Role Of AI in Early Detection of Lung Nodules on HRCT

Background: Early detection of pulmonary nodules plays a crucial role in reducing lung cancer–related mortality. High-resolution computed tomography (HRCT) is the imaging modality of choice for identifying lung nodules; however, detection of small or subtle nodules is subject to inter-observer variability and radiologist fatigue. Artificial intelligence (AI)–based algorithms have emerged as promising tools to enhance diagnostic accuracy and efficiency.

Aim: To evaluate the role of artificial intelligence in the early detection of lung nodules on HRCT and to compare its diagnostic performance with conventional radiologist interpretation.

Materials and Methods: This prospective observational study was conducted in the Department of Radiology at a tertiary care center. HRCT chest scans of patients clinically suspected of pulmonary pathology were analyzed over a defined study period. Each scan was independently reviewed by experienced radiologists and by an AI-based lung nodule detection software. The number, size, location, and characteristics of detected nodules were recorded. Diagnostic performance parameters including sensitivity, specificity, and detection rate were compared between AI-assisted analysis and radiologist interpretation.

Results: AI-based analysis demonstrated a higher detection rate for small pulmonary nodules, particularly those measuring less than 6 mm, compared to manual interpretation. The combined approach of AI assistance and radiologist review improved overall sensitivity and reduced missed nodules. AI also reduced reporting time and inter-observer variability.

Conclusion: Artificial intelligence serves as a valuable adjunct to radiologists in the early detection of lung nodules on HRCT. Integration of AI into routine radiological workflow can enhance diagnostic accuracy, improve efficiency, and support early diagnosis of lung malignancies