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Related Concept Videos

Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
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Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

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Related Experiment Video

Updated: Dec 7, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

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COVID-19: Recovery Models for Radiology Departments.

Steven Guitron1, Oleg S Pianykh2, Marc D Succi3

  • 1Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.

Journal of the American College of Radiology : JACR
|September 26, 2020
PubMed
Summary
This summary is machine-generated.

Radiology departments can use three new models to predict imaging volumes during the COVID-19 pandemic. These tools help plan for recovery and manage future surges in demand for imaging services.

Keywords:
COVID-19predictive modelsrecovery planningsupply and demand

Related Experiment Videos

Last Updated: Dec 7, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

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Area of Science:

  • Radiology and Medical Imaging
  • Epidemiology
  • Healthcare Management

Background:

  • The COVID-19 pandemic significantly disrupted healthcare services, leading to reduced demand for elective imaging and interventional procedures.
  • Accurate forecasting of imaging volumes is crucial for effective radiology department planning and recovery strategies.

Purpose of the Study:

  • To develop and present three distinct models for predicting imaging volumes during the COVID-19 pandemic.
  • To provide tools for radiology planning and recovery at various stages of an unpredictable pandemic.

Main Methods:

  • A long-term volume model using historical data and outbreak analogues for initial pandemic planning.
  • A short-term volume model employing a supply-demand approach with real-time COVID-19 data for weekly predictions.
  • A next-wave model designed to estimate the impact of potential future COVID-19 surges.

Main Results:

  • The study presents three adaptable models applicable throughout the pandemic timeline.
  • These models offer predictive insights for both immediate and long-term radiology service management.
  • The models aim to assist in navigating the unpredictable fluctuations in imaging demand.

Conclusions:

  • The developed models provide a framework for radiology departments to manage and plan for fluctuating imaging volumes.
  • These predictive tools are essential for adapting to the ongoing challenges posed by the COVID-19 pandemic.
  • The models offer flexibility for use at any point during a public health crisis affecting healthcare demand.