Automated Echocardiographic Assessment of Dilated Cardiomyopathy Using Deep Learning in a Resource-Limited Setting
Deogratias Mzurikwao, Sophia Ahmed, Mahadia Tunga, Lulu Fundikira, Asa Kalonga, Paschal Kondi, Henry Mayala, Peter Kisenge, Gladness Urassa, William Jaluo, Jimson Alex, Sang’udi Sang’udi, Edna Mwacha, Asmin Issa, Simeon Mayala, Charles Okanda Nyatega, Zacharia Mzurikwao, Lulu Sakafu, Pilly Chillo, Gideon Kwesigabo, Barikiel Israel Panga, Bruno Sunguya, Nicoline van den Berg, Appolinary KamuhabwaAbstract
Deep learning models may lose performance when echocardiographic acquisition characteristics differ between training and deployment settings, a challenge for AI echocardiography in low- and middle-income countries (LMICs). We evaluated a U-Net deep learning framework for automated left ventricular (LV) segmentation and ejection fraction (EF) estimation using real-world echocardiographic data from 186 patients with dilated cardiomyopathy (DCM) at the Jakaya Kikwete Cardiac Institute (JKCI), Tanzania. We assessed how models developed on established echocardiographic datasets, the CAMUS still-image dataset and the EchoNet-Dynamic video dataset, would generalise to this local clinical dataset, and to what extent transfer learning could mitigate domain shift. The CAMUS-trained still-image model produced fragmented predictions on JKCI data that were unsuitable for LV volume estimation. Fine-tuning the EchoNet-Dynamic model reduced EF mean absolute error (MAE) from 7.21% to 4.63% and root mean square error (RMSE) from 9.84% to 6.66%. LV segmentation achieved Dice similarity coefficients of 0.90 at end-systole and 0.84 at end-diastole. Because the models differed in architecture, input modality, and adaptation, these results do not isolate the independent effect of video input or temporal encoding. The findings support further evaluation of locally adapted echocardiography models in resource-limited settings.