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Machine Learning Applications for Head and Neck Imaging.

Farhad Maleki¹, William Trung Le², Thiparom Sananmuang³

¹Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology & Research Institute of the McGill University Health Centre, 5252 Boulevard de Maisonneuve Ouest, Montreal, Quebec H4A 3S5, Canada.

Neuroimaging Clinics of North America

|October 11, 2020

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This summary is machine-generated.

Machine learning (ML) enhances head and neck (HN) imaging for diagnosing disorders. This review covers deep learning applications, challenges, and solutions for clinical use.

Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Medicine
Head and Neck (HN) Anatomy

Background:

The head and neck (HN) region contains numerous vital structures, making imaging crucial for diagnosing and managing disorders.
Machine learning (ML) is increasingly applied to medical imaging, offering advanced analytical capabilities.
Deep learning, a subset of ML, shows significant promise in medical image analysis.

Purpose of the Study:

To review recent applications of machine learning (ML), particularly deep learning, in head and neck (HN) imaging.
To categorize ML applications in HN imaging into deep learning and traditional ML approaches.
To discuss challenges and propose solutions for implementing ML in clinical HN imaging.

Main Methods:

Literature review of recent advancements in ML for HN imaging.

Keywords:

Artificial intelligence Autosegmentation Classification Convolutional neural network Deep learning Head and neck cancer Head and neck imaging Machine learning

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Categorization of ML applications into deep learning and traditional ML.

Analysis of challenges and recommendations for clinical deployment.

Main Results:

ML applications in HN imaging are diverse, spanning diagnostic and management tasks.
Deep learning models demonstrate high potential for image analysis in the HN region.
Key challenges include data availability, interpretability, and clinical integration.

Conclusions:

ML, especially deep learning, offers transformative potential for HN imaging.
Addressing challenges in data, validation, and clinical workflow is essential for successful ML adoption.
Future research should focus on robust validation and seamless integration into clinical practice.