Diagnostic performance of a wearing dynamic ECG recorder for atrial fibrillation screening: the HUAMI heart study

Artificial intelligence in respiratory care: Current scenario and future perspective

BACKGROUND

This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field.

METHODS

A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making.

RESULTS

The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies.

CONCLUSION

By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices.

IMPROVEMENTS

Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.

Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis

Atrial fibrillation (AF) is the most common arrhythmia with a high burden of morbidity including impaired quality of life and increased risk of thromboembolism. Early detection and management of AF could prevent thromboembolic events. Artificial intelligence (AI)--based methods in healthcare are developing quickly and can be proved as valuable for the detection of atrial fibrillation. In this metanalysis, we aim to review the diagnostic accuracy of AI-based methods for the diagnosis of atrial fibrillation. A predetermined search strategy was applied on four databases, the PubMed on 31 August 2022, the Google Scholar and Cochrane Library on 3 September 2022, and the Embase on 15 October 2022. The identified studies were screened by two independent investigators. Studies assessing the diagnostic accuracy of AI-based devices for the detection of AF in adults against a gold standard were selected. Qualitative and quantitative synthesis to calculate the pooled sensitivity and specificity was performed, and the QUADAS-2 tool was used for the risk of bias and applicability assessment. We screened 14,770 studies, from which 31 were eligible and included. All were diagnostic accuracy studies with case-control or cohort design. The main technologies used were: (a) photoplethysmography (PPG) with pooled sensitivity 95.1% and specificity 96.2%, and (b) single-lead ECG with pooled sensitivity 92.3% and specificity 96.2%. In the PPG group, 0% to 43.2% of the tracings could not be classified using the AI algorithm as AF or not, and in the single-lead ECG group, this figure fluctuated between 0% and 38%. Our analysis showed that AI-based methods for the diagnosis of atrial fibrillation have high sensitivity and specificity for the detection of AF. Further studies should examine whether utilization of these methods could improve clinical outcomes.

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

Validation of an algorithm for atrial fibrillation detection with an analog smartwatch: prospective interventional clinical study

BACKGROUND

Atrial Fibrillation (AF) affects about 4% of the World's population and is one of the major causes of stroke, heart failure, sudden death, and cardiovascular morbidity. It can be difficult to diagnose when asymptomatic or in the paroxysmal stage, and its natural history is not well understood. New wearables and connected devices offer an opportunity to improve on this situation.

OBJECTIVE

To validate an algorithm for the automatic detection of AF from a single-lead electrocardiogram (ECG) taken with a smartwatch.

METHODS

Eligible patients were recruited from 4 sites in Paris, France. Twelve-lead reference ECGs and single-lead ECG were captured simultaneously. The ECGs were reviewed by independent, blinded board-certified cardiologists. The sensitivity and specificity of the algorithm to detect AF and normal sinus rhythm (NSR) were calculated. The quality of single-lead ECGs (visibility and polarity of waves, interval durations, heart rate) was assessed by comparison to the gold standard.

RESULTS

Two hundred and sixty two patients were included in the final analysis: 100 AF, 113 NSR, 45 Other arrhythmia, 4 presented unreadable ECGs. Mean age was of 74.3 years ± 12.3 in the AF group versus 61.8 years old ± 14.3 and 66.9 years old ± 15.2 in the NSR and other arrhythmia groups respectively. 6.9% (18/262) were classified as "Noise" by the algorithm. Excluding "Other" arrhythmia and "Noise", the sensitivity to detect AF was of SeAF/NSR = 0.963 (0.894), and specificity of SpAF/NSR = 1.000 (0.967). Visibility and polarity accuracies (1-lead ECG vs 12-lead ECG) were respectively: P-waves: 96.9%/100%, QRS-complexes: 99.2%/98.8%, and T-waves: 91.2%/99.5% . P-wave visibility accuracy was of 99% (99/100) in AF patients and of 95.7% (155/162) when excluding AF patients. The absolute values of the mean difference in PR duration and QRS width were below 3ms, and more than 97% of these differences were below 40ms. The mean difference between the HR calculated by the algorithm and the device 1-lead ECG read by cardiologists was 0.55 bpm.

CONCLUSIONS

Withings algorithm demonstrated great diagnostic performance for AF detection. The smartwatch single-lead ECGs also demonstrated good quality for physician use in daily routine care.

CLINICALTRIAL

ClinicalTrials.gov registration number: NCT04351386.