AIMC Topic: Radiography

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Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence.

BMC oral health
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.

Application of symmetry evaluation to deep learning algorithm in detection of mastoiditis on mastoid radiographs.

Scientific reports
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...

Artificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial Algorithms.

Seminars in roentgenology
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...

Enhancing Multi-disease Diagnosis of Chest X-rays with Advanced Deep-learning Networks in Real-world Data.

Journal of digital imaging
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...

Development and validation of a deep learning model using convolutional neural networks to identify femoral internal fixation device in radiographs.

Skeletal radiology
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.

Deep learning-based automatic sella turcica segmentation and morphology measurement in X-ray images.

BMC medical imaging
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...

Automated deep learning for classification of dental implant radiographs using a large multi-center dataset.

Scientific reports
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...

Domain-guided data augmentation for deep learning on medical imaging.

PloS one
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 ...

Introduction to Radiomics and Artificial Intelligence: A Primer for Radiologists.

Seminars in roentgenology
Health informatics and artificial intelligence (AI) are expected to transform the healthcare enterprise and the future practice of radiology. There is an increasing body of literature on radiomics and deep learning/AI applications in medical imaging....