Advancements In AI-Assisted Imaging For Early Detection Of Pulmonary Nodules In High-Risk Populations: A Retrospective Study

Abstract

Background : Pulmonary nodules are critical indicators of early-stage lung cancer, particularly in high-risk populations. Advancements in imaging technologies, including low-dose computed tomography (LDCT) and artificial intelligence (AI), have enhanced the accuracy and timeliness of detection, offering improved patient outcomes. This study evaluates imaging techniques and their diagnostic impact in high-risk individuals.
Study Design : A Retrospective Study.
Place and duration of study: pulmonology MTI lady reading hospital Peshawar from July 2021 to Dec 2021
Objectives : To assess the efficacy of advanced imaging modalities, including LDCT and AI-driven analysis, in detecting pulmonary nodules in high-risk populations, and to evaluate patient outcomes and diagnostic accuracy.
Methods : This retrospective cohort study analyzed 150 patients from high-risk groups undergoing LDCT screening between 2015 and 2020. AI-assisted analysis was used for nodule characterization. Data on patient demographics, nodule size, malignancy likelihood, and follow-up outcomes were collected. Statistical analysis included mean age, standard deviation, and p-values to assess diagnostic efficiency and patient outcomes.
Results: Among 150 patients, 65% were male. The mean age was 62.4 years (SD ± 8.1). Nodules were detected in 38% of patients, with 12% confirmed malignant. LDCT combined with AI showed a sensitivity of 94% for detecting malignant nodules. Significant differences were noted between malignant and benign nodules (p < 0.01). AI analysis reduced false positives by 25% compared to manual radiological review, enhancing specificity. Early-stage lung cancer diagnoses increased by 18% in high-risk patients screened with LDCT.
Conclusion : Advanced imaging techniques, particularly LDCT with AI integration, significantly improve pulmonary nodule detection in high-risk populations, leading to earlier lung cancer diagnoses and better patient outcomes. The study underscores the importance of adopting these technologies in routine screening programs. Continued research is necessary to optimize protocols and address cost-effectiveness and accessibility challenges.