Pneumonia

Development and validation of a CT-based deep learning algorithm to augment non-invasive diagnosis of idiopathic pulmonary fibrosis.

RATIONALE: Non-invasive diagnosis of idiopathic pulmonary fibrosis (IPF) involves identification of usual interstitial pneumonia (UIP) pattern by computed tomography (CT) and exclusion of other known etiologies of interstitial lung disease (ILD). However, uncertainty in identification of radiologic UIP pattern leads to the continued need for invasive surgical biopsy. We thus developed and validate...

Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning.

We proposed a self-supervised machine learning method to automatically rate the severity of pulmonary edema in the frontal chest X-ray radiographs (CXR) which could be potentially related to COVID-19 viral pneumonia. For this we use the modified radiographic assessment of lung edema (mRALE) scoring system. The new model was first optimized with the simple Siamese network (SimSiam) architecture whe...

A Novel Deep Learning Model for Medical Report Generation by Inter-Intra Information Calibration.

Automatic generation of medical reports can provide diagnostic assistance to doctors and reduce their workload. To improve the quality of the generate...

Distilling BlackBox to Interpretable Models for Efficient Transfer Learning.

Building generalizable AI models is one of the primary challenges in the healthcare domain. While radiologists rely on generalizable descriptive rules...

Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis.

Computer vision and deep learning have the potential to improve medical artificial intelligence (AI) by assisting in diagnosis, prediction, and progno...

The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease.

To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed ...

scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science.

Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. W...

Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study.

BACKGROUND: Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19...

Testing the performance, adequacy, and applicability of an artificial intelligence model for pediatric pneumonia diagnosis.

BACKGROUND: Community-acquired Pneumonia (CAP) is a common childhood infectious disease. Deep learning models show promise in X-ray interpretation and...

Are there accurate and legitimate ways to machine-quantify predatoriness, or an urgent need for an automated online tool?

Yamada and Teixeira da Silva voiced valid concerns with the inadequacies of an online machine learning-based tool to detect predatory journals, and st...

Improving chest X-ray report generation by leveraging warm starting.

Automatically generating a report from a patient's Chest X-rays (CXRs) is a promising solution to reducing clinical workload and improving patient car...

Development and validation of a hybrid deep learning-machine learning approach for severity assessment of COVID-19 and other pneumonias.

The Coronavirus Disease 2019 (COVID-19) is transitioning into the endemic phase. Nonetheless, it is crucial to remain mindful that pandemics related t...

Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation.

OBJECTIVES: To assess whether a computer-aided detection (CADe) system could serve as a learning tool for radiology residents in chest X-ray (CXR) int...

A Year at the Forefront of Hydrostat Motion.

Currently, in the field of interdisciplinary work in biology, there has been a significant push by the soft robotic community to understand the motion...

Toward Community-Based Natural Language Processing (CBNLP): Cocreating With Communities.

Rapid development and adoption of natural language processing (NLP) techniques has led to a multitude of exciting and innovative societal and health c...

Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms.

The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple or...

An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration.

Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from th...

Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup.

In this white paper, the ACR Pediatric AI Workgroup of the Commission on Informatics educates the radiology community about the health equity issue of...

Robotic ultrasound imaging: State-of-the-art and future perspectives.

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiati...

Propensity-matched analysis of robotic versus sternotomy approaches for mitral valve replacement.

To compare early and medium-term outcomes between robotic and sternotomy approaches for mitral valve replacement (MVR). Clinical data of 1393 cases wh...