ABSTRACT
Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic's impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology's most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general "fear of the unknown in AI" by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.
ABSTRACT
OBJECTIVE: The objective was to study hospitalised COVID-19 patients' mortality and intensive care unit (ICU) admission with covariates of interest (age, gender, ethnicity, clinical presentation, comorbidities and admission laboratory findings). METHODS: Logistic regression analyses were performed for patients admitted to University Hospital, University Hospitals Coventry and Warwickshire NHS Trust, between 24 January 2020 - 13 April 2020. RESULTS: There were 321 patients hospitalised. Median age was 73 years and 189 (59%) were male. Ethnicity was divided between Caucasian (77%), and black, Asian, and minority ethnic (BAME) groups (23%). Commonest symptoms were dyspnoea (62.9%), fever (59.1%) and cough (56%). Gastrointestinal symptoms amounted to 11.8%.Forty-four patients (13.7%) received ICU care. ICU male to female ratio was 3:1 (p=0.027; odds ratio (OR) 2.3; 95% confidence interval (CI) 1.1-4.9), BAME (p=0.008; OR 2.5; 95% CI 1.3-4.9), age >65 years (p=0.026; OR 0.28; 95% CI 0.09-0.93), heart disease (p=0.009; OR 0.2; 95% CI 0.1-0.6) and elevated C-reactive protein (CRP; p<0.001; OR 1.004; 95% CI 1.002-1.008) were associated with ICU admission.One-hundred and four patients (32.4%) died. Age >65 years (p=0.011; OR 5; 95% CI 1.6-21.9), neutrophils (p=0.047), neutrophil:lymphocyte ratio (NLR; p=0.028), CRP (p<0.001) and albumin (p=0.002) were associated with mortality. When analysis adjusted for age, CRP (p<0.001; OR 1.006; 95% CI 1.004-1.008) and albumin (p=0.005; OR 0.94; 95% CI 0.90-0.98) remained associated with mortality. CONCLUSIONS: COVID-19 has high mortality. BAME and male patients were associated with ICU admission. High CRP and low albumin (after correcting for age) were associated with mortality.
