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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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Related Experiment Video

Updated: Aug 7, 2025

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

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

Published on: April 13, 2013

Incorporating algorithmic uncertainty into a clinical machine deep learning algorithm for urgent head CTs.

Byung C Yoon¹, Stuart R Pomerantz^1,2, Nathaniel D Mercaldo³

¹Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.

|March 13, 2023

Summary

This summary is machine-generated.

Machine learning (ML) algorithms can now detect intracranial hemorrhage on head CT scans with high accuracy. By incorporating uncertainty, these ML tools may accelerate patient management for critical findings.

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Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Machine learning (ML) algorithms for diagnostic imaging often use dichotomous classifications, which may not account for uncertainty in findings.
Indeterminate imaging findings and algorithmic inferences can lead to substantial uncertainty in diagnoses.

Purpose of the Study:

To incorporate uncertainty awareness into an ML algorithm for detecting intracranial hemorrhage or other urgent abnormalities on head CT scans.
To evaluate the performance of this uncertainty-aware ML algorithm in a prospective cohort of head CTs.

Main Methods:

An ML algorithm was developed to classify head CT scans into high probability (IC+) and low probability (IC-) for intracranial hemorrhage or urgent abnormalities.
A prospective evaluation was conducted on 1000 consecutive noncontrast head CTs, with scans not classified as IC+ or IC- designated as No Prediction (NP).

Main Results:

The ML algorithm achieved a positive predictive value of 0.91 for IC+ cases and a negative predictive value of 0.94 for IC- cases.
IC+ cases showed significantly higher rates of admission (75%), neurosurgical intervention (35%), and 30-day mortality (10%) compared to IC- cases.
Among the 168 NP cases, 32% had intracranial hemorrhage or urgent abnormalities, indicating a group requiring further review.

Conclusions:

An ML algorithm incorporating uncertainty effectively classifies head CTs into clinically relevant groups with high predictive values.
This uncertainty-aware ML approach shows promise in accelerating the management of patients with potential intracranial hemorrhage or other urgent intracranial abnormalities.