Multicenter Chest Radiographic Findings from Ethiopia with Radiologist-Generated Annotations

Abstract Background Chest radiography is widely used in Ethiopia for the evaluation of respiratory and cardiac disease. However, chest X-ray datasets used to develop and benchmark artificial intelligence (AI) systems are predominantly derived from high-income settings, which may limit generalizability and obscure region-specific radiographic patterns. Purpose To describe multicenter patterns of chest X-ray imaging findings in Ethiopia using the Afro-Chest X-ray cohort and to summarize the radiologist-led workflow used to generate high-quality localization labels. Materials and Methods This retrospective multicenter study included deidentified chest X-rays acquired at 10 Ethiopian institutions from December 2022 through July 2025. After quality filtering, 55,409 chest X-rays were retained. Radiology reports were standardized using the Afro-Chest X-ray reporting template (Table 5 ). A stratified subset of 11,880 chest X-rays was manually annotated by 11 radiologists using bounding boxes for 19 thoracic findings (Table 3) with recorded confidence levels. Finding patterns was summarized descriptively using counts and proportions at the exam and finding-instance levels. Results Among 55,409 chest X-rays, 31,939 were linked to radiology reports from 48,962 patients (male, 18,324 [37.4%]; female, 30,387 [62.1%]). In the annotated subset (11,880 chest X-rays), 7,003 (58.9%) were abnormal and contained 22,531 labeled finding instances (mean, 3.22 instances per abnormal chest X-ray). The most frequent findings were reticulonodular or ground-glass opacities (n = 6,896, 30.6%), pleural effusion (n = 2,685, 11.9%), cardiomegaly or chamber enlargement (n = 2,161, 9.6%), fibrosis or fibrobronchiectatic change (n = 1,869, 8.3%), and consolidation with cavitary lesions (n = 1,835, 8.1%). Conclusion In this multicenter Ethiopian cohort, annotated abnormalities were dominated by parenchymal opacities, pleural effusion, and cardiomegaly. Afro-Chest X-ray provides a radiologist-verified reference for describing regional chest X-ray patterns and supporting the local validation and development of AI systems applicable to East and other sub-Saharan African clinical settings.