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). Each phase utilised two different sets of 24 PFT reports of patients with a clinically validated gold standard diagnosis. Each PFT was interpreted without (control) and with XAI's suggestions (intervention). Pulmonologists provided a differential diagnosis consisting of a preferential diagnosis and optionally up to three additional diagnoses. The primary end-point compared accuracy of preferential and additional diagnoses between control and intervention. Secondary end-points were the number of diagnoses in differential diagnosis, diagnostic confidence and inter-rater agreement. We also analysed how XAI influenced pulmonologists’ decisions. Results In phase 1 (n=16 pulmonologists), mean preferential and differential diagnostic accuracy significantly increased by 10.4% and 9.4%, respectively, between control and intervention (p<0.001). Improvements were somewhat lower but highly significant (p<0.0001) in phase 2 (5.4% and 8.7%, respectively; n=62 pulmonologists). In both phases, the number of diagnoses in the differential diagnosis did not reduce, but diagnostic confidence and inter-rater agreement significantly increased during intervention. Pulmonologists updated their decisions with XAI's feedback and consistently improved their baseline performance if AI provided correct predictions. Conclusion A collaboration between a pulmonologist and XAI is better at interpreting PFTs than individual pulmonologists reading without XAI support or XAI alone

European expert consensus on assessment and management of hospitalised exacerbations of COPD (CICERO ERS CRC)

This abstract was presented at the 2020 ERS International Congress, in session “Respiratory viruses in the "pre COVID-19" era”

Standardisation of clinical assessment, management and follow-up of acute hospitalised exacerbation of COPD: a Europe-wide consensus

Background: Despite hospitalization for exacerbation being a high-risk event for morbidity and mortality, there is little consensus globally regarding the assessment and management of hospitalised exacerbations of COPD. We aimed to establish a consensus list of symptoms, physiological measures, clinical scores, patient questionnaires and investigations to be obtained at time of hospitalised COPD exacerbation and follow-up. Methods: A modified Delphi online survey with pre-defined consensus of importance, feasibility and frequency of measures at hospitalisation and follow-up of a COPD exacerbation was undertaken. Findings: A total of 25 COPD experts from 18 countries contributed to all 3 rounds of the survey. Experts agreed that a detailed history and examination were needed. Experts also agreed on which treatments are needed and how soon these should be delivered. Experts recommended that a full blood count, renal function, C-reactive protein and cardiac blood biomarkers (BNP and troponin) should be measured within 4 hours of admission and that the modified Medical Research Council dyspnoea scale (mMRC) and COPD assessment test (CAT) should be performed at time of exacerbation and follow-up. Experts encouraged COPD clinicians to strongly consider discussing palliative care, if indicated, at time of hospitalisation. Interpretation: This Europe-wide consensus document is the first attempt to standardise the assessment and care of patients hospitalised for COPD exacerbations. This should be regarded as the starting point to build knowledge and evidence on patients hospitalised for COPD exacerbations

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). Each phase utilised two different sets of 24 PFT reports of patients with a clinically validated gold standard diagnosis. Each PFT was interpreted without (control) and with XAI’s suggestions (intervention). Pulmonologists provided a differential diagnosis consisting of a preferential diagnosis and optionally up to three additional diagnoses. The primary end-point compared accuracy of preferential and additional diagnoses between control and intervention. Secondary end-points were the number of diagnoses in differential diagnosis, diagnostic confidence and inter-rater agreement. We also analysed how XAI influenced pulmonologists’ decisions. Results In phase 1 (n=16 pulmonologists), mean preferential and differential diagnostic accuracy significantly increased by 10.4% and 9.4%, respectively, between control and intervention (p<0.001). Improvements were somewhat lower but highly significant (p<0.0001) in phase 2 (5.4% and 8.7%, respectively; n=62 pulmonologists). In both phases, the number of diagnoses in the differential diagnosis did not reduce, but diagnostic confidence and inter-rater agreement significantly increased during intervention. Pulmonologists updated their decisions with XAI’s feedback and consistently improved their baseline performance if AI provided correct predictions. Conclusion A collaboration between a pulmonologist and XAI is better at interpreting PFTs than individual pulmonologists reading without XAI support or XAI alone