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Classification of Illness

Classification of Illness

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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Related Experiment Video

Updated: Nov 7, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification.

Mikhail Goncharov¹, Maxim Pisov², Alexey Shevtsov³

¹Skolkovo Institute of Science and Technology, Moscow, Russia; Kharkevich Institute for Information Transmission Problems, Moscow, Russia.

Medical Image Analysis

|May 1, 2021

Summary

This summary is machine-generated.

This study introduces a deep learning model for prioritizing COVID-19 (coronavirus disease 2019) CT scans and assessing disease severity. The multitask approach effectively identifies COVID-19 cases and quantifies lung involvement, aiding in timely patient care.

Keywords:

COVID-19 Chest computed tomography Convolutional neural network Triage

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

Area of Science:

Radiology
Computer Science
Artificial Intelligence

Background:

Healthcare systems, including radiology departments, are overloaded by the COVID-19 pandemic.
Existing deep learning methods for CT analysis have not directly addressed study triage.
Prioritizing COVID-19 cases and assessing severity are crucial for efficient patient management.

Purpose of the Study:

To develop a deep learning model for simultaneous identification of COVID-19 and severity quantification from CT scans.
To formalize study triage as a computer science problem, addressing both patient prioritization and care allocation.
To investigate a multitask approach for improved performance compared to single-task models.

Main Methods:

A convolutional neural network utilizing a multitask approach was proposed, integrating classification and severity estimation.
The model was trained on approximately 1500 public CT studies and validated on a diverse holdout dataset of 123 chest CT scans.
The classification layers were strategically applied to a spatially detailed feature map within the U-Net architecture.

Main Results:

The multitask model achieved high performance in identifying COVID-19, with ROC AUC scores of 0.87±0.01 (vs. bacterial pneumonia), 0.93±0.01 (vs. cancerous nodules), and 0.97±0.01 (vs. healthy controls).
Excellent performance was observed in severity quantification, achieving a 0.97±0.01 Spearman Correlation.
The proposed model outperformed existing single-task approaches in both identification and quantification tasks.

Conclusions:

The developed multitask deep learning model offers an effective solution for prioritizing COVID-19 CT studies and quantifying disease severity.
This approach can significantly aid in early patient isolation and directing severe cases to appropriate medical care.
The study highlights the benefit of a multitask strategy and specific architectural choices in deep learning for medical image analysis.