AIMC Topic: Radiography

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Identification of L5 vertebra on lumbar spine radiographs using deep learning.

The Journal of international medical research
OBJECTIVE: Deep learning is an advanced machine-learning approach that is used in several medical fields. Here, we developed a deep learning model using an object detection algorithm to identify the L5 vertebra on anteroposterior lumbar spine radiogr...

Present and Future Innovations in AI and Cardiac MRI.

Radiology
Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on ...

Revisiting the Trustworthiness of Saliency Methods in Radiology AI.

Radiology. Artificial intelligence
Purpose To determine whether saliency maps in radiology artificial intelligence (AI) are vulnerable to subtle perturbations of the input, which could lead to misleading interpretations, using prediction-saliency correlation (PSC) for evaluating the s...

Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: a qualitative study.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Artificial intelligence (AI)-based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizati...

[Automated cephalometric landmark identification and location based on convolutional neural network].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
To develop an automated landmark location system applicable to the case of landmark missing. Four and eighty-one lateral cephalograms, which contained 240 males and 241 females, with an average age of (24.5±5.6) years, taken from January 2015 to Ja...

Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels.

Korean journal of radiology
OBJECTIVE: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model.

Bone Age Estimation and Prediction of Final Adult Height Using Deep Learning.

Yonsei medical journal
PURPOSE: The appropriate evaluation of height and accurate estimation of bone age are crucial for proper assessment of the growth status of a child. We developed a bone age estimation program using a deep learning algorithm and established a model to...