Collaborating Authors

An Approach to Intelligent Pneumonia Detection and Integration Artificial Intelligence

Each year, over 2.5 million people, most of them in developed countries, die from pneumonia [1]. Since many studies have proved pneumonia is successfully treatable when timely and correctly diagnosed, many of diagnosis aids have been developed, with AI-based methods achieving high accuracies [2]. However, currently, the usage of AI in pneumonia detection is limited, in particular, due to challenges in generalizing a locally achieved result. In this report, we propose a roadmap for creating and integrating a system that attempts to solve this challenge. We also address various technical, legal, ethical, and logistical issues, with a blueprint of possible solutions.

Diagnosing Pneumonia from Chest X-Rays by Image-Based Deep Learning using Neural Networks


This article is to set up the framework with a simple model with a detailed walk through of each step. There are tons of improvements that can be made to boost model performance! In the world of healthcare, one of the major issues that medical professionals face is the correct diagnosis of conditions and diseases of patients. Not being able to correctly diagnose a condition is a problem for both the patient and the doctor. The doctor is not benefiting the patient in the appropriate way if the doctor misdiagnoses the patient.

This prevents pneumonia?

FOX News

Aside from protecting your pearly whites, here's more motivation to squeeze in that twice-a-year teeth cleaning: It could keep you from getting sick. A new study suggests that regular dental visits may protect against pneumonia by reducing levels of harmful bacteria in the mouth (ick). The study's findings--based on the health records of more than 26,000 people nationwide--suggest that people who never get dental checkups have a far greater risk of getting bacterial pneumonia than those who keep up with biannual visits. "There is a well-documented connection between oral health and pneumonia," said lead author Michelle Doll, MD, assistant professor of internal medicine at Virginia Commonwealth University, in a press release. "We can never rid the mouth of bacteria altogether, but good oral hygiene can limit the quantities of bacteria present."

Confounding variables can degrade generalization performance of radiological deep learning models Machine Learning

Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis in real-world clinical settings, we must verify their ability to generalize across a variety of hospital systems. A cross-sectional design was used to train and evaluate pneumonia screening CNNs on 158,323 chest x-rays from NIH (n=112,120 from 30,805 patients), Mount Sinai (42,396 from 12,904 patients), and Indiana (n=3,807 from 3,683 patients). In 3 / 5 natural comparisons, performance on chest x-rays from outside hospitals was significantly lower than on held-out x-rays from the original hospital systems. CNNs were able to detect where an x-ray was acquired (hospital system, hospital department) with extremely high accuracy and calibrate predictions accordingly. The performance of CNNs in diagnosing diseases on x-rays may reflect not only their ability to identify disease-specific imaging findings on x-rays, but also their ability to exploit confounding information. Estimates of CNN performance based on test data from hospital systems used for model training may overstate their likely real-world performance.