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A rapid and accurate diagnosis of cardiomegaly and pleural effusion is of theutmost importance to reduce mortality and medical costs. ArtificialIntelligence has shown promise in diagnosing medical conditions. With thisstudy, we seek to evaluate how well Artificial Intelligence (AI) systems,developed my minoHealth AI Labs, will perform at diagnosing cardiomegaly andpleural effusion, using chest x-rays from Ghana, Vietnam and the USA, and howwell AI systems will perform when compared with radiologists working in Ghana.The evaluation dataset used in this study contained 100 images randomlyselected from three datasets. The Deep Learning models were further tested on alarger Ghanaian dataset containing five hundred and sixty one (561) samples.Two AI systems were then evaluated on the evaluation dataset, whilst we alsogave the same chest x-ray images within the evaluation dataset to 4radiologists, with 5 - 20 years experience, to diagnose independently. Forcardiomegaly, minoHealth-ai systems scored Area under the Receiver operatingcharacteristic Curve (AUC-ROC) of 0.9 and 0.97 while the AUC-ROC of individualradiologists ranged from 0.77 to 0.87. For pleural effusion, the minoHealth-aisystems scored 0.97 and 0.91 whereas individual radiologists scored between0.75 and 0.86. On both conditions, the best performing AI model outperforms thebest performing radiologist by about 10%. We also evaluate the specificity,sensitivity, negative predictive value (NPV), and positive predictive value(PPV) between the minoHealth-ai systems and radiologists.

minoHealth.ai: A Clinical Evaluation Of Deep Learning Systems For the  Diagnosis of Pleural Effusion and Cardiomegaly In Ghana, Vietnam and the  United States of America