Quantitative phase retrieval (QPR) in propagation-based x-ray phase contrast imaging of heterogeneous and structurally complicated objects is challenging under laboratory conditions due to partial spatial coherence and polychromaticity. A deep learni...
BACKGROUND: The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis.
As many human organs exist in pairs or have symmetric appearance and loss of symmetry may indicate pathology, symmetry evaluation on medical images is very important and has been routinely performed in diagnosis of diseases and pretreatment evaluatio...
There is a rapidly increasing number of artificial intelligence (AI) products cleared by the Food and Drug Administration (FDA) for quantification, identification, and even diagnosis in clinical radiology. This review article aims to summarize the la...
The current artificial intelligence (AI) models are still insufficient in multi-disease diagnosis for real-world data, which always present a long-tail distribution. To tackle this issue, a long-tail public dataset, "ChestX-ray14," which involved fou...
OBJECTIVE: The purpose of this study is to develop and validate a deep convolutional neural network (DCNN) model to automatically identify the manufacturer and model of hip internal fixation devices from anteroposterior (AP) radiographs.
BACKGROUND: Although the morphological changes of sella turcica have been drawing increasing attention, the acquirement of linear parameters of sella turcica relies on manual measurement. Manual measurement is laborious, time-consuming, and may intro...
This study aimed to evaluate the accuracy of automated deep learning (DL) algorithm for identifying and classifying various types of dental implant systems (DIS) using a large-scale multicenter dataset. Dental implant radiographs of pos-implant surge...
While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Our objective was to test whether domain-specific data augmentation is useful for medical ...
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