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Machine learning and image analysis in vascular surgery.

Roger T Tomihama¹, Saharsh Dass¹, Sally Chen²

¹Department of Radiology, Section of Vascular and Interventional Radiology, Linda University School of Medicine, 11234 Anderson Street, Suite MC-2605E, Loma Linda, CA 92354.

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Summary

This summary is machine-generated.

Deep learning, a subset of machine learning, enhances medical image analysis in vascular surgery by automatically learning features. This review explores its applications in disease classification and segmentation.

Keywords:

Artificial intelligence Image segmentation Vascular surgery

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Area of Science:

Artificial Intelligence
Machine Learning
Medical Image Analysis

Background:

Traditional methods for medical image analysis in vascular surgery rely on manual feature extraction.
Deep learning (DL) models, a subset of machine learning, offer automated feature learning without prior assumptions.
Convolutional Neural Networks (CNNs) are a key DL technique for image processing, utilizing multilevel architectures and weighted connections.

Purpose of the Study:

To review machine learning (ML) image analysis concepts.
To explore the application of ML and DL in vascular surgery imaging.
To highlight the role of CNNs in medical image analysis for vascular surgery.

Main Methods:

Review of existing literature on machine learning and deep learning in medical imaging.
Focus on convolutional neural networks (CNNs) for image analysis tasks.
Examination of applications in disease classification, object identification, and segmentation.

Main Results:

Deep learning methods demonstrate significant success in medical image analysis for vascular surgery.
CNNs can automatically learn image features and classify data effectively.
Applications include disease classification, object identification, semantic segmentation, and instance segmentation.

Conclusions:

Deep learning, particularly CNNs, represents a powerful advancement in vascular surgery medical image analysis.
These techniques offer automated and efficient solutions compared to traditional methods.
The review underscores the growing importance and potential of AI in vascular surgery diagnostics and treatment planning.