We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and achieve state of the art results on all 14 diseases.

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

What is an intensive care unit? A report of the task force of the World Federation of Societies of Intensive and Critical Care Medicine

Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population Chest X-rays are primarily used for the diagnosis of this disease However, even for a trained radiologist, it is a challenging task to examine chest X-rays There is a need to improve the diagnosis accuracy In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy Partial data augmentation techniques are employed to increase the training dataset in a balanced way The proposed weighted classifier is able to outperform all the individual models Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score The final proposed weighted classifier model is able to achieve a test accuracy of 9843% and an AUC score of 9976 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning.

Artificial intelligence applications for thoracic imaging

Lung Ultrasonography: A Viable Alternative to Chest Radiography in Children with Suspected Pneumonia?

The RAD-AID Conference on International Radiology for Developing Countries was an assembly of individuals and organizations interested in improving access to medical imaging services in developing countries where the availability of radiology has been inadequate for both patient care and public health programs. The purpose of the meeting was to discuss data, experiences, and models pertaining to radiology in the developing world and to evaluate potential opportunities for future collaboration. Conference participants included radiologists, technologists, faculty members of academic medical institutions, and leadership of nongovernmental organizations involved in international health care and social entrepreneurship. Four main themes from the conference are presented in this white paper as important factors for the implementation and optimization of radiology in the developing world: (1) ensuring the economic sustainability of radiologic services through financial and administrative training support of health care personnel; (2) designing, testing, and deploying clinical strategies adapted for regions with limited resources; (3) structuring and improving the role of American radiology residents interested in global health service projects; and (4) implementing information technology models to support digital imaging in the developing world.

White Paper Report of the RAD-AID Conference on International Radiology for Developing Countries: Identifying Challenges, Opportunities, and Strategies for Imaging Services in the Developing World

Aduke Thelwell

Papers

White Paper Report of the RAD-AID Conference on International Radiology for Developing Countries: Identifying Challenges, Opportunities, and Strategies for Imaging Services in the Developing World