Spirometry services in England post-pandemic and the potential role of AI support software: a qualitative study of challenges and opportunities.

Background: Spirometry services to diagnose and monitor lung disease in primary care were identified as a priority in the NHS Long Term Plan, and are restarting post-COVID-19 pandemic in England; however, evidence regarding best practice is limited.

Aim: To explore perspectives on spirometry provision in primary care, and the potential for artificial intelligence (AI) decision support software to aid quality and interpretation.

Design And Setting: Semi-structured interviews with stakeholders in spirometry services across England.

Machine learning to differentiate pulmonary hypertension due to left heart disease from pulmonary arterial hypertension.

Background And Aims: Pulmonary hypertension due to left heart disease (PH-LHD) is the most frequent form of PH. As differential diagnosis with pulmonary arterial hypertension (PAH) has therapeutic implications, it is important to accurately and noninvasively differentiate PH-LHD from PAH before referral to PH centres. The aim was to develop and validate a machine learning (ML) model to improve prediction of PH-LHD in a population of PAH and PH-LHD patients.

Predicting total lung capacity from spirometry: a machine learning approach.

Background And Objective: Spirometry patterns can suggest that a patient has a restrictive ventilatory impairment; however, lung volume measurements such as total lung capacity (TLC) are required to confirm the diagnosis. The aim of the study was to train a supervised machine learning model that can accurately estimate TLC values from spirometry and subsequently identify which patients would most benefit from undergoing a complete pulmonary function test.

Methods: We trained three tree-based machine learning models on 51,761 spirometry data points with corresponding TLC measurements.

Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation.

Background: Few studies have investigated the collaborative potential between artificial intelligence (AI) and pulmonologists for diagnosing pulmonary disease. We hypothesised that the collaboration between a pulmonologist and AI with explanations (explainable AI (XAI)) is superior in diagnostic interpretation of pulmonary function tests (PFTs) than the pulmonologist without support.

Methods: The study was conducted in two phases, a monocentre study (phase 1) and a multicentre intervention study (phase 2).

Estimating individual treatment effects on COPD exacerbations by causal machine learning on randomised controlled trials.

Rationale: Estimating the causal effect of an intervention at individual level, also called individual treatment effect (ITE), may help in identifying response prior to the intervention.

Objectives: We aimed to develop machine learning (ML) models which estimate ITE of an intervention using data from randomised controlled trials and illustrate this approach with prediction of ITE on annual chronic obstructive pulmonary disease (COPD) exacerbation rates.

Methods: We used data from 8151 patients with COPD of the Study to Understand Mortality and MorbidITy in COPD (SUMMIT) trial (NCT01313676) to address the ITE of fluticasone furoate/vilanterol (FF/VI) versus control (placebo) on exacerbation rate and developed a novel metric, Q-score, for assessing the power of causal inference models.

Artificial intelligence based software facilitates spirometry quality control in asthma and COPD clinical trials.

Rationale: Acquiring high-quality spirometry data in clinical trials is important, particularly when using forced expiratory volume in 1 s or forced vital capacity as primary end-points. In addition to quantitative criteria, the American Thoracic Society (ATS)/European Respiratory Society (ERS) standards include subjective evaluation which introduces inter-rater variability and potential mistakes. We explored the value of artificial intelligence (AI)-based software (ArtiQ.

Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD.

Background: Parameters from maximal expiratory flow-volume curves (MEFVC) have been linked to CT-based parameters of COPD. However, the association between MEFVC shape and phenotypes like emphysema, small airways disease (SAD) and bronchial wall thickening (BWT) has not been investigated.

Research Question: We analyzed if the shape of MEFVC can be linked to CT-determined emphysema, SAD and BWT in a large cohort of COPDGene participants.

Turbulent finite element model applied for blood flow calculation in arterial bifurcation.

Background And Objective: Due to the relatively low fluid velocities in major arteries and veins, blood flow is by default laminar, however, turbulence can occur as a result of stenosis or other obstacles. Hemodynamic parameters like Wall Shear Stress or Oscillatory Shear Index can be used for plaque formation prediction, and these parameters are depended on the nature of the flow. Implementation of the k-ω turbulent flow in the Finite Element solver aims to improve numerical analysis of cardio-vascular condition development and progression.

Deep-learning algorithm helps to standardise ATS/ERS spirometric acceptability and usability criteria.

Rationale: While American Thoracic Society (ATS)/European Respiratory Society (ERS) quality control criteria for spirometry include several quantitative limits, it also requires manual visual inspection. The current approach is time consuming and leads to high intertechnician variability. We propose a deep-learning approach called convolutional neural network (CNN), to standardise spirometric manoeuvre acceptability and usability.

Applications of artificial intelligence and machine learning in respiratory medicine.

The past 5 years have seen an explosion of interest in the use of artificial intelligence (AI) and machine learning techniques in medicine. This has been driven by the development of deep neural networks (DNNs)-complex networks residing in silico but loosely modelled on the human brain-that can process complex input data such as a chest radiograph image and output a classification such as 'normal' or 'abnormal'. DNNs are 'trained' using large banks of images or other input data that have been assigned the correct labels.

Dealing with the Effect of Air in Fluid Structure Interaction by Coupled SPH-FEM Methods.

Smoothed particle hydrodynamics (SPH) and the finite element method (FEM) are often combined with the scope to model the interaction between structures and the surrounding fluids (FSI). There is the case, for instance, of aircrafts crashing on water or speedboats slamming into waves. Due to the high computational complexity, the influence of air is often neglected, limiting the analysis to the interaction between structure and water.

Area under the forced expiratory flow-volume loop in spirometry indicates severe hyperinflation in COPD patients.

Background: Severe hyperinflation causes detrimental effects such as dyspnea and reduced exercise capacity and is an independent predictor of mortality in COPD patients. Static lung volumes are required to diagnose severe hyperinflation, which are not always accessible in primary care. Several studies have shown that the area under the forced expiratory flow-volume loop (AreaFE) is highly sensitive to bronchodilator response and is correlated with residual volume/total lung capacity (RV/TLC), a common index of air trapping.

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests.

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases.120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations.

Mechanical properties, safety and resistance values of Lemgruber elastic tubing.

Background: Lemgruber elastic tubing has been used as an adjunct to exercise training with positive effects in healthy adults and in patients with chronic lung disease. Despite its benefits, there is a lack of information on the specific resistance, elongation, reproducibility and safety of the different types of Lemgruber elastic tubing.

Objectives: The primary outcome was to assess the length-resistance relation (E/R) of five Lemgruber elastic tubing of different diameters.

Inspiratory muscle training reduces diaphragm activation and dyspnea during exercise in COPD.

Among patients with chronic obstructive pulmonary disease (COPD), those with the lowest maximal inspiratory pressures experience greater breathing discomfort (dyspnea) during exercise. In such individuals, inspiratory muscle training (IMT) may be associated with improvement of dyspnea, but the mechanisms for this are poorly understood. Therefore, we aimed to identify physiological mechanisms of improvement in dyspnea and exercise endurance following inspiratory muscle training (IMT) in patients with COPD and low maximal inspiratory pressure (Pi).

Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential.

Purpose Of Review: The application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases.

Automated Interpretation of Pulmonary Function Tests in Adults with Respiratory Complaints.

Background: The use of pulmonary function tests is primarily based on expert opinion and international guidelines. Current interpretation strategies are using predefined cutoffs for the description of a typical pattern.

Objectives: We aimed to explore the predicted disease outcome based on the American Thoracic Society/European Respiratory Society (ATS/ERS) interpreting strategy.

Non-linear parameters of specific resistance loops to characterise obstructive airways diseases.

Background: Specific resistance loops appear in different shapes influenced by different resistive properties of the airways, yet their descriptive ability is compressed to a single parameter - its slope. We aimed to develop new parameters reflecting the various shapes of the loop and to explore their potential in the characterisation of obstructive airways diseases.

Methods: Our study included 134 subjects: Healthy controls (N = 22), Asthma with non-obstructive lung function (N = 22) and COPD of all disease stages (N = 90).

Unexpected improvements of lung function in chronic obstructive pulmonary disease.

Chronic Obstructive Pulmonary Disease (COPD) is usually characterized by a progressive decline of lung function. We reported the 10 years follow-up of an elderly man, a heavy smoker with severe COPD and apical bullous emphysema. During 6 months pulmonary rehabilitation program the patient's clinical state improved significantly and it associated with a steep increase in forced expiratory volume in one second (FEV1).