Technical challenges of imaging &amp; image-guided interventions in obese patients

Uppot, Raul N.

doi:10.1259/bjr.20170931

Cited by 77 publications

(70 citation statements)

References 28 publications

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“…However, perhaps a more pressing concern is that highlighted in the recent editorial in Obesity, namely, that appropriate resources for those with (severe) obesity are often inadequate in hospitals 7 ; for example, the access to imaging may be limited by the lack of machines able to accommodate patients with severe obesity 8 . If the more complex lifting and handling demands of those with obesity 9 are also factored in, there is a real risk that people with severe obesity may be seriously disadvantaged regarding health care.…”

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confidence: 99%

COVID‐19 and obesity

Finer¹,

2020

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confidence: 99%

COVID‐19 and obesity

Finer¹,

2020

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“…Although radiological imaging is a widely available, as well as affordable method, due to the dangers of ionizing radiation from CT and X-ray equipment, it is not suitable for frequent use, and there are other contraindications to this procedure. It is necessary to consider imaging pregnant women, other complications can be caused by the presence of metal objects, the inability of the patient to take a deep full breath or also to adapt the equipment for too thin or obese patients (Uppot 2018).…”

Section: Limitations Of the Used Methodologymentioning

confidence: 99%

Automatic detection of pneumonia in chest X-rays using Lobe deep residual network

Kvak¹,

K²

2021

Preprint

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One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. At a time when the speed and reliability of results, especially for COVID-19 positive patients, is important, the development of applications that would facilitate the work of untrained staff involved in the evaluation is also crucial. Our model takes the form of a simple and intuitive application, into which you only need to upload X-rays: tens or hundreds at once. In just a few seconds, the physician will determine the patient's diagnosis, including the percentage accuracy of the estimate. While the original idea was a mere binary classifier that could tell if a patient was suffering from pneumonia or not, in this paper we present a model that distinguishes between a bacterial disease, a viral infection, or a finding caused by COVID-19. The aim of this research is to demonstrate whether pneumonia can be detected or even spatially localized using a uniform, supervised classification.

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“…A patient's weight, abdominal girth, and distribution of fatty tissue must be taken into account when assessing whether a particular scanner or other type of imaging equipment can accommodate that patient. [6][7][8] Our analysis revealed that there are facilities in Pennsylvania that are not able to accommodate some patients due to their large size or weight. Reviewing the event details drew our attention to the fact that some patients were brought to an imaging department for a study without first being measured or with only their weight being measured.…”

Section: Imagingmentioning

confidence: 99%

How Safety Is Compromised When Hospital Equipment Is a Poor Fit for Patients Who Are Obese

Kukielka¹

2020

PatientSaf

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Obesity is common, serious, and costly, and according to recent data, its prevalence is on the rise in the United States. Event reports submitted to the Pennsylvania Patient Safety Reporting System (PA-PSRS) indicate that some healthcare facilities do not have the necessary equipment to monitor and care for some individuals in this patient population, leading to embarrassment for patients, delays in care, and injuries to patients. An analysis of 107 events related to monitoring and patient care for patients who are obese submitted to PA-PSRS from 2009 through 2018 showed that imaging equipment, especially MRI and CT scanners, was most often implicated in event reports (49.5%; 53 events); other equipment included stretchers (24.3%; 26 events) and wheelchairs (11.2%; 12 events). Events most often occurred in an imaging department (30.8%; 33 events) or a medical/surgical unit (21.5%; 23 events). Analysts determined that 80 events (74.8%) resulted in a delay in care and that 44 events (41.1%) resulted in temporary harm to the patient, including skin tears and abrasions. Healthcare providers may not be able to prevent delays in care resulting from the unavailability of adequate equipment for patients who are obese, but they may be able to prevent harm and embarrassment for patients through proactive assessment.

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Technical challenges of imaging & image-guided interventions in obese patients

Cited by 77 publications

References 28 publications

COVID‐19 and obesity

COVID‐19 and obesity

Automatic detection of pneumonia in chest X-rays using Lobe deep residual network

How Safety Is Compromised When Hospital Equipment Is a Poor Fit for Patients Who Are Obese

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