Visual and Predictive Assessment of Pneumothorax Recurrence in Adolescents Using Machine Learning on Chest CT

• Published in: Journal of clinical medicine Vol. 14; no. 17; p. 5956
• Main Authors: Hyun, Kwanyong, Kim, Jae Jun, Im, Kyong Shil, Han, Sang Chul, Ryu, Jeong Hwan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.08.2025
• Subjects: Adolescent medicine; Algorithms; Clinical medicine; CT imaging; Datasets; Deep learning; Diseases; Health aspects; Hemothorax; Machine learning; Medical imaging; Neural networks; Pediatric research; Pneumothorax; Prognosis; Python; Regression analysis; Relapse; Risk factors; Software; Surgery; Technology application; Teenagers; South Korea; United States > US; prediction; machine learning; spontaneous pneumothorax; chest computed tomography
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm14175956

Background: Spontaneous pneumothorax (SP) in adolescents has a high recurrence risk, particularly without surgical treatment. This study aimed to predict recurrence using machine learning (ML) algorithms applied to chest computed tomography (CT) and to visualize CT features associated with recurrence. Methods: We retrospectively reviewed 299 adolescents with conservatively managed SP from January 2018 to December 2022. Clinical risk factors were statistically analyzed. Chest CT images were evaluated using ML models, with performance assessed by AUC, accuracy, precision, recall, and F1 score. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for visual interpretation. Results: Among 164 right-sided and 135 left-sided SP cases, recurrence occurred in 54 and 43 cases, respectively. Mean recurrence intervals were 10.5 ± 9.9 months (right) and 12.7 ± 9.1 months (left). Presence of blebs or bullae was significantly associated with recurrence (p < 0.001). Neural networks achieved the best performance (AUC: 0.970 right, 0.958 left). Grad-CAM confirmed the role of blebs/bullae and highlighted apical lung regions in recurrence, even in their absence. Conclusions: ML algorithms applied to chest CT demonstrate high accuracy in predicting SP recurrence in adolescents. Visual analyses support the clinical relevance of blebs/bullae and suggest a key role of apical lung regions in recurrence, even when blebs/bullae are absent.