Lung ultrasound with deep learning models may help stratify COVID-19 risk: Study
Italy: Deep-learning (DL) models can be used to automatically stratify patients on lung ultrasound(LUS) as having an either low or high risk of clinical worsening in COVID-19 patients, according to a recent study in the Journal of the Acoustical Society of America.
In the current coronavirus pandemic, lung ultrasound(LUS) has played an important role in evaluating COVID-19 patients. However, its use remains limited to the visual inspection of ultrasound data that negatively impacts the reproducibility and reliability of the findings. Many different proposed protocols lacked clinical validation.
To address the above problems, Federico Mento, University of Trento, Trento, Italy, and colleagues, were the first to propose protocol and scoring system. Next, they developed the DL algorithms capable of evaluating LUS videos providing, for each video-frame, the score as well as semantic segmentation. They also analyzed the impact of different imaging protocols and demonstrated the prognostic value of our approach. Based on which, they reported on the level of agreement between the DL and LUS experts, when evaluating LUS data.
Disclaimer: This website is primarily for healthcare professionals. The content here does not replace medical advice and should not be used as medical, diagnostic, endorsement, treatment, or prescription advice. Medical science evolves rapidly, and we strive to keep our information current. If you find any discrepancies, please contact us at corrections@medicaldialogues.in. Read our Correction Policy here. Nothing here should be used as a substitute for medical advice, diagnosis, or treatment. We do not endorse any healthcare advice that contradicts a physician's guidance. Use of this site is subject to our Terms of Use, Privacy Policy, and Advertisement Policy. For more details, read our Full Disclaimer here.
NOTE: Join us in combating medical misinformation. If you encounter a questionable health, medical, or medical education claim, email us at factcheck@medicaldialogues.in for evaluation.