Abstract
Artificial Intelligence (AI) systems using symptoms/signs to detect respiratory diseases may improve diagnosis especially in limited resource settings. Heterogeneity in such AI systems creates an ongoing need to analyse performance to inform future research. This systematic literature review aimed to investigate performance and reporting of diagnostic AI systems using machine learning (ML) for pneumonia detection based on symptoms and signs, and to provide recommendations on best practices for designing and implementing predictive ML algorithms. This article was conducted following the PRISMA protocol, 876 articles were identified by searching PubMed, Scopus, and OvidSP databases (last search 5th May 2021). For inclusion, studies must have differentiated clinically diagnosed pneumonia from controls or other diseases using AI. Risk of Bias was evaluated using The STARD 2015 tool. Information was extracted from 16 included studies regarding study characteristics, ML-model features, reference tests, study population, accuracy measures and ethical aspects. All included studies were highly heterogenous concerning the study design, setting of diagnosis, study population and ML algorithm. Study reporting quality in methodology and results was low. Ethical issues surrounding design and implementation of the AI algorithms were not well explored. Although no single performance measure was used in all studies, most reported an accuracy measure over 90%. There is strong evidence to support further investigations of ML to automatically detect pneumonia based on easily recognisable symptoms and signs. To help improve the efficacy of future research, recommendations for designing and implementing AI tools based on the findings of this study are provided.
Highlights
Pneumonia is a form of acute lower respiratory infection
The research question we addressed is what symptom-based machine learning (ML) predictive models have been developed and how well do they perform? In this way, the aims of this study were to assess both the performance of published ML methods to diagnose pneumonia based on symptoms or signs, and the reporting quality of these studies
All types of studies were included if they reported on the use of artificial intelligence (AI) systems such as ma chine learning (ML) or deep learning (DL) techniques applied to dis tinguishing pneumonia based on signs and symptoms
Summary
Pneumonia is a form of acute lower respiratory infection. Pneumonia is generally characterized by specific symptoms such as fever, chills, cough with sputum production, chest pain and shortness of breath [1]. Many factors affect how serious pneumonia is, such as the type of pathogen causing the lung infection, age, and overall health status. Pneumonia tends to be more serious for children under the age of five, adults over the age of 65, people with certain conditions such as heart failure, diabetes, or COPD (chronic obstructive pulmonary disease), or people who have weak immune systems due to HIV/AIDS, chemo therapy (a treatment for cancer), or organ or blood and marrow stem cell transplant procedures [2]. When an individual has pneumonia, the alveoli, small sacs within the lungs, are filled with pus and fluid, which makes breathing painful and limits oxygen exchange [3]. Pneumonias can be categorized as community-acquired (CAP), hospital-acquired (HAP) (excluding ventilator-associated [4], which occurs in immunocompromised patients such as patients with human immunodeficiency virus (HIV) infection
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