Enhancing Diagnostic Accuracy in Lung Cancer: Evaluating the Performance of a Cutting-edge Artificial Intelligence Algorithm for Pulmonary Nodule Detection in Chest Radiographs

Background: Lung cancer remains a major global health challenge, and early diagnosis critically improves outcomes. Artificial Intelligence (AI) is increasingly used to enhance diagnostic accuracy in medical imaging, particularly for thoracic diseases. Objective: This study evaluated a cutting-edge AI algorithm for detecting pulmonary nodules in chest radiographs to improve early lung cancer diagnosis. Material and Methods: In this analytical and experimental investigation, two datasets were used: Group1 (112120 frontal-view chest radiographs labeled for 14 thoracic diseases, including nodules) and Group2 (800 radiographs evenly split into positive and negative cases, with expert consensus labeling). The proprietary AI algorithm is based on CheXNet, a 121-layer convolutional neural network pretrained on ImageNet to initialize weights, enabling transfer learning for superior feature extraction. This model was chosen for its validated performance in thoracic disease detection, particularly in chest X-ray analysis. The datasets were divided into Type A (combining Group1 and Group2) and Type B (using only Group2) to assess performance under different conditions. A five-fold cross-validation was employed. Results: The model achieved an Area Under the Curve (AUC) of 0.73 for Type A and 0.782 for Type B. Sensitivity and specificity metrics were 0.736 and 0.592 for Type A, and 0.716 and 0.734 for Type B, respectively. Notably, the algorithm outperformed radiologists in some cases, demonstrating its capability to detect subtle or challenging nodules. Conclusion: This AI algorithm shows promise as a clinical decision support tool, especially where access to experienced radiologists is limited. Further research is needed to refine the algorithm and validate use in real-world clinical workflows.