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Updated: Aug 11, 2025

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

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

Published on: November 30, 2022

Deep learning applications in osteoarthritis imaging.

Richard Kijowski¹, Jan Fritz², Cem M Deniz²

¹Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3Rd Floor, New York, NY, 10016, USA. Richard.Kijowski@nyulangone.org.

Skeletal Radiology

|February 9, 2023

Summary

This summary is machine-generated.

Deep learning (DL) shows promise in osteoarthritis (OA) imaging for diagnosis and risk assessment. While DL matches human performance in detecting knee OA on MRI and X-rays, further refinement and validation are needed for clinical use.

Keywords:

Artificial intelligence Deep learning Knee MRI Osteoarthritis X-rays

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Last Updated: Aug 11, 2025

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Published on: November 30, 2022

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Published on: May 24, 2020

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Published on: March 18, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Osteoarthritis Research

Background:

Osteoarthritis (OA) poses a significant health burden.
Medical imaging plays a crucial role in OA diagnosis and management.
Current imaging analysis methods can be time-consuming and subjective.

Purpose of the Study:

To review current applications of deep learning (DL) in osteoarthritis imaging.
To evaluate DL's performance in cartilage lesion detection, OA diagnosis, segmentation, and risk assessment.
To discuss the potential and limitations of DL in OA imaging.

Main Methods:

Review of existing literature on DL applications in OA imaging.
Analysis of DL methods for cartilage lesion detection, OA diagnosis, segmentation, and risk prediction.
Comparison of DL performance against human readers and current methods.

Main Results:

DL demonstrates comparable diagnostic performance to human readers for knee OA detection and grading on MRI and X-rays.
Automated DL segmentation of knee tissues achieves higher accuracy and speed.
DL models show high performance in predicting OA incidence, progression, and knee pain.

Conclusions:

Deep learning shows encouraging preliminary results in OA imaging.
Further technical refinement and validation in diverse datasets are necessary for clinical implementation.
DL has the potential to significantly advance OA diagnosis, monitoring, and risk prediction.