To evaluate a deep learning algorithm as a decision support tool, for classifying HRCT based on ATS/ERS/JRS/ALAT IPF guideline criteria (SOFIA), among an international group of radiologists and pulmonologists. Automated decision support used in conjunction with formal HRCT evaluation may improve the application of guideline-based HRCT diagnoses and in principle could be used both as a screening tool in clinical trials and in the initial evaluation of patients with pulmonary fibrosis.
Walsh, S., Wells, A., Calandriello, L., Felder, F., Mackintosh, J., Glaspole, I., Goh, N., Grainge, C., Hopkins, P., Moodley, Y., Vidya, N., Reynolds, P., Corte, T., (Abstract) Artificial Intelligence- based Decision Support for HRCT Stratification in Fibrotic Lung Disease: An International Study of 195 Observers From 43 Countries, <<AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE>>, 2024; 209 (Supplement): 1-2 [https://hdl.handle.net/10807/324461]
Artificial Intelligence- based Decision Support for HRCT Stratification in Fibrotic Lung Disease: An International Study of 195 Observers From 43 Countries
Calandriello, Lucio;
2024
Abstract
To evaluate a deep learning algorithm as a decision support tool, for classifying HRCT based on ATS/ERS/JRS/ALAT IPF guideline criteria (SOFIA), among an international group of radiologists and pulmonologists. Automated decision support used in conjunction with formal HRCT evaluation may improve the application of guideline-based HRCT diagnoses and in principle could be used both as a screening tool in clinical trials and in the initial evaluation of patients with pulmonary fibrosis.
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