Shaping the future of cardiovascular medicine in the new era of wearable devices

Gehr, Sinje; Rußmann, Christoph

doi:10.1038/s41569-022-00729-2

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…The European in vitro Diagnostics Regulation (IVDR) explicitly includes software and thus AI algorithms in its requirements. This poses significant challenges for in vitro diagnostics (IVD) companies that use AI for data analysis and decision support [19]. However, if the ethical and legal issues are taken in to account and addressed well, we see enormous potential for AI to revolutionize medical biotechnology by enabling the faster, more accurate and more cost-effective identification and development beyond new drugs.…”

Section: Ai In Medical Biotechnologymentioning

confidence: 99%

“…This can involve the use of DL algorithms to automatically segment and classify structures in medical images; (iv) Predictive modeling: AI can be used to analyse data from various sources, such as electronic health records and wearable devices, to make predictions about an individual's health. This can include the use of machine learning algorithms to predict the likelihood of an individual developing a particular disease or the likelihood of a particular treatment being effective [19].…”

Section: Ai In Medical Biotechnologymentioning

confidence: 99%

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Current Trends of Artificial Intelligence in Biotechnology

Mishra*

2024

OAJDA

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Artificial intelligence (AI) is already widely used in biotechnology to solve a variety of problems. These include, for example, drug discovery, drug safety, functional and structural proteomics/genomics, metabolomics, pharmacology, pharmacogenetics and pharmacogenomics, among many others. Future advances in this domain depend critically on the ability of biotechnology researchers to use advanced AI solutions effectively. The biotechnology industry currently relies heavily on data storage, filtering, analysis and sharing. Biotechnology companies and various healthcare organizations around the world already maintain huge data bases. Drug manufacturing, chemical analysis of various compounds, sequencing of RNA and DNA, enzyme studies, and other similar biological processes all require strong support from AI software solution to move faster and reduce manual errors. It is important to emphasize at the very beginning that all the successful AI we are describing today relies entirely on digital technology to function. Digitalization is therefore the very first step towards any AI application. In many cases, AI systems are integrated with other digital technologies such as sensors, actors (cyber-physical systems (CPS), often just called robots), and technology to enable the automation of tasks and the collection and analysis of data. Overall, the development and use of AI is dependent on digital technology - the basis for it is digital computers. Digital transformation refers to the use of digital technologies to fundamentally change the way companies, organizations, research institutions and universities operate. In the context of biotechnology, digital transformation can involve the introduction of new technologies and processes to improve the efficiency, accuracy, and speed of research and development and enable the development of entirely new and disruptive products and services. Digital transformation can help accelerate the development and use of AI in biotechnology by providing access to big data and automating certain tasks, which can help improve the efficiency and accuracy of research and development. In this Editorial, it has been clearly stated what exactly AI means, concomitant with explaining the specific differences between AI, machine learning, and deep learning to provide a worthy common understanding. Afterwards, there is successful introduction of significant domains of biotechnology where AI is being applied or may be applicable in the future. Thereafter, this editorial presents certain cross-cutting challenges where it is predominantly significant to advance future studies

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Section: Ai In Medical Biotechnologymentioning

confidence: 99%

Section: Ai In Medical Biotechnologymentioning

confidence: 99%

Current Trends of Artificial Intelligence in Biotechnology

Mishra*

2024

OAJDA

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“…Given their increasing utility and ubiquity, portable and wearable devices capable of recording single-lead ECG tracings have been proposed as a platform for cardiovascular monitoring and screening. [16][17][18][19] Further, artificial intelligence (AI)enhanced interpretation of ECGs (AI-ECG) has been shown to detect hidden cardiovascular disease signatures from single-lead ECGs, highlighting the potential of these devices for improving cardiovascular health. [20][21][22][23][24] However, these portable ECGs are prone to the introduction of noise during acquisition, which can limit the performance of AI models unless specialized measures are taken to ensure they are resilient to such noise.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Enabled Prediction of Heart Failure Risk from Single-lead Electrocardiograms

Dhingra,

Aminorroaya,

Pedroso

et al. 2024

Preprint

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Importance: Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment. Objective: To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs. Design: Multicohort study. Setting: Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Participants: Individuals without HF at baseline. Exposures: AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). Main Outcomes and Measures: Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results: There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (delta=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF. Conclusions and Relevance: Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.

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“…With the development of articial intelligence and interactive technology, smart sensors and the internet of things (IoT) entered the mainstream market because of their excellent chemical and physical properties. [1][2][3][4][5] Typically, infrared (IR)based technology has attracted signicant attention because the corresponding spectrum of matter (including transmission and reection spectra) contains rich physical and chemical information. 6 As an emerging IR thermal detector, photothermoelectric (PTE) detectors combining photothermal and thermoelectric conversion can overcome bandgap limitations and achieve effective IR detection.…”

Section: Introductionmentioning

confidence: 99%