Marcus Matheus Quadros Santos, Bianca Alessandra Gomes do Carmo, Taymara Barbosa Rodrigues, Bruna Rafaela Leite Dias, Cleyton Abreu Martins, Glenda Roberta Oliveira Naiff Ferreira, Andressa Tavares Parente, Cíntia Yollete Urbano Pauxis Aben-Atha, Sandra Helena Isse Polaro, Eliã Pinheiro Botelho
Spatial variability of mother-to-child human immunodeficiency virus transmission in a province in the Brazilian Rainforest: An ecological study
Geospatial health
reliability automated image interpretation pointofcare poc echocardiography scans depends quality acquired ultrasound data work reports development validation spatiotemporal deep learning models assess suitability input ultrasound cine loops collected using handheld echocardiography device processing automated quantification algorithm eg ejection fraction estimation poc echocardiograms n885 dicom cine loops 175 patients two sites collected using handheld ultrasound device annotated image quality framelevel attributes highquality frames left ventricular lv quantification included temporallystable lv reasonable coverage lv borders good contrast borders chamber attributes lowquality frames included temporal instability lv andor imaging artifacts eg lack contrast haze reverberation acoustic shadowing three different neural network architectures investigated framelevel convolutional neural network cnn operates individual echo frames vectorcnn b singlestream sequencelevel cnn operates sequence echo frames vectorcnnlstm c twostream sequencelevel cnns operate sequence echo optical flow frames vectorcnnlstmaverage vectorcnnlstmminmax vectorcnnlstmconvpool evaluation sequestered test dataset containing 76 dicom cine loops 16914 frames showed vectorcnnlstm effectively utilize spatial temporal information regress quality input frame accuracy 0925 sensitivity 0860 specificity 0952 compared framelevel vectorcnn utilizes spatial information frame accuracy 0903 sensitivity 0791 specificity 0949 furthermore independent sample ttest indicated cine loops classified adequate quality vectorcnnlstm model statistically significant lower bias automatically estimated ef mean bias 373 746 versus clinically obtained reference ef compared loops classified inadequate mean bias 1592 1217 p 0007 thus cine loop stratification using proposed spatiotemporal cnn model improves reliability automated pointofcare echocardiography image interpretation