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
develop validate deep learning algorithm automated segmentation key temporal bone structures clinical computed tomography ct data sets crosssectional study total 325 ct scans clinical database stateoftheart deep learning dl algorithm swinunetr used train prediction model rapid segmentation 9 key temporal bone structures data set 325 clinical cts data set manually annotated specialist serve ground truth data set randomly split training n 260 testing n 65 sets models performance objectively assessed external validation test set using metrics including dice balanced accuracy hausdorff distances processing time model achieved average dice coefficient 087 structures average balanced accuracy 094 average hausdorff distance 079 mm average processing time 91 seconds per ct present dl model automated simultaneous segmentation multiple structures within temporal bone cts achieved high accuracy according currently commonly employed objective analysis results demonstrate potential method improve preoperative evaluation intraoperative guidance otologic surgery