How an AI platform could help create better Screening Campaigns

The majority of global disease burden and mortality are caused by non-communicable diseases. However, a sizable portion of patients in both high-income nations and low-income and middle-income countries are still uninformed that they have these life threatening illnesses.

Many diagnostic systems are extremely costly, invasive, or need skilled healthcare professionals to operate, making their use in environments with limited resources impossible.
AI software for lung nodule evaluation utilizing deep learning (DL) could be able to recognize certain patterns in imaging data and provide outstanding results.

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How AI prevents burnout in healthcare

While the current literature is vague when it comes to explain burnout, the World Health Organization (WHO) defines it as “a syndrome conceptualized as a result of chronic stress at work, which has not been successfully managed.”

Burnout can manifest itself in several ways, such as feeling overwhelmed, cynicism, physical or emotional exhaustion, and inefficiency.

This phenomenon can lead to many health problems for radiologists, including sleep deprivation, substance abuse, depression and cardiovascular problems, among others. At the same time, there is a decrease in productivity, which can lead to medical errors.

For this problem to go away, we need to focus on improving collaboration in the workplace, assessing the risk factors that can cause burnout, and accepting the limitations of a radiologist’s abilities.

XVision was designed to assist radiologists in their mission. Our suite of X-ray imaging and CT scans is seamlessly integrated into your doctor’s and hospital’s workflow. It is an additional brain that allows you to increase the ability to analyze and reduce the amount of work.

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Evaluation of Patients with Pulmonary Nodules

One of the most frequent causes for patient referrals for pulmonary examination is the presence of lung nodules.

XVision’s software offers a recognition algorithm that enables radiologists to detect lung nodules and display them using an easy-to-read User-Interface, allowing higher accuracy in a shorter amount of time.

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How to develop an AI medical imaging platform

When thinking about the clinical validation of a medical system, one of the first factors that must be considered is how the system will be integrated into the clinical workflow.

In the initial years, software systems were mostly embedded in hardware medical devices, and thus the primary concern was guarding against the possibility of physical harm, with attention to aspects such as the transmission of energy and/or substances to or from the body, the degree of invasiveness, the closeness to sensitive organs, etc.
This was reflected in many of the regulatory directives and guidelines, which often did not offer specific indications for software developed as a standalone clinical aid. As the use of such applications steadily grew, the need for specific guidance was felt and several contributions were developed.

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Why are doctors afraid of AI? 

Artificial intelligence (AI) technology has advanced extremely fast in the last years, owing in great part to breakthroughs in machine learning, which is closely tied to data science and statistical prediction. Modeling is involved across several elements of the health sector, including diagnosing, treatment, management, and logistics. Because of the link between machine learning’s capabilities and the needs of the health-care space, it’s widely assumed that AI will have a significant impact.

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Pulmonary edema and early detection with XVision

Pulmonary edema, also known as pulmonary congestion, is excessive liquid accumulation in the tissue and air spaces (usually alveoli) of the lungs. It causes poor oxygen delivery, which can lead to hypoxemia and respiratory failure. It occurs when the left ventricle of the heart fails to appropriately remove oxygenated blood from the pulmonary circulation (cardiogenic pulmonary edema), or when lung tissue or blood vessels are injured directly (non-cardiogenic pulmonary edema)

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Teleradiology: the next step in smart healthcare 

One of the most difficult problems we are currently experiencing is figuring out how to manage remote workforces. Because the largest portion of IT enterprises operate remotely, the IT sector has emerged as a leading model for the rest of the globe. However, due to the pandemic’s obstacles, a substantial number of organizations are fighting to stay afloat, the medical field being of them. 

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There is a contrast enhanced CT shortage and AI could solve it 

Contrast CT, or contrast enhanced computed tomography (CECT), is a form of X-ray computed tomography using radiocontrast (substance that is injected into the body to allow the visualization of special structures such as blood vessels). Radiocontrast for CT is made generally from iodine-based compounds. This is important for highlighting features that are too difficult to distinguish from […]

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Human vs AI: How to Read a CT Scan like a pro 

In today’s blog post, we’ll continue the pulmonary medical imaging lesson that a radiologist learns for doing a chest computed tomography on a patient who may be experiencing lung issues.

CT is still the most commonly utilized and reviewed cross-sectional scanning technology for the thorax, and it is continuously evolving. Thoracic CT imaging techniques are adjusted to the clinical question and the structures of interest, much like any other imaging procedure.

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AI vs Human: How to read an X-ray like a pro

In today’s blog post we will learn what are the steps that a radiologist follows when he does a chest radiography for a patient who may be coming for pulmonary problems.  
It is the most common diagnostic visualization technique used to assess respiratory issues. Its popularity comes from a number of variables, including the fact that it is inexpensive, quick, and simple to conduct, produces almost no irradiation, and is both accessible and feasible to patients and doctors.

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