Diagnostic Accuracy of an Offline CNN Framework Utilizing Multi-View Chest X-Rays for Screening 14 Co-Occurring Communicable and Non-Communicable Diseases

Background: Chest radiography is the most widely used diagnostic imaging modality. Yet its interpretation is challenged by limited radiology workforce, especially in low- and middle-income countries (LMICs). The interpretation is both time consuming and error-prone in high volume settings. Artificial Intelligence (AI) systems trained on public data may lack generalizability to multi-view, real-world, local images. Deep learning tools have the potential to augment radiologists by providing real-time decision support by overcoming these. Objective: We evaluated the diagnostic accuracy of a deep learning-based convolutional neural network (CNN) trained on multi-view, hybrid (public and local datasets) for detecting thoracic abnormalities in chest radiographs of adults presenting to a tertiary hospital. Methods: A CNN was pretrained on large public datasets (VinBig, NIH) and fine-tuned on adult chest radiographs both frontal [posteroanterior (PA) and anteroposterior (AP)] from Tribhuvan University Teaching Hospital TUTH, Nepal. The dataset included emergency (ER), ICU, and outpatient (OPD) radiographs. Data augmentation simulated poor-quality images and artifacts (ECG wires, text labels, rotation, low exposure). Fourteen thoracic pathologies were annotated by 3 radiologists. Bounding boxes were refined using Weighted Boxes Fusion (WBF) and displayed in 14 unique colors for interpretability. The system was evaluated on a held-out test set (N=522) against radiologist consensus. Primary outcomes included AUC, sensitivity, specificity, mean average precision (mAP), and reporting time. Deployment feasibility was tested on Picture Archiving and Communication System (PACS) and in offline standalone mode. Results: The CNN achieved an overall AUC of 0.86 across 14 abnormalities, with 68% sensitivity,99% specificity, and 0.93 mAP. Colored bounding boxes improved clarity when multiple pathologies co-occurred (e.g., cardiomegaly with effusion). The system performed effectively on PA, AP, and lateral views, including poor-quality ER/ICU images. Deployment testing confirmed seamless PACS integration and offline functionality. Conclusion: The CNN trained on adult CXRs performed reliably in detecting key thoracic findings across varied clinical settings. Its robustness to image quality, integration of multiple views and visualization capabilities suggest it could serve as a useful aid for triage and diagnosis.