Page 20 - Kaleidoscope Academic Conference Proceedings 2022

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

Darlington Akogo

minoHealth AI Labs, karaAgro AI, Runmila AI Institute, Ghana

A rapid and accurate diagnosis of medical conditions like cardiomegaly and pleural effusion is of the
utmost importance to reduce mortality and medical costs, and artificial intelligence has shown
promise in diagnosing medical conditions. We evaluated how well Artificial Intelligence (AI)
systems, developed by minoHealth AI Labs, perform at diagnosing cardiomegaly and pleural
effusion, using chest x-rays from Ghana, Vietnam and the USA, and how well AI systems perform
when compared with radiologists working in Ghana. The evaluation dataset used in this study
contained 100 images randomly selected from three datasets. The deep learning models were further
tested on a larger Ghanaian dataset containing 561 samples. Two AI systems were then evaluated on
the evaluation dataset, whilst we also gave the same chest x-ray images within the evaluation dataset
to four radiologists, with 5 - 20 years’ experience, to give their independent diagnoses.

For cardiomegaly, minoHealth.ai systems scored an Area Under the Receiver Operating
Characteristic Curve (AUC-ROC) of 0.9 and 0.97 while the AUC-ROC of individual radiologists
ranged from 0.77 to 0.87. For pleural effusion, the minoHealth.ai systems scored 0.97 and 0.91,
whereas individual radiologists scored between 0.75 and 0.86. On both conditions, the best
performing AI model outperforms the best performing radiologist by about 10%. These models will
be of great use in regions, such as sub-Saharan Africa, where there are few radiologists. They can
potentially be used to augment the effort of radiologists to improve the diagnosis and treatment of
chest conditions.

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