Pneumonia

Collimation border with U-Net segmentation on chest radiographs compared to radiologists.

INTRODUCTION: Chest Radiography (CXR) is a common radiographic procedure. Radiation exposure to patients should be kept as low as reasonably achievable (ALARA), and monitored continuously as part of quality assurance (QA) programs. One of the most effective dose reduction tools is proper collimation practice. The purpose of this study is to determine whether a U-Net convolutional neural networks (...

Can Machine Learning Be Better than Biased Readers?

Training machine learning (ML) models in medical imaging requires large amounts of labeled data. To minimize labeling workload, it is common to divide training data among multiple readers for separate annotation without consensus and then combine the labeled data for training a ML model. This can lead to a biased training dataset and poor ML algorithm prediction performance. The purpose of this s...

Pneumonia Detection Using Enhanced Convolutional Neural Network Model on Chest X-Ray Images.

Pneumonia, caused by microorganisms, is a severely contagious disease that damages one or both the lungs of the patients. Early detection and treatmen...

Collaborative training of medical artificial intelligence models with non-uniform labels.

Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robus...

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia.

BACKGROUND: The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widel...

MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion.

Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of such images. As a method to mitigate such artifacts, denois...

Leveraging deep learning to improve vaccine design.

Deep learning has led to incredible breakthroughs in areas of research, from self-driving vehicles to solutions, to formal mathematical proofs. In the...

Framework for dual-energy-like chest radiography image synthesis from single-energy computed tomography based on cycle-consistent generative adversarial network.

BACKGROUND: Dual-energy (DE) chest radiography (CXR) enables the selective imaging of two relevant materials, namely, soft tissue and bone structures,...

Deep Learning-Based Computer-Aided Detection System for Preoperative Chest Radiographs to Predict Postoperative Pneumonia.

RATIONALE AND OBJECTIVES: The role of preoperative chest radiography (CR) for prediction of postoperative pneumonia remains uncertain. We aimed to dev...

Automated prediction of COVID-19 severity upon admission by chest X-ray images and clinical metadata aiming at accuracy and explainability.

In the past few years COVID-19 posed a huge threat to healthcare systems around the world. One of the first waves of the pandemic hit Northern Italy s...

Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19.

INTRODUCTION: Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision l...

[Robotic esophageal surgery].

Robot-assisted minimally invasive esophagectomy (RAMIE) is increasingly becoming established as a standard procedure in surgical centers for esophagec...

Elective robotic partial colon and rectal resections: series of 170 consecutive robot procedures involving the Da Vinci Xi robot by a community general surgeon.

Robotic colorectal procedures may overcome the disadvantages of laparoscopic surgery. While the literature has multiple studies from specialized cente...

Developing an Implementation Model for ADHD Intervention in Community Clinics: Leveraging Artificial Intelligence and Digital Technology.

Implementation of behavior therapy for ADHD faces challenges in community settings. We describe development of a community-based implementation model ...

An Artificial Plant Community Algorithm for the Accurate Range-Free Positioning of Wireless Sensor Networks.

The problem of positioning wireless sensor networks is an important and challenging topic in all walks of life. Inspired by the evolution behavior of ...

Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.

BACKGROUND: There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions o...

Unsupervised anomaly detection for posteroanterior chest X-rays using multiresolution patch-based self-supervised learning.

The demand for anomaly detection, which involves the identification of abnormal samples, has continued to increase in various domains. In particular, ...

Challenges for enforcing editorial policies on AI-generated papers.

ChatGPT, a chatbot released by OpenAI in November 2022, has rocked academia with its capacity to generate papers "good enough" for academic journals. ...

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

BACKGROUND: An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19.

Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network.

The generative adversarial network (GAN) is a promising deep learning method for generating images. We evaluated the generation of highly realistic an...