Deep-learning-based automatic detection of pulmonary nodules from chest radiographs

Abstract

Objective: To assess a deep learning-based artificial intelligence model for the detection of pulmonary nodules on chest radiographs and to compare its performance with board-certified human readers. Methods: For this retrospective study, 308 chest radiographs were obtained between January 2019 to December 2021 from a tertiary care hospital. All radiographs were analyzed using a deep learning AI model called DxNodule AI Screen. Two expert board-certified radiologists established the ground truth, and 11 test readers independently reviewed all radiographs in two sessions (unaided and AI-aided mode) with a washout period of one month. Results: The standalone model had an AUROC of 0.905 [0.87, 0.94] in detecting pulmonary nodules. The mean AUROC across the 11 readers improved from 0.798 [0.74, 0.86] for unaided interpretation to 0.846 [0.82, 0.880] for AI-aided interpretation. With DxNodule AI Screen, readers were able to identify nodules at the correct locations, which they otherwise missed. The mean specificity, accuracy, PPV, and NPV of the readers improved significantly from 0.87 [0.78, 0.96], 0.78 [0.72, 0.84], 0.77 [0.65, 0.88], and 0.86 [0.81, 0.90] in the unaided session to 0.89 [0.82, 0.96], 0.83 [0.80, 0.85], 0.82 [0.73, 0.9], and 0.89 [0.86, 0.92], respectively in the aided session. Conclusion: DxNodule AI Screen outperformed human readers in nodule detection performance on chest radiographs, and enhanced human readers' performances when used as an aid.

Evaluation of the Performance of an Artificial Intelligence (AI) Algorithm in Detecting Thoracic Pathologies on Chest Radiographs

“…In 2022, Kufel et al concluded in their systematic review that, with appropriate modifications, the developed AI tools could be utilized to diagnose other diseases related to chest organs in the future [ 9 ]. This hypothesis has now been confirmed, as multiple deep-learning (DL) technologies are currently being used to assist in triaging and detecting different chest anomalies, including pneumonia, pleural effusion, pulmonary nodules, tuberculosis, and pneumothorax [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Rayvolve, developed by AZmed (Paris, France) is a DL-based AI tool developed in a multi-centric environment.…”