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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Updated: Jun 30, 2025

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

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

Published on: December 19, 2020

Development and external validation of a deep learning-based computed tomography classification system for COVID-19.

Yuki Kataoka^1,2,3,4, Tomohisa Baba⁵, Tatsuyoshi Ikenoue^6,7

¹Department of Internal Medicine, Kyoto Min-Iren Asukai Hospital.

Annals of Clinical Epidemiology

|March 20, 2024

Summary

A new machine learning model shows high sensitivity for classifying CT scans for COVID-19 (SARS-CoV-2). This tool may help emergency departments rule out the virus quickly, though specificity needs improvement.

Keywords:

COVID-19 COVID-19 Nucleic Acid Testing Computer-Assisted Deep Learning Diagnosis Tomography X-Ray Computed

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Area of Science:

Radiology
Artificial Intelligence
Infectious Disease

Background:

Developing accurate diagnostic tools for COVID-19 is crucial.
Computed tomography (CT) imaging is widely used for COVID-19 diagnosis.
Machine learning (ML) offers potential for automated image analysis.

Purpose of the Study:

To develop and externally validate a novel machine learning model for classifying CT findings related to SARS-CoV-2.
To assess the model's performance in identifying COVID-19 positive cases.

Main Methods:

Utilized 2,928 images for model development and internal validation (633 COVID-19, 2,295 non-COVID-19).
Externally validated the model on 893 images from 740 patients suspected of COVID-19.
Used reverse transcription polymerase chain reaction (RT-PCR) as the reference standard.

Main Results:

In external validation, the model achieved a sensitivity of 0.869 at a low cutoff and 0.724 at a high cutoff.
Specificities were 0.432 (low cutoff) and 0.721 (high cutoff).
Area under the receiver operating characteristic curve was 0.76.

Conclusions:

The machine learning model demonstrates high sensitivity in external validation datasets.
The model may aid physicians in rapidly ruling out COVID-19 diagnoses in emergency settings.
Further research is needed to enhance the model's specificity.