Elena Smets

Optimising a COPD diagnostic pathway: benchmarking AI decision support to release clinician time for smoking cessation referrals

Emma Giffen, Dervla Carroll, Elena Smets, Marko Topalovic et autres

Background AI tools offer potential to enhance diagnostics and streamline workflows. NHS Greater Glasgow and Clyde’s high-volume COPD spirometry service (~600 attendees/month) faced significant backlogs. The ‘POLARIS’ project was established to increase capacity via digital transformation, including evaluation of the ArtiQ.Spiro AI …

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P3 Integrating AI-assisted spirometry: validation and user experience in a COPD diagnostic pathway

Emma Giffen, David Carroll, Elena Smets, Karolien Van Orshoven et autres

Introduction Accurate interpretation of spirometry is essential for diagnosing chronic obstructive pulmonary disease (COPD). AI-based tools have the potential to enhance diagnostic accuracy and streamline workflow. ArtiQ.Spiro is an AI-based software that provides spirometry quality feedback and diagnostic support. As part of …

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Response: To the Comment on AI-Assisted Spirometry Interpretation in Primary Care: A Randomized Controlled Trial

Gillian Doe, Winston Banya, George Edwards, Marko Topalovic et autres

Response to the Comment on “AI-Assisted Spirometry Interpretation in Primary Care: A Randomized Controlled Trial.”

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Retrospective Validation of an AI-Based Spirometry Decision Support Tool in a COPD Diagnostic Pathway

Emma Giffen, Fergus Wilkie, Elena Smets, Karolien Van Orshoven et autres

Introduction Accurate interpretation of spirometry is essential for diagnosing chronic obstructive pulmonary disease (COPD). AI-based tools have the potential to enhance diagnostic accuracy and streamline workflow. ArtiQ. Spiro is an AI-based software that provides spirometry quality feedback and diagnostic support. As part …

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AI-Assisted Spirometry Interpretation in Primary Care: A Randomized Controlled Trial

Gillian Doe, Winston Banya, George Edwards, Marko Topalovic et autres

BackgroundSpirometry quality and confidence in spirometry interpretation are highly variable in primary care, contributing to underdiagnosis, overdiagnosis, and misdiagnosis of chronic respiratory diseases worldwide. Artificial intelligence (AI) decision support software has been shown to improve the accuracy of lung function interpretation in …

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A case study on the impact of Artificial Intelligence supported spirometry in primary care

Elena Smets, Julie Maes, Jonathan Rees, Marko Topalovic

Background: Spirometry is a key test to identify respiratory diseases. However, long waiting lists are present throughout England, with an estimated backlog of 200 – 250 patients per 500.000. Moreover, poor quality of spirometry data and a lack of confidence when interpreting …

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AI model generalization assessed on unlabeled pulmonary function data from daily use

Ahmed Elmahy, Julie Maes, Elena Smets, Maarten De Vos et autres

Rationale: AI has achieved promising applications in respiratory care, but generalization of models to new data is underexplored. We aim to investigate the representativeness of an AI diagnostic model for pulmonary function testing (ArtiQ.PFT) to its clinical usage. Methods: We compared the …

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Big data insights in the ILD population through AI-driven analytics

Ahmed Elmahy, Julie Maes, Elena Smets, Maarten De Vos et autres

Rationale: ILD is a rare progressive disease with a huge burden. We aim to utilize AI model for pulmonary function testing (ArtiQ.PFT) to leverage ILD related data insights. Understanding the ILD population in a specific region would enable better allocation of resources …

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Late Breaking Abstract - SPIROmetry interpretation in primary care with or without Artificial Intelligence Decision support software (SPIRO-AID)

Gillian Doe, Ethaar El-Emir, George Edwards, Marko Topalovic et autres

Introduction: Quality and interpretation accuracy of spirometry are variable in primary care. We aimed to evaluate whether an AI decision support software (ArtiQ. Spiro) improves the diagnostic prediction of primary care clinicians. Methods: A parallel, two-group, randomised controlled trial of primary care …

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Artefact detection and quality assessment of ambulatory ECG signals

Jonathan Moeyersons, Elena Smets, John Morales, Amalia Villa et autres

BACKGROUND AND OBJECTIVES: The presence of noise sources could reduce the diagnostic capability of the ECG signal and result in inappropriate treatment decisions. To mitigate this problem, automated algorithms to detect artefacts and quantify the quality of the recorded signal are needed. …

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Accelerometer-based Sleep/Wake Detection in an Ambulatory Environment

Jan Cornelis, Elena Smets, Chris Van Hoof

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Large-scale wearable data reveal digital phenotypes for daily-life stress detection

Elena Smets, Emmanuel Rios Velazquez, Giuseppina Schiavone, Imen Chakroun et autres

Physiological signals have shown to be reliable indicators of stress in laboratory studies, yet large-scale ambulatory validation is lacking. We present a large-scale cross-sectional study for ambulatory stress detection, consisting of 1002 subjects, containing subjects' demographics, baseline psychological information, and five consecutive …

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