Validated technology for Respiratory Diagnostics and Clinical Research
ArtiQ technology has been extensively validated and published in key journals. Discover the potential of respiratory AI in our whitepapers.
AI model generalization assessed on unlabeled pulmonary function data from daily use
We aim to investigate the representativeness of an AI diagnostic model for pulmonary function testing (ArtiQ.PFT) to its clinical usage.
2024
Big data insights in the ILD population through AI-driven analytics
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.
2024
ArtiQ.PFT: AI-powered decision support helps pulmonologists to diagnose ILD more accurately
This study shows that AI-based decision support on PFT interpretation improves accurate and early ILD diagnosis.
2023
How many lung function interpretations can an expert make in one hour based on the 2021 ERS/ATS standards?
This study investigates the speed at which an expert can perform PFT interpretations.
2023
Impact of the ERS/ATS 2021 standards for lung function interpretation
This study investigates how the recently published ERS/ATS guidelines for lung function interpretation (Stanojevic 2021) affect patient classification compared to the previous document (Pellegrino 2005).
2023
ArtiQ.PFT supports pulmonologists with the diagnosis of lung diseases.
This study with 19 pulmonologists and 60 retrospective cases shows an improvement in diagnostic accuracy of the primary diagnosis from 44% to 63%.
2022
Pulmonologists and ArtiQ.PFT team up to improve the diagnostic interpretation of PFTs.
This study where 78 pulmonologists collaborated with ArtiQ.PFT on 24 PFT cases shows an increase in diagnostic accuracy of up to 10%.
2022
ArtiQ.PFT, an example of the future Doctor and AI Teamwork.
This study where 16 pulmonologists collaborated with ArtiQ.PFT on 24 PFT cases shows an increase in diagnostic accuracy of up to 20%.
2022
ArtiQ.PFT supports diagnostic pathway with incomplete lung function
AI detect respiratory diseases even when PFTs did not include plethysmography data.
2021
AI in healthcare: are doctors still needed?
Watch the presentation below to get an overview of the latest applications of AI in respiratory medicine and to learn how doctors and AI can work together as a complimentary team.
2020
The reasoning behind ArtiQ.PFT’s diagnostic probability
A study investigating locally interpretable model-agnostic explanation (LIME) to explain the predictions of ArtiQ.PFT’s diagnostic probability.
2019
ArtiQ.PFT outperforms pulmonologists in the interpretation of PFT.
The ArtiQ.PFT software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all 1500 cases.
2019
Home Spirometry in Clinical Trials: Evaluating Adherence and Quality in Patients with asthma
This analysis aims to evaluate the adherence to the study protocol and the spirometry quality when performing unsupervised home spirometry.
2024
AI-Based Quality Assessment of Peak Expiratory Flow in Spirometry
This study introduces an AI model to automatically classify the quality of PEF curves.
2024
Is onsite spirometry quality predicting the quality of home spirometry?
This analysis aimed to determine whether the quality of a patient’s onsite spirometry could predict the quality of their home spirometry.
2024
High-Quality FVC Through Automated AI-based Detection of Early Termination in Spirometry: A Comparative Analysis
Accurate FVC measurements are essential for correct interpretation of spirometry. The updated ATS/ERS-technical standards for spirometry require at least one of three EOFE criteria to be met.
2024
Impact of Artificial Intelligence over-reading for instant feedback on quality of home spirometry
In this study, we are investigating the impact of instant feedback provided by an AI algorithm (ArtiQ.QC) on the quality of home spirometry.
2023
AI Over-Reading Based On ATS/ERS 2019 Criteria Is A Reliable Option For Instant Spirometry Quality Control In Clinical Trials
This study explores the value and reliability of artificial intelligence (AI) based quality control software (ArtiQ.QC) to assess spirometry quality in clinical trials using the 2019 ATS/ERS guidelines.
2023
ArtiQ.QC supports the evalutation of the GLI 2012 reference values in China (abstract)
Over-reading spirometry data from the Chinese population to improve data quality in GLI-research.
2022
How objective are the ATS/ERS 2019 guidelines?
How much do spirometry experts agree with each other… and with themselves?
2022
ArtiQ.QC in real-world research
Spirometry data from preschool children and healthy adults, evaluated by ArtiQ.QC.
2021
ArtiQ.QC in clinical trials (Asthma & COPD) based on 2005 ATS/ERS guidelines (abstract)
ArtiQ.QC over-reading & manual over-reading: are the results comparable? And with whom do experts agree more?
2021
Impact on spirometry quality results when using 2005 or 2019 ATS/ERS guidelines.
Learn more about the consequences of the ATS/ERS 2019 guidelines update.
2021
ArtiQ.QC in clinical trials (COPD) based on 2005 ATS/ERS guidelines
ArtiQ.QC over-reading & manual over-reading: are the results comparable? And with whom do experts agree more?
2021
ArtiQ.QC for large epidemiological studies.
Find out how AI can improve data quality in epidemiological studies.
2021
Deep learning algorithm helps to standardize ATS/ERS spirometric acceptability and usability criteria
ArtiQ.QC combines the visual experience of skilled technicians and ATS/ERS quantitative rules in automating the critical phase of spirometry quality control.
2020
Automating the quality control process: ArtiQ.QC meets NHANES
Neural Networks to standardize spirometric manoeuvre acceptability and usability.
2020
A case study on the impact of Artificial Intelligence supported spirometry in primary care
This study aims to investigate the impact of providing ArtiQ.Spiro to support primary care practitioners in performing and interpreting spirometry.
2024
Comparing performance of primary care clinicians in the interpretation of SPIROmetry with or without Artificial Intelligence Decision support software(SPIRO-AID): a protocol for a randomised controlled trial
This study aims to evaluate whether artificial intelligence (AI) decision-support software improves the performance of primary care clinicians in the interpretation of spirometry, against reference standards (expert interpretation).
2024
Clinical evaluation of artificial intelligence supported software in the interpretation of spirometry results in a primary care setting
This study aims to assess the accuracy of general practitioners in diagnosing respiratory diseases based on clinical examination only and with AI-supported spirometry.
2024
Spirometry services in England post-pandemic and the potential role of AI support software: a qualitative study of challenges and opportunities
This study explores perspectives on spirometry provision in primary care, and the potential for AI decision-support software to aid quality and interpretation.
2023
Insights into spirometry usage and the potential for AI-based decision support software in Belgian primary care
By focusing on a population at risk of COPD without a formal diagnosis, we study how ArtiQ.Spiro can support general practitioners (GPs) in diagnosing COPD.
2023
Belgian General Practitioners’ perspectives on the use of spirometry in primary care: a qualitative study
This study gives insights into the prevailing ideas of Belgian GPs about the spirometry and identifies barriers and facilitators for its use in primary care.
2023
AI models for respiratory diseases from spirometry alone: validation based on UK Biobank
This study evaluates the ability of AI to diagnose respiratory diseases in primary care on a representative dataset (UK Biobank, UKBB) using spirometry.
2023
AI-powered spirometry for early detection of ILD.
AI detected ILD in 27% of subjects, 3.8±2.1 years prior to ILD diagnosis through standard care.
2022
ERS/ATS technical standard on interpretative strategies for routine lung function tests
At the end of 2021, the ERS/ATS task force led by Sanja Stanojevic published the updated ERS/ATS standard for the interpretation of pulmonary function tests (PFT). In this summary document, we highlight what we believe are the most important elements from the 81-page document.
How can a normal lung function pattern still potentially hide asthma?
Asthma is a common, chronic disease that affects approximately 300 million people worldwide. It was noticed that ArtiQ.PFT often detected Asthma in patients having a normal lung function pattern according to the ERS/ATS 2021 Interpretation Guidelines and the GLI reference values, but why is that? The ArtiQ team did a study to try to better understand why ArtiQ.PFT detects asthma in these cases.
What is the Impact of the new 2021 ERS/ATS Interpretation Guidelines?
At the end of 2021 an international joint Task Force led by Sanja Stanojevic, appointed by the ERS/ATS with multidisciplinary expertise in conducting and interpreting PFTs and developing international standards released the new ERS/ATS Guidelines for the evaluation of pulmonary function tests (PFT).
Artificial Intelligence in Pulmonology: threat or opportunity?
Our world has changed drastically in the past decades with the advancement of new technologies. We have seen revolutions that can impact many different areas of our lives: self-driving cars became a reality, computers are beating world champions in chess, navigation apps show us the fastest route… All these technologies have one thing in common: artificial intelligence (AI).
Guestblog on how AI could outperform 120 pulmonologists in a PFT interpretation study.
Pulmonary function tests (PFTs) are a group of common medical tests that measure how well the lungs work. The tests usually refer to a combination of spirometry, body-plethysmography and diffusion capacity test.