AI Article Synopsis

  • The study developed a deep-learning AI model to accurately estimate the amount of pneumothorax using chest radiographs, aiding in diagnosing and treating this condition.
  • The model achieved high accuracy (97.8%) and showed good agreement with the gold standard measurements, indicating its potential effectiveness.
  • In testing, the average pneumothorax amounts predicted by the AI were closely aligned with the gold standard, revealing only a minor difference, but still indicated room for improvement compared to actual measurements.

Article Abstract

Artificial intelligence (AI) techniques can be a solution for delayed or misdiagnosed pneumothorax. This study developed, a deep-learning-based AI model to estimate the pneumothorax amount on a chest radiograph and applied it to a treatment algorithm developed by experienced thoracic surgeons. U-net performed semantic segmentation and classification of pneumothorax and non-pneumothorax areas. The pneumothorax amount was measured using chest computed tomography (volume ratio, gold standard) and chest radiographs (area ratio, true label) and calculated using the AI model (area ratio, predicted label). Each value was compared and analyzed based on clinical outcomes. The study included 96 patients, of which 67 comprised the training set and the others the test set. The AI model showed an accuracy of 97.8%, sensitivity of 69.2%, a negative predictive value of 99.1%, and a dice similarity coefficient of 61.8%. In the test set, the average amount of pneumothorax was 15%, 16%, and 13% in the gold standard, predicted, and true labels, respectively. The predicted label was not significantly different from the gold standard ( = 0.11) but inferior to the true label (difference in MAE: 3.03%). The amount of pneumothorax in thoracostomy patients was 21.6% in predicted cases and 18.5% in true cases.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406694PMC
http://dx.doi.org/10.3390/diagnostics12081823DOI Listing

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