About Oxytech.io

The startup develops AI-driven diagnostic technologies for X-ray analysis, focusing on the detection of abnormalities, spine curvatures, dislocations, and fractures. By providing detailed assessments of X-ray images, the technology enhances radiologists' ability to interpret complex cases accurately and efficiently.

```xml <problem> Radiologists face challenges in efficiently and accurately interpreting X-ray images, particularly in detecting subtle abnormalities, spinal curvatures, dislocations, and fractures. Manual analysis can be time-consuming and prone to human error, potentially leading to delayed or inaccurate diagnoses. </problem> <solution> Oxytech provides AI-powered diagnostic tools for the automated analysis of X-ray images, enabling faster and more accurate detection of a range of conditions. The platform utilizes deep learning models trained on a large dataset of X-ray studies to identify abnormalities in chest, spine, sinuses, knees, and pelvis images. By automating the detection process and providing quantitative measurements, Oxytech aims to improve diagnostic accuracy, reduce reporting times, and enhance clinical decision-making. The solution is available through a web interface, API, and integration with PACS and DICOM storage systems. </solution> <features> - AI-driven analysis of chest X-rays for detecting multiple lung abnormalities in under 1 minute - Automated detection and measurement of spine curvatures and bone abnormalities in spine X-rays in under 30 seconds - AI-powered analysis of skull X-rays for sinusitis detection and sinus fullness percentage in under 30 seconds - Algorithms for analyzing knee and pelvis X-rays to assess degenerative processes in under 1 minute - API for integration into existing systems - Web-based access with a built-in DICOM viewer - Integration capabilities with PACS and DICOM storage systems - Option for an out-of-the-box solution that does not require a cloud connection </features> <target_audience> The primary target audience includes radiologists, medical institutions, and healthcare providers seeking to improve the efficiency and accuracy of X-ray image analysis. </target_audience> ```

What does Oxytech.io do?

Where is Oxytech.io located?

Oxytech.io is based in London, United Kingdom.

When was Oxytech.io founded?

Oxytech.io was founded in 2021.

How much funding has Oxytech.io raised?

Oxytech.io has raised 280000.

Location

London, United Kingdom

Founded

2021

Funding

280000

Employees

12 employees

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Oxytech.io

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Executive Summary

oxytech.io 300+

Founded 2021 – London, United Kingdom

Funding

Estimated Funding

$100K+

Team (10+)

No team information available.

Company Description

Problem

Radiologists face challenges in efficiently and accurately interpreting X-ray images, particularly in detecting subtle abnormalities, spinal curvatures, dislocations, and fractures. Manual analysis can be time-consuming and prone to human error, potentially leading to delayed or inaccurate diagnoses.

Solution

Oxytech provides AI-powered diagnostic tools for the automated analysis of X-ray images, enabling faster and more accurate detection of a range of conditions. The platform utilizes deep learning models trained on a large dataset of X-ray studies to identify abnormalities in chest, spine, sinuses, knees, and pelvis images. By automating the detection process and providing quantitative measurements, Oxytech aims to improve diagnostic accuracy, reduce reporting times, and enhance clinical decision-making. The solution is available through a web interface, API, and integration with PACS and DICOM storage systems.

Features

AI-driven analysis of chest X-rays for detecting multiple lung abnormalities in under 1 minute

Automated detection and measurement of spine curvatures and bone abnormalities in spine X-rays in under 30 seconds

AI-powered analysis of skull X-rays for sinusitis detection and sinus fullness percentage in under 30 seconds

Algorithms for analyzing knee and pelvis X-rays to assess degenerative processes in under 1 minute

API for integration into existing systems

Web-based access with a built-in DICOM viewer

Integration capabilities with PACS and DICOM storage systems

Option for an out-of-the-box solution that does not require a cloud connection

Target Audience

The primary target audience includes radiologists, medical institutions, and healthcare providers seeking to improve the efficiency and accuracy of X-ray image analysis.