PLUTO: a YOLO-based lung field detector for pediatric lateral chest X-rays generalizable to adults.

Introduction: Lateral chest X-rays (CXRs) are very important for detecting tuberculosis (TB) in infants and children, particularly for assessing TB-related lymphadenopathy and intrathoracic structures that are obscured in frontal projections. Although deep learning (DL)-based artificial intelligence (AI) has advanced CXR analysis, lateral projection imaging remains largely unexplored. Lung field detection is a critical first step in such pipelines, enabling DL models to focus on the relevant anatomy and improving downstream tasks such as disease detection, classification, and clinical decision support. Methods: Our DL-based model, called Pediatric Lateral lUng deTection with yOlo (PLUTO), enables lateral lung field detection not only in pediatric CXRs, but also demonstrates cross-domain generalizability to adult lateral CXRs. Related to prior work in this emerging area, PLUTO advances the field through a systematic age-stratified evaluation framework and an explicit assessment of cross-domain transfer to adult images. PLUTO uses a YOLO11s detector backbone selected after evaluating multiple state-of-the-art YOLO11 variants through five-fold cross-validation on age-stratified pediatric CXRs. The test cohort includes internal pediatric hold-out data and external pediatric and adult CXRs. Results: PLUTO achieved strong performance, with mAP@[0.5:0.95] scores of 0.8816 ± 0.0061 (internal pediatric) and 0.8898 ± 0.0084 (external pediatric), and demonstrated preliminary cross-domain generalizability to adult images. However, confirmation at a larger scale remains an important direction for future work. PLUTO also improved zero-shot lateral lung field segmentation performance. Discussion: The PLUTO model provides a valuable resource for anatomically grounded AI in lateral pediatric TB imaging and will enhance research in pulmonary TB and related diseases.