Evaluation of the AudibleHealth Dx AI/ML-Based Dx SaMD Using FCV-SDS in the Diagnosis of COVID-19 Illness: Clinical Validation

SARS-CoV-2 Infection Clinical Trial

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: A Prospective, Two-Arm Non-Inferiority Clinical Validation Trial of AudibleHealth Dx Software as a Medical Device (EUA-US)

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05364268
• Other study ID #: Pro00061778
• Status: Completed
• Start date: May 4, 2022
• Est. completion date: June 1, 2022

Study information

• Verified date: July 2022
• Source: AudibleHealth AI, Inc.
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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 (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.

A secondary purpose of the study will be usability testing of the device for participants and providers.

Description:

The study is a prospective, multi-site, non-inferiority trial comparing the AudibleHealth Dx to FDA 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 and the "BioFire Respiratory 2.1 (RP2.1)" (brand name) test 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 the second RT-PCR results (if necessary for discordance) have been obtained. Results for the RT-PCR test will be received by the participant according to the clinical site's protocol.

Target enrollment for this trial will be 65 COVID-19 positive cases and 152 COVID-19 negative cases, presuming a prevalence of 0.30 for a total of 217 subjects meeting all inclusion criteria.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 514
• Est. completion date: June 1, 2022
• Est. primary completion date: June 1, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 100 Years

Eligibility

Inclusion Criteria:

• 18 years of age or older

• Present for elective, outpatient COVID-19 RT-PCR testing

• Meet the FDA EUA approved indications for use for RT-PCR nasal swab testing 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

• Ability to complete both the informed consent form and the screens on the medical device app in English (no translation to other languages is currently available)

Exclusion Criteria:

• Any individual who was a part of the AudibleHealth Dx Development, Training, and Usability trial (Training and test data sets are to be kept strictly separate.)

• Less than 18 years of age

• Unable to produce a voluntary forced cough vocalization (FCV)

• Recent 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

Related Conditions & MeSH terms

• 2019 Novel Coronavirus Infection
• Communicable Diseases
• Coronavirus Disease 2019
• Coronavirus Infections
• COVID-19
• COVID-19 Pandemic
• COVID-19 Virus Disease
• COVID-19 Virus Infection
• Infections
• SARS-CoV-2 Infection
• Virus Diseases

Intervention

Device:
• 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, specifically COVID-19 illness for this study.

Locations

Country	Name	City	State
United States	Sunrise Research Institute	Sunrise	Florida

Sponsors (5)

Lead Sponsor	Collaborator
AudibleHealth AI, Inc.	Analytical Solutions Group, Inc., Kelley Medical Consultants LLC, R. P. Chiacchierini Consulting, LLC, Sunrise Research Institute

Country where clinical trial is conducted

United States, 

References & Publications (19)

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

* Note: There are 19 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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 FDA approved SARS CoV-2 RT-PCR testing for the diagnosis of COVID-19 illness	Participants will have a single encounter lasting less than one hour; anticipated study duration is 6 weeks. Target enrollment is 65 positive and 152 negative participants.
Primary	Non-inferiority of the negative percent agreement (NPA)	2. To demonstrate non-inferiority of the negative percent agreement (NPA) of the AudibleHealth Dx when compared to FDA approved SARS-CoV-2 RT-PCR testing for the diagnosis of COVID-19 illness.	Participants will have a single encounter lasting less than one hour; anticipated study duration is 6 weeks. Target enrollment is 65 positive and 152 negative participants.