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An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader

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An artificial intelligence (AI) algorithm significantly increased the detection of pulmonary nodules requiring follow-up on ultra-low-dose computed tomography (ULDCT) scans. However, this also led to a substantial rise in false positives, particularly in patients with major abnormalities.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Pulmonary Medicine

Background:

  • Ultra-low-dose computed tomography (ULDCT) is increasingly used in emergency departments (ED) for suspected pulmonary disease.
  • The OPTIMACT trial investigated the utility of AI for detecting pulmonary nodules on ULDCT.
  • Accurate detection of incidental pulmonary nodules is crucial for early diagnosis and management.

Purpose of the Study:

  • To evaluate the added value of an artificial intelligence (AI) algorithm in detecting pulmonary nodules on ULDCT.
  • To compare the detection rates of pulmonary nodules by AI versus standard radiologist interpretation in the ED setting.
  • To assess the trade-off between true positive and false positive findings when using AI for nodule detection.

Main Methods:

  • Retrospective analysis of 870 patients from the OPTIMACT trial who underwent ULDCT.
  • Prospective reading by ED radiologists followed by post hoc analysis using an AI deep learning algorithm for nodule detection (≥6 mm).
  • Independent review by three chest radiologists to establish a true positive reference standard for both prospectively detected nodules and AI marks.

Main Results:

  • AI detected 5.8 times more true positive pulmonary nodules requiring follow-up compared to prospective ED radiologist reporting (104 vs. 18).
  • The use of AI resulted in a 42.9 times increase in false positive findings (1,758 vs. 41).
  • A median of 1 AI mark per ULDCT was observed, with false positives predominantly found in patients with major abnormalities.

Conclusions:

  • AI significantly enhances the detection of incidental pulmonary nodules on ULDCT in an emergency setting.
  • The increased detection rate comes with a substantial trade-off of a higher false positive rate.
  • AI may aid in early pulmonary cancer detection but requires careful consideration of its impact on workflow due to increased false positives.