Researchers have revealed a novel AI-based system for automated lung-lobe segmentation, that can achieve both COVID-19 identification and lesion categorisation from CT scans, which is key to evaluating damage to the lungs and making a prognosis.
This first-of-its-kind AI-powered pipeline, based on the deep-learning paradigm, uses a new segmentation module that automatically identifies lung parenchyma and lobes. The segmentation network is then combined with classification networks for COVID-19 identification and lesion categorisation. To test the system, the model’s classification results were compared with those obtained by three expert radiologists on a dataset of 166 CT scans.
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In a recently published paper, they reported a sensitivity of 90.3% and a specificity of 93.5% for COVID-19 detection, similar or better than findings by the expert radiologists, and an average lesion categorisation accuracy of about 84%. Further, the prior segmentation of the lung and lobe improved classification performance by over 6 percent.
Investigators tested the AI-empowered software pipeline on multiple CT scans at the Spallanzani Institute in Italy, and showed that:
- The segmentation networks are able to effectively extract lung parenchyma and lobes from CT scans, outperforming state of the art models.
- The COVID-19 identification module yields better accuracy (as well as specificity and sensitivity) than expert radiologists.
- The AI model learned automatically, and without any supervision, the CT scan features corresponding to the three most common lesions spotted in the COVID-19 pneumonia, i.e., consolidation, ground glass and crazy paving, which demonstrate its reliability in supporting the diagnosis by using only radiological images. This means that a positive patient can be differentiated from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by simply evaluating the presence of those lesions in CT scans.
- The AI models can be integrated into a publicly available user-friendly GUI to support AI explainability for radiologists. The GUI is able to process entire CT scans and report if the patient is likely to be affected by COVID-19, while showing the scan slices that support the decision.
The results obtained in this study both for COVID-19 identification and lesion categorisation pave the way for further improvements towards the implementation of an advanced COVID-19 CT/RX diagnostic system that is easily interpretable, robust and able to provide disease identification and differential diagnosis, as well as risk of disease progression.