Publications by authors named "Mohammad Hasanian"
Objectives: The ultrasound examination of the hip joint is performed in the static (Graf) technique in the lateral recumbent position and in the dynamic technique in the supine position. This study compares the two static and dynamic techniques and assesses the role of the patient's position in the examination of DDH.
Methods: This cross-sectional study was conducted in 2020-2021 at Akbar Hospital, Mashhad University of Medical Sciences, Iran.
Article Synopsis
- - The study explored the use of a deep learning model to predict COVID-19 patient outcomes based on chest CT images, aiming to improve its clinical application through deep privacy-preserving federated learning (DPFL).
- - A total of 3,055 patients from 19 medical centers were analyzed, with the data being divided for training, validation, and testing to evaluate model performance using metrics like accuracy and sensitivity.
- - The results showed that the centralized model achieved an accuracy of 76% and the DPFL model had an accuracy of 75%, with both models demonstrating similar specificity and comparable area under the curve (AUC) values, suggesting no significant statistical differences between the two approaches.
Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.
Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers.
Objectives: The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients.
Methods: In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images.