RESUMEN
BACKGROUND: Lung ultrasound (LUS) can be an important imaging tool for the diagnosis and assessment of lung involvement. Ultrasound sonograms have been confirmed to illustrate damage to a person's lungs, which means that the correct classification and scoring of a patient's sonogram can be used to assess lung involvement. METHODS: The purpose of this study was to establish a lung involvement assessment model based on deep learning. A novel multimodal channel and receptive field attention network combined with ResNeXt (MCRFNet) was proposed to classify sonograms, and the network can automatically fuse shallow features and determine the importance of different channels and respective fields. Finally, sonogram classes were transformed into scores to evaluate lung involvement from the initial diagnosis to rehabilitation. RESULTS AND CONCLUSION: Using multicenter and multimodal ultrasound data from 104 patients, the diagnostic model achieved 94.39% accuracy, 82.28% precision, 76.27% sensitivity, and 96.44% specificity. The lung involvement severity and the trend of COVID-19 pneumonia were evaluated quantitatively.
Asunto(s)
COVID-19/diagnóstico por imagen , Pulmón/diagnóstico por imagen , Neumonía/diagnóstico por imagen , Ultrasonografía , Algoritmos , Bases de Datos Factuales , Reacciones Falso Positivas , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Modelos Estadísticos , Redes Neurales de la Computación , Lenguajes de Programación , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Programas InformáticosRESUMEN
The handheld ultrasound demonstrates clinical and economic value in combating COVID-19 based on interviews with frontline ultrasound physician and cardiologist as well as a national expert in medical ultrasound.