Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review

by   Michael Roberts, et al.

Background: Machine learning methods offer great potential for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images. In this systematic review we critically evaluate the machine learning methodologies employed in the rapidly growing literature. Methods: In this systematic review we reviewed EMBASE via OVID, MEDLINE via PubMed, bioRxiv, medRxiv and arXiv for published papers and preprints uploaded from Jan 1, 2020 to June 24, 2020. Studies which consider machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images were included. A methodology quality review of each paper was performed against established benchmarks to ensure the review focusses only on high-quality reproducible papers. This study is registered with PROSPERO [CRD42020188887]. Interpretation: Our review finds that none of the developed models discussed are of potential clinical use due to methodological flaws and underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. Typically, we find that the documentation of a model's development is not sufficient to make the results reproducible and therefore of 168 candidate papers only 29 are deemed to be reproducible and subsequently considered in this review. We therefore encourage authors to use established machine learning checklists to ensure sufficient documentation is made available, and to follow the PROBAST (prediction model risk of bias assessment tool) framework to determine the underlying biases in their model development process and to mitigate these where possible. This is key to safe clinical implementation which is urgently needed.


page 16

page 17


MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis

Early and reliable COVID-19 diagnosis based on chest 3-D CT scans can as...

Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data

There are multiple papers published about different AI models for the CO...

A Systematic Review of Computational Thinking in Early Ages

Nowadays, technology has become dominant in the daily lives of most peop...

Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research

The current flood of information in all areas of machine learning resear...

Machine learning approaches for COVID-19 detection from chest X-ray imaging: A Systematic Review

There is a necessity to develop affordable, and reliable diagnostic tool...

Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis

Around 450 million people are affected by pneumonia every year which res...

A Research Agenda on Pediatric Chest X-Ray: Is Deep Learning Still in Childhood?

Several reasons explain the significant role that chest X-rays play on s...

Please sign up or login with your details

Forgot password? Click here to reset