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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.
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Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning.

Fakrul Islam Tushar¹, Vincent M D'Anniballe¹, Rui Hou¹

¹Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology and Department of Electrical and Computer Engineering, Duke University, 2424 Erwin Rd, Studio 302, Durham, NC 27705 (F.I.T., R.H., M.A.M., W.F., E.S., J.Y.L.); Department of Radiology, Duke University, Durham, NC (V.M.D.); and Department of Medical Imaging, University of Arizona, Tucson, Ariz (G.D.R.).

Radiology. Artificial Intelligence

|February 11, 2022

Summary

This summary is machine-generated.

Weakly supervised deep learning models accurately classify multiple diseases across various organ systems using body CT scans. This approach leverages automatically extracted labels from radiology reports for improved diagnostic capabilities.

Keywords:

CT Diagnosis/Classification/Application Domain Semisupervised Learning Whole-Body Imaging

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Deep Learning for Medical Diagnosis

Background:

Radiology text reports contain valuable diagnostic information.
Automating disease label extraction from reports can facilitate large-scale analysis.
Deep learning models show promise in analyzing medical imaging data.

Purpose of the Study:

To develop multidisease classifiers for body CT scans.
To utilize automatically extracted labels from radiology reports for training.
To target three distinct organ systems: lungs/pleura, liver/gallbladder, and kidneys/ureters.

Main Methods:

A retrospective study of 12,092 patients with 13,667 CT scans (2012-2017).
Rule-based algorithms extracted 19,225 disease labels.
A 3D DenseVNet segmented organs, followed by a 3D CNN for classification into disease or no apparent disease categories.

Main Results:

Extracted labels showed 91%-99% accuracy upon manual validation.
Area Under the Curve (AUC) values for disease classification ranged from 0.62 to 0.97 across organ systems.
High AUCs were achieved for conditions like emphysema (0.89), effusion (0.97), and kidney atrophy (0.92).

Conclusions:

Weakly supervised deep learning models can effectively classify diverse diseases in multiple organ systems from CT scans.
The study demonstrates the feasibility of using automated report labels for training diagnostic AI models.
This approach holds potential for enhancing diagnostic efficiency and accuracy in radiology.