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September 16, 2025Frontiers in MedicineOpen Access

Severity assessment of COVID-19 disease: radiological visual score versus automated quantitative CT parameters using a pneumonia analysis algorithm

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Authors

DBDario BaldiSynlab Czech (Czechia)BPBruna PunzoSynlab Czech (Czechia)ACAndrea ColacinoSynlab Czech (Czechia)

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Implication

Observational analysis identified high concordance between AI-assisted assessments and radiologist scores, suggesting improvements in pneumonia management.

Key Points

  • Visual and AI-based CT assessments showed high concordance, particularly in patients with over 25% lung involvement.
  • Both assessment methods had similar diagnostic performance, with visual accuracy at 44% and quantitative at 45%, indicating limited predictive power for COVID-19.
  • The analysis included 611 patients and assessed severity using a 5-class scale against AI-derived CT parameters.
  • Temporal assessment showed a trend of improving agreement over time, emphasizing the need for consistent evaluation methods.

Cite This Study

Baldi et al. (2025) studied this question.

synapsesocial.com/papers/68d4565b31b076d99fa5b3d1https://doi.org/10.3389/fmed.2025.1606771
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