AI can detect COVID and other conditions from chest X-rays
An international team of researchers has developed a groundbreaking artificial intelligence (AI) system that can rapidly detect COVID-19 from chest X-rays with more than 98% accuracy, as documented in the journal Scientific Reports.
Corresponding author Professor Amir H Gandomi, from the University of Technology Sydney (UTS) Data Science Institute, said there is a pressing need for effective automated tools to detect COVID-19, given the significant impact on public health and the global economy. Common symptoms include fever, cough, difficulty breathing and a sore throat; however, it can be difficult to distinguish COVID-19 from flu and other types of pneumonia.
“The most widely used COVID-19 test, real-time polymerase chain reaction (PCR), can be slow and costly, and produce false negatives,” Gandomi said. “To confirm a diagnosis, radiologists need to manually examine ... CT scans or X-rays, which can be time-consuming and prone to error.”
The new AI system uses a deep learning-based algorithm called a custom convolutional neural network (custom CNN) that is able to quickly and accurately distinguish between COVID-19 cases, normal cases and pneumonia in X-ray images. As explained by Gandomi, “Deep learning offers an end-to-end solution, eliminating the need to manually search for biomarkers. The custom CNN model streamlines the detection process, providing a faster and more accurate diagnosis of COVID-19.
“If a PCR test or rapid antigen test shows a negative or inconclusive result, due to low sensitivity, patients may require further examination via radiological imaging to confirm or rule out the virus’s presence. In this situation the new AI system could prove beneficial.”
The performance of the custom CNN model was evaluated via a comprehensive comparative analysis, with accuracy as the performance criterion, with the results showing that the new model outperforms the other AI diagnostic models. The breakthrough thus represents a significant step in combating the ongoing challenges posed by the pandemic, potentially transforming the landscape of COVID-19 diagnosis and management.
“The new AI system could be particularly beneficial in countries experiencing high levels of COVID-19 where there is a shortage of radiologists,” Gandomi noted. “Chest X-rays are portable, widely available and provide lower exposure to ionising radiation than CT scans.”
Separately to this, researchers at CSIRO’s Australian e-Health Research Centre (AEHRC) have been comparing different AI models to identify improvements in the diagnostic accuracy of automated chest X-ray interpretation and reporting. CSIRO Research Scientist Dr Aaron Nicolson said a better understanding of optimal models will lead to greater accuracy in using AI to diagnose heart and lung conditions via X-ray images.
“AI has the potential to improve health services, and in particular better support health professionals by easing their burden and workload of current non-automated practices,” Nicolson said.
“Automated report generation for X-rays could reduce clinician burnout and create space for them to provide more robust patient care.”
Current methods of AI X-ray report generation use an ‘encoder’ to read the chest X-ray images and ‘decoder’ to produce a report — but until now there has been no research into which encoder and decoder is best for automated chest X-ray report generation. Additionally, it is possible to transfer the knowledge learned from one task, such as classifying natural images or generating Wikipedia articles, to improve the task at hand — in this case, automated reporting. This method is known as ‘warm starting’ an AI model.
The imaging team at AEHRC tested different encoders and decoders, as well as the effectiveness of different tasks for warm starting the chest X-ray report generation task. Their findings, published in Artificial Intelligence in Medicine, show that the optimal combination of encoder and decoder, together with the use of the warm starting method, produce a 26.9% relative improvement on the accuracy of automated image reporting. Evaluation was done by comparing with human radiologist reports.
“The increasing clinical reliance on imaging for diagnosis, combined with a relative shortage of radiologists, is creating unsustainable workloads and a search for workload management solutions,” said radiologist Dr Doug Anderson, from Monash Medicine.
“An exciting potential solution to onerous radiologist workloads is using artificial intelligence to assist with interpreting chest X-rays and documentation.”
While the model identifies some pathologies consistently (eg, pleural effusion) it does not yet accurately identify others (eg, lung lesion). The next step is improving the AI model so it can accurately identify most pathologies. These improvements are required before the technology can be used in a clinical setting.
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