AI-based software for lung nodule detection in chest X-rays -- Time for a second reader approach?

• URL: http://arxiv.org/abs/2206.10912v1
• Date: Wed, 22 Jun 2022 08:35:04 GMT
• Title: AI-based software for lung nodule detection in chest X-rays -- Time for a second reader approach?
• Authors: Susanne Ohlmann-Knafo, Naglis Ramanauskas, Sebastian Huettinger, Emil Johnson Jeyakumar, Darius Baru\v{s}auskas, Neringa Bielskien\.e, Vytautas Naujalis, Jonas Bialopetravi\v{c}ius, Jonas Ra\v{z}anskas, Art\=uras Samuilis, J\=urat\.e Dementavi\v{c}ien\.e, Dirk Pickuth
• Abstract summary: The Japanese Society of Radiological Technology database was analyzed. Both AI modes -- automated and assisted -- produced an average increase in sensitivity. Both AI modes flagged the pulmonary nodules missed by radiologists in a significant number of cases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objectives: To compare artificial intelligence (AI) as a second reader in detecting lung nodules on chest X-rays (CXR) versus radiologists of two binational institutions, and to evaluate AI performance when using two different modes: automated versus assisted (additional remote radiologist review). Methods: The CXR public database (n = 247) of the Japanese Society of Radiological Technology with various types and sizes of lung nodules was analyzed. Eight radiologists evaluated the CXR images with regard to the presence of lung nodules and nodule conspicuity. After radiologist review, the AI software processed and flagged the CXR with the highest probability of missed nodules. The calculated accuracy metrics were the area under the curve (AUC), sensitivity, specificity, F1 score, false negative case number (FN), and the effect of different AI modes (automated/assisted) on the accuracy of nodule detection. Results: For radiologists, the average AUC value was 0.77 $\pm$ 0.07, while the average FN was 52.63 $\pm$ 17.53 (all studies) and 32 $\pm$ 11.59 (studies containing a nodule of malignant etiology = 32% rate of missed malignant nodules). Both AI modes -- automated and assisted -- produced an average increase in sensitivity (by 14% and 12%) and of F1-score (5% and 6%) and a decrease in specificity (by 10% and 3%, respectively). Conclusions: Both AI modes flagged the pulmonary nodules missed by radiologists in a significant number of cases. AI as a second reader has a high potential to improve diagnostic accuracy and radiology workflow. AI might detect certain pulmonary nodules earlier than radiologists, with a potentially significant impact on patient outcomes.

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