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Assessing the accuracy of an automated atrial fibrillation detection algorithm using smartphone technology: The iREAD Study
Abstract
Background The Kardia Mobile Cardiac Monitor (KMCM) detects atrial fibrillation (AF) via a handheld cardiac rhythm recorder and AF detection algorithm. The algorithm operates within predefined parameters to provide a "normal" or "possible atrial fibrillation detected" interpretation; outside of these parameters, an "unclassified" rhythm is reported. The system has been increasingly used, but its performance has not been independently tested.Objective The objective of this study was to evaluate whether the KMCM system can accurately detect AF.Methods A single-center, adjudicator-blinded case series of 52 consecutive patients with AF admitted for antiarrhythmic drug initiation were enrolled. Serial 12-lead electrocardiograms (ECGs) and nearly simultaneously acquired KMCM recordings were obtained.Results There were 225 nearly simultaneously acquired KMCM and ECG recordings across 52 enrolled patients (mean age 68 years; 67% male). After exclusion of unclassified recordings, the KMCM automated algorithm interpretation had 96.6% sensitivity and 94.1% specificity for AF detection as compared with physician-interpreted ECGs, with a κ coefficient of 0.89. Physician-interpreted KMCM recordings had 100% sensitivity and 89.2% specificity for AF detection as compared with physician-interpreted ECGs, with a κ coefficient of 0.85. Sixty-two recordings (27.6%) were unclassified by the KMCM algorithm. In these instances, physician interpretation of KMCM recordings had 100% sensitivity and 79.5% specificity for AF detection as compared with 12-lead ECG interpretation, with a κ coefficient of 0.71.Conclusion The KMCM system provides sensitive and specific AF detection relative to 12-lead ECGs when an automated interpretation is provided. Direct physician review of KMCM recordings can enhance diagnostic yield, especially for unclassified recordings.
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“the automatic diagnostic algorithm has excellent sensitivity (96.6%) and specificity (94%) for correctly interpreting AF versus normal sinus rhythm [15].”
From Multi-centre Randomised Controlled Trial of a Smartphone-based Event Recorder Alongside Standard Care Versus Standard Care for Patients Presenting to the Emergency Department with Palpitations and Pre-syncope: The IPED (Investigation of Palpitations in the ED) study (2019) · cites this paper as [15]
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“As previously stated, false-negative rates in the diagnosis of AF are negligible.(16) Smartwatches also have PPG sensors incorporated in their case, on the side that is in contact with the wrist.”
Ding, E., Svennberg, E., Wurster, C. J., Duncker, D., Manninger, M., Lubitz, S. A., Dickson, E. L., Fitzgibbons, T. P., AKOUM, N. W., Al‐Khatib, S. M., et al. (2020) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2020.06.002 · Cited by 43
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“1%).(3,15,21) Differentiation of sensitivity values between various studies may result from including or excluding “unreadable” and “unclassified” recordings.”
BackgroundDigital Health and the Care of the Patient With Arrhythmia
Tarakji, K. G., Silva, J. N. A., Chen, L. Y., Turakhia, M. P., Perez, M. V., Attia, Z. I., PASSMAN, R. S., Boissy, A. R., Cho, D. J., Majmudar, M. D., et al. (2020) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.120.007953 · Cited by 35
“15 Even when the results are accurate for diagnosing AF, the management should be tailored and cannot be universal.”
BackgroundThe role of timing in treatment of atrial fibrillation: An AFFIRM substudy
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“regarded atrial flutter as the same disease state as AF, viewing the incident as a positive AF result [16,19,20,24,26-29].”
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“This also corroborates earlier studies using handheld devices; these have reported AF-detection sensitivities ranging from 77% to 100% and specificities from 80% to 96% when interpreted by the physicians and sensitivities of 55%–100% and specificities of 84%–98% when the interpretation was performed using automated al…”
ResultCHARGE-AF: A Useful Score For Atrial Fibrillation Prediction?
Goudis, C. A., Daios, S., Dimitriadis, F., & Liu, T. (2022) · Current Cardiology Reviews · doi:10.2174/1573403x18666220901102557 · Cited by 28
“Kardia Mobile Cardiac Monitor (KMCM) utilizes AF detection algorithms and a handheld cardiac rhythm recorder to detect AF [22].”
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State of the art of mobile health technologies use in clinical arrhythmia care
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The Digital Reconstruction of Health Care
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Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review
Zepeda-Echavarria, A., van de Leur, R. R., van Sleuwen, M., Hassink, R. J., Wildbergh, T. X., Doevendans, P. A., Jaspers, J., & van Es, R. (2023) · JMIR Cardio · doi:10.2196/44003 · Cited by 24
“Of the 36 articles, 24 (66%) evaluated the devices’ capabilities to diagnose rhythm disorders [15-17,21,24,25,29-35, 37-43,49-56], whereas 7 (20%) reported on capabilities to detect rhythm disorders and other heart conditions, such as cardiomyopathy, conduction disorders, and cardiac ischemia [18,22,23,28,36,45,57].”
BackgroundComparison of Apple Watch vs KardiaMobile: A Tale of Two Devices
Lee, C., Lee, C., Fernando, C., & Chow, C.-M. (2022) · CJC Open · doi:10.1016/j.cjco.2022.07.011 · Cited by 20
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“The sensitivity of the detection of atrial fibrillation ranges from 54.5% to 100%, with most studies calculating values of >87% [36,37].”
BackgroundInfluentialUse of virtual visits for the care of the arrhythmia patient
Hu, P. T., Hilow, H., Patel, D. R., Eppich, M., Cantillon, D. J., TCHOU, P. J., Bhargava, M., Kanj, M. H., Baranowski, B. J., Hussein, A. A., et al. (2020) · Heart Rhythm · doi:10.1016/j.hrthm.2020.05.011 · Cited by 19
Technology and cardiovascular diseases in the era of COVID‐19
Harky, A., Adan, A., Mohamed, M., Elmi, A. A., & Theologou, T. (2020) · Journal of Cardiac Surgery · doi:10.1111/jocs.15096 · Cited by 18
“A study which assessed the accuracy of smartphone technology in detecting atrial fibrillation (AF) with physician interpretation, using a kardia mobile cardiac monitor, found a 96.6% sensitivity and 94.1% specificity for AF detection compared with physician interpreted ECGs.24 Due to the nature of AF meaning that it c…”
BackgroundInfluentialReliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions
Jacobsen, M., Dembek, T. A., Ziakos, A. P., Gholamipoor, R., Kobbe, G., Kollmann, M., Blum, C. F., Müller‐Wieland, D., Napp, A., Heinemann, L., et al. (2020) · Sensors · doi:10.3390/s20195517 · Cited by 18
“[19,20] From the data obtained, the percentage of ‘good quality data’ was assessed by aggregating the time periods during which data were available that enabled an automatic IBI analysis.”
MethodReed, M. J., Grubb, N. R., Lang, C. C., O’Brien, R., Simpson, K., Padarenga, M., Grant, A., & Tuck, S. (2018) · Trials · doi:10.1186/s13063-018-3098-1 · Cited by 17
A Review on Atrial Fibrillation Detection From Ambulatory ECG
Ma, C., Xiao, Z., Zhao, L., Biton, S. B., Behar, J. A., Long, X., Vullings, R., Aarts, R. M., Li, J., & Liu, C. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3321792 · Cited by 20
“William et al. [125] monitored 233 patients using the AliveCor Kardia Mobile (KM) and its algorithm.”
MethodJCS / JHRS 2022 Guideline on Diagnosis and Risk Assessment of Arrhythmia
Takase, B., Ikeda, T., Shimizu, W., Abe, H., Aiba, T., Chinushi, M., Koba, S., KUSANO, K. F., Niwano, S., Takahashi, N., et al. (2024) · Journal of Arrhythmia · doi:10.1002/joa3.13052 · Cited by 19
Patel, D. R., Hu, P. T., Hilow, H., Lambert, C. T., Moufawad, M., Poe, S. M., Hussein, A. A., Baranowski, B. J., Bhargava, M., Rickard, J., et al. (2019) · Pacing and Clinical Electrophysiology · doi:10.1111/pace.13850 · Cited by 16
Garikapati, K., Turnbull, S., Bennett, R. G., Campbell, T. G., Kanawati, J., Wong, M. S., Thomas, S. P., Chow, C. K., & Kumar, S. (2022) · Heart Lung and Circulation · doi:10.1016/j.hlc.2022.08.001 · Cited by 18
Senoo, K., Miki, T., Okura, T., Shiraishi, H., Shirayama, T., Inoue, K., Sakatani, T., Kakita, K., Hattori, T., Nakai, K., et al. (2020) · Circulation Reports · doi:10.1253/circrep.cr-20-0032 · Cited by 16
Wearable Technology in Healthcare
Krey, M., Schlatter, U., Mahadevan, D. A., Derungs, K., & Oehler, J. K. (2020) · Journal of Advances in Information Technology · doi:10.12720/jait.11.3.172-180 · Cited by 16
“1% was measured compared to a 12-lead electrocardiogram [32].”
MethodA comparison of ECG-based home monitoring devices in adults with CHD
Pengel, L. K. D., Robbers‐Visser, D., Groenink, M., Winter, M. M., Schuuring, M. J., Bouma, B. J., & Bokma, J. P. (2022) · Cardiology in the Young · doi:10.1017/s1047951122002244 · Cited by 15
Wegner, F. K., Kochhäuser, S., Frommeyer, G., Lange, P. S., Ellermann, C., Leitz, P. R., Müller, P., Köbe, J., Eckardt, L., & Dechering, D. G. (2021) · Clinical Research in Cardiology · doi:10.1007/s00392-021-01856-5 · Cited by 15
Pitman, B. M., Chew, S.-H., Wong, C. X. J., Jaghoori, A., Iwai, S., Thomas, G., Chew, A., Sanders, P., & Lau, D. H. (2021) · JMIR mhealth and uhealth · doi:10.2196/24470 · Cited by 14
“Similarly, others have reported the KardiaMobile automated algorithm correctly detected AF with 93% sensitivity and 84% specificity in 100 participants with a history of AF who presented for a scheduled elective electrical cardioversion after excluding a substantial 34% of recordings with unclassified tracings [11].”
Enhancing Healthcare Access–Smartphone Apps in Arrhythmia Screening: Viewpoint
Książczyk, M., Dębska-Kozłowska, A., Warchoł, I., & Lubiński, A. (2021) · JMIR mhealth and uhealth · doi:10.2196/23425 · Cited by 14
Management of patients with arterial hypertension and atrial fibrillation
Chazova, I. E., Голицын, С. П., Zhernakova, Y. V., Zheleznova, E. A., Станиславовна, К. Е., Миронов, Н. Ю., Kostyukevich, M. V., Laiovich, L. I., Utsumueva, M. D., Александровна, Ю. Ю., et al. (2021) · Systemic Hypertension · doi:10.26442/2075082x.2021.3.201077 · Cited by 13
Atrial fibrillation: an update on management
McCallum, C., Raja, D. C., & Pathak, R. K. (2019) · Australian Prescriber · doi:10.18773/austprescr.2019.067 · Cited by 13
Moldt, J.-A., Wietek, T. F., Fuhl, W., Zabel, S., Claassen, M., Wagner, S., Nieselt, K., & Herrmann‐Werner, A. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-096208 · Cited by 12
Rosenfeld, L. E., Amin, A., Hsu, J. C., Oxner, A. Z., Hills, M. T., & Frankel, D. S. (2019) · Heart Rhythm · doi:10.1016/j.hrthm.2019.04.007 · Cited by 12
LUCIA, R. D., Giannini, C., Parollo, M., Barletta, V., Costa, G., Santoro, M. G., Primerano, C., Angelillis, M., Carlo, M. D., Zucchelli, G., et al. (2023) · EP Europace · doi:10.1093/europace/euac285 · Cited by 11
“6% and a specificity of 94% for AF detection, and, if compared with other MCT devices, it has a lower cost, could be easily reused, has a good battery autonomy, and is not burdened with the durability of the adhesives or the need of frequent electrode changes.(23) Finally, peculiar ‘digital’ findings of our novel AeEC…”
BackgroundA Morphology-Preserving Algorithm for Denoising of EMG-Contaminated ECG Signals
Atanasoski, V. A., Petrovic, J. S., Popović‐Maneski, L., Miletić, M., Babić, M., Nikolić, A., Panescu, D., & Ivanović, M. D. (2024) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2024.3380352 · Cited by 11
“However, the underlying physiology of the EMG noise, resulting from the engagement of fingers during measurement by handheld devices [1], [3], does not allow for an obvious mitigation strategy and must be eliminated in postprocessing.”
BackgroundMethodTonko, J., & Wright, M. J. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10173922 · Cited by 12
“However, overall diagnostic accuracy and performance of automated algorithms appear to be acceptable [13].”
BackgroundNath, L. C., Forbes, G., Elliott, A. D., Tomren, V., Ryan, A., & Franklin, S. H. (2021) · Australian Veterinary Journal · doi:10.1111/avj.13137 · Cited by 11
Sattar, Y., Song, D. D., Sarvepalli, D., Zaidi, S. R., Ullah, W., Arshad, J., Mir, T., Zghouzi, M., Elgendy, I. Y., Qureshi, W., et al. (2021) · Journal of Interventional Cardiac Electrophysiology · doi:10.1007/s10840-021-01068-x · Cited by 11
“[42] found very high accuracy in the form of sensitivity and specificity.”
BackgroundPitman, B. M., Chew, S.-H., Wong, C. X. J., Jaghoori, A., Iwai, S., Lyrtzis, E., Lim, M. Y., Chew, R. R.-X., Chew, A., Sanders, P., et al. (2022) · Heart Rhythm O2 · doi:10.1016/j.hroo.2022.09.008 · Cited by 10
“Unspecified 65 (2) Religion Orthodox 786 (26) Protestant 2015 (67) Muslim 129 (4) Other religion or no religion 70 (2) Education Illiterate 160 (5) Primary level school 462 (15) Secondary level school 1028 (34) Certificate, diploma, or above 1324 (44) Unspecified 26 (1) Occupation Unemployed 84 (3) Employed (worker) 4…”
Weidlich, S., Mannhart, D., Kennedy, A., Doggart, P., Serban, T., Knecht, S., de Lavallaz, J. D. F., Kühne, M., Sticherling, C., & Badertscher, P. (2023) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2023.12.003 · Cited by 9
“The results of our study extend and corroborate previous work with smart devices 9,10,17,18 as well as neural networks 19,20 and con fi rm that the amount of inconclusive tracings is higher than initially reported.”
ResultFrancisco, A., Pascoal, C., Lamborne, P., Morais, H., & Gonçalves, M. A. D. A. (2025) · Cureus · doi:10.7759/cureus.77404 · Cited by 12
Consumer wearable technologies to identify and monitor exercise-related arrhythmias in athletes
Sanchis‐Gomar, F., Lavie, C. J., & Perez, M. V. (2020) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000817 · Cited by 9
Utsha, U. T., Tsai, I. H., & Morshed, B. I. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-45878-1_10 · Cited by 9
Stroke Prevention in Atrial Fibrillation
Gao, X., & PASSMAN, R. S. (2022) · Current Cardiology Reports · doi:10.1007/s11886-022-01787-1 · Cited by 8
Validation of diagnostic accuracy of a handheld, smartphone-based rhythm recording device
Paul‐Nordin, A., Carnlöf, C., Insulander, P., Ali, A. M., Jensen‐Urstad, M., Saluveer, O., & Drca, N. (2023) · Expert Review of Medical Devices · doi:10.1080/17434440.2023.2171290 · Cited by 8
Ikeda, T., Ashihara, T., Iwasaki, Y., Ono, M., Kagiyama, N., Kimura, T., KUSANO, K. F., Kohno, R., Saku, K., Sasano, T., et al. (2025) · Circulation Journal · doi:10.1253/circj.cj-24-0960 · Cited by 14
Smartphone and new tools for atrial fibrillation diagnosis: evidence for clinical applicability
PAPACCIOLI, G., BASSI, G., Cecilia, L. U. G. I., Parente, E., D’ANDREA, A., PROIETTI, R., Imbalzano, E., TURKI, A. A., & Russo, V. (2022) · Minerva Cardiology and Angiology · doi:10.23736/s2724-5683.22.05841-0 · Cited by 7
Is machine learning the future for atrial fibrillation screening?
Sivanandarajah, P., Wu, H., Bajaj, N., Khan, S. N., & Ng, F. S. (2022) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2022.04.001 · Cited by 8
Wearables in Sports Cardiology
Beavers, D. L., & Chung, E. H. (2022) · Clinics in Sports Medicine · doi:10.1016/j.csm.2022.02.004 · Cited by 7
Introduction of Wearable Device in Cardiovascular Field for Monitoring Arrhythmia
Kim, N. H., & Ko, J. S. (2021) · Chonnam Medical Journal · doi:10.4068/cmj.2021.57.1.1 · Cited by 7
Krey, M. (2020) · International Conference on Information Management · doi:10.1109/icim49319.2020.244711 · Cited by 8
“A sensitivity of 96.6% and a specificity of 94.1% was measured compared to a 12-lead electrocardiogram [32].”
MethodInfluentialGunawardhana, M., Kulathilaka, A., & Zhao, J. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00324-7 · Cited by 8
Santala, O. E., Lipponen, J. A., Jäntti, H., Rissanen, T. T., Tarvainen, M. P., Väliaho, E., Rantula, O., Naukkarinen, N. S., Hartikainen, J. E. K., Martikainen, T. J., et al. (2023) · Cardiology in Review · doi:10.1097/crd.0000000000000526 · Cited by 10
“52,53 Traditional automatic AF detection algorithms are designed to detect AF during an arrhythmia episode.”
Method
Metadata
- Authors
- William, A. D., Kanbour, M., Callahan, T., Bhargava, M., Varma, N., Rickard, J., Saliba, W., Wolski, K., Hussein, A., Lindsay, B. D., Wazni, O. M., & Tarakji, K. G.
- Affiliations
- Cleveland Clinic (US)
- Marshall University (US)
- Corresponding author
- Tarakji, Khaldoun G.
- ORCID
- Venue
- Heart Rhythm
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 15
- Issue
- 10
- Pages
- 1561-1565
- ISSN
- 1547-5271 (ISSN-L)
- OpenAlex
- W2888509599
- Cited by
- 154
- References
- 14
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 10 · 2023: 32 · 2022: 19 · 2021: 30 · 2020: 25 · 2019: 23 · 2018: 2
- FWCI
- 15.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Heart Rhythm (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- atrial fibrillation, diagnostic accuracy, sensitivity and specificity, 12-lead ECG
- MeSH terms
- Smartphone, Adult, Aged, Aged, 80 and over, Algorithms, Atrial Fibrillation, Electrocardiography, Female, Heart Conduction System, Humans, Male, Middle Aged, Predictive Value of Tests, Signal Processing, Computer-Assisted, Telemetry, Reproducibility of Results, Single-Blind Method, Mobile Applications
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