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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.

ArtiQ.PFT
Bekijk onze peer-reviewed ArtiQ.PFT publicaties en conferentie resultaten.
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

ArtiQ.QC
Bekijk onze peer-reviewed ArtiQ.QC publicaties en conferentie resultaten.
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