Application of Deep Learning-Based Artificial Intelligence Model in Lung Ultrasound for Pediatric Lobar Pneumonia

Background Pneumonia remains a major contributor to global childhood morbidity and mortality, posing significant public health challenges. Lung ultrasound (LUS) serves as a critical tool for phased assessment of pneumonia progression and guidance of clinical management. This study developed a deep learning artificial intelligence (AI) model (LunNet) to automatically identify and precisely segment lesion characteristics in LUS images, aiming to assist ultrasound physicians in accurate lesion measurement for longitudinal disease monitoring and treatment guidance. Methods We retrospectively analyzed 419 pediatric patients diagnosed with lobar pneumonia (male : female, 199:220; mean age 7.1 ± 3.0 years) who underwent LUS examinations between May 2024 and December 2024. A total of 1,383 images from this cohort were used for LunNet (modified U-Net) development and validation. The model's lesion segmentation performance was evaluated using the dice coefficient and compared with the performance of ultrasound physicians. Results LunNet demonstrated robust performance in automatically identifying and segmenting lung consolidation, B-lines, and pleural effusion, achieving mean dice coefficients of 0.8401 (95% CI: 0.8191–0.8610), 0.8274 (95% CI: 0.7874–0.8673), and 0.8140 (95% CI: 0.7808–0.8472), respectively. The segmentation performance for lung consolidation exhibited marked disparity between junior and senior ultrasound physicians, with mean dice coefficients of 0.6946 (95% CI: 0.6312–0.7581) and 0.9441 (95% CI: 0.9352–0.9530), respectively. Notably, when assisted by LunNet, junior ultrasound physicians exhibited substantial improvement in lung consolidation segmentation, attaining a mean dice coefficient of 0.9221 (95% CI: 0.8191–0.8610), (P < 0.001). In the generalizability validation experiment, LunNet maintained competent performance for lung consolidation segmentation with a Dice coefficient of 0.7773 (95% CI: 0.9108–0.9335). Conclusion The LunNet AI model demonstrates excellent segmentation capabilities for pediatric lobar pneumonia lesions in ultrasound imaging. It effectively assists ultrasound physicians in precise quantification of pathological findings and significantly enhances diagnostic efficiency for novice practitioners. These results underscore LunNet's potential clinical value in supporting diagnosis, longitudinal monitoring, and therapeutic decision-making for lobar pneumonia.