl Kelley, MD, Principal investigator, RAIsonance, Inc.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05175690
- Sponsor's protocol ID
- Pro00057996
- Status last verified
- May 2022
- First submitted
- 22 Dec 2021
- Last updated
- 5 May 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
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.
PubMed 27654978Arevalo-Rodriguez I, Buitrago-Garcia D, Simancas-Racines D, Zambrano-Achig P, Del Campo R, Ciapponi A, Sued O, Martinez-Garcia 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.
PubMed 33301459Assandri R, Canetta C, Vigano 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.
PubMed 32774118Bahreini 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.
PubMed 33072432Chaimayo 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.
PubMed 33187528Chen 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. doi: 10.1002/sim.1688.
PubMed 15057876Imran 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.
PubMed 32839734Katz 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.
PubMed 32530131Khomsay, 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.
PubMed 25532164Krizhevsky, 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.
PubMed 32422057Laguarta 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.
PubMed 34812418Liu 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(Suppl 4):S2. doi: 10.1186/1472-6947-15-S4-S2. Epub 2015 Nov 25.
PubMed 26606168Mehta 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.
PubMed 22105690
Study at a glance
From the registry record, last updated by the sponsor on 5 May 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of the Artificial Intelligence/Machine Learning-Based Diagnostic Software as a Medical Device Using Forced Cough Vocalization Signal Data Signatures in the Diagnosis of COVID-19 Illness
- ClinicalTrials.gov ID
- NCT05175690
- Acronym / sponsor ID
- Pro00057996
- Conditions
- COVID19
- Keywords
- 2019 Novel Coronavirus Disease, 2019 Novel Coronavirus Infection, 2019-nCoV Disease, 2019-nCoV Infection, COVID-19 Pandemic, COVID-19 Pandemics, COVID-19 Virus Disease, COVID-19 Virus Infection
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,126 participants (actual)
- Lead sponsor
- AudibleHealth AI, Inc.Industry (pharma, biotech, devices)
- Collaborators
- University of South Florida, R. P. Chiacchierini Consulting, LLC, Analytic Solutions Group, Renaissance Worldwide Solutions, LLC, Medical & Regulatory Affairs Specialists, LLC
- Study start
- 10 Jan 2022 (actual)
- Primary completion
- 3 May 2022 (actual)
- Study completion
- 3 May 2022 (actual)
- First posted
- 4 Jan 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Trial Population | – | Diagnostic Test: Diagnostic Software as Medical DeviceThe 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Adults seeking elective, outpatient COVID RT-PCR testing will be included.
Key inclusion criteria (5 listed)
- Male or Female, 18 years of age or older
- Present for elective, outpatient COVID-19 RT-PCR testing
- Meet the FDA EUA approved indications for use for the RT-PCR nasal swab test for COVID-19
- Stated willingness to comply with all trial procedures and availability for the duration of the trial
- Informed consent must be obtained prior to testing
Key exclusion criteria (7 listed)
- Less than 18 years of age
- Unable to cough voluntarily
- Present with acute traumatic injury to the head, neck, throat, chest, abdomen or trunk
- Patent tracheostomy stoma
- Recent chest/abdomen/trunk trauma or surgery, recent/persistent neurovascular injury or recent intracranial surgery
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Diagnostic Software as Medical DeviceAlso: Dx SaMD, AudibleHealth DxAudibleHealth 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. | Diagnostic test | Trial Population |
Primary outcome
Non-inferiority of the positive percent agreement (PPA)
Time frame: 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)
Time frame: 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.)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 May 2022.
- Background: PubMed 27654978Amoh 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.
- Background: PubMed 33301459Arevalo-Rodriguez I, Buitrago-Garcia D, Simancas-Racines D, Zambrano-Achig P, Del Campo R, Ciapponi A, Sued O, Martinez-Garcia 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.
- Background: PubMed 32774118Assandri R, Canetta C, Vigano 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.
- Background: PubMed 33072432Bahreini 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.
- Background: PubMed 33187528Chaimayo 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.
- Background: PubMed 15057876Chen 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. doi: 10.1002/sim.1688.
- Background: PubMed 32839734Imran 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.
- Background: PubMed 32530131Katz 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.
- BackgroundKhomsay, 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)
- Background: PubMed 25532164Kosasih 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.
- BackgroundKrizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
- Background: PubMed 32422057Kucirka 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Karl Kelley, MD principal investigator
RAIsonance, Inc.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of COVID19, run by AudibleHealth AI, Inc. at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 5 May 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Non-inferiority of the positive percent agreement (PPA) (time frame: 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.)). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05175690, which also lists 19 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 May 2022; AzTrials last refreshed its copy on 8 Nov 2026.