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Veins of Head and Neck

Veins of Head and Neck

The blood drainage from the head and neck is primarily managed by three pairs of veins: the external jugular, internal jugular, and vertebral veins. The external jugular veins drain superficial scalp and face structures, passing over the sternocleidomastoid muscles to empty into the subclavian veins.
On the other hand, the vertebral veins, unlike their arterial counterparts, are not primarily responsible for brain drainage. Instead, they drain the cervical vertebrae, spinal cord, and some small...

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Related Experiment Video

Updated: Mar 18, 2026

Porcine As a Training Module for Head and Neck Microvascular Reconstruction

Porcine As a Training Module for Head and Neck Microvascular Reconstruction

Published on: September 29, 2018

Identifying Venous Insufficiency in Head and Neck Reconstruction Flaps Using Machine Learning and Deep Learning

Yurong He^1,2, Jugao Fang^1,2, Lizhen Hou^1,2

¹Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.

|March 16, 2026

Summary

Artificial intelligence (AI) offers a reliable method for detecting venous insufficiency, a common cause of flap failure in head and neck reconstruction. Deep learning models demonstrate high accuracy in identifying this critical postoperative complication.

Keywords:

artificial intelligence deep learning flap failure flap monitoring machine learning

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Area of Science:

Biomedical Engineering
Medical Imaging
Artificial Intelligence

Background:

Venous insufficiency is a significant risk factor for flap failure in head and neck cancer reconstruction.
Early detection of venous insufficiency is crucial for successful patient outcomes.
Artificial intelligence (AI) presents a promising avenue for improving the early detection of flap complications.

Purpose of the Study:

To evaluate the efficacy of machine learning and deep learning models in detecting venous insufficiency in head and neck flaps.
To identify key image features indicative of venous insufficiency using AI-based visualization techniques.
To establish AI as a valuable auxiliary tool for postoperative flap monitoring.

Main Methods:

Retrospective analysis of clinical data and postoperative flap images from head and neck cancer patients (2018-2024).
Development and comparison of eight machine learning classifiers and three deep learning models (ResNet, GoogleNet, Densenet).
Utilized SHAP and Grad-CAM for feature analysis and model interpretability.

Main Results:

A dataset of 2575 flap images from 576 patients was analyzed.
The Random Forest model achieved 90.25% accuracy, with Hue_mean and Green_median identified as key features.
The ResNet deep learning model demonstrated superior performance with 95.23% accuracy, 84.81% sensitivity, 97.27% specificity, and an AUC of 0.940.

Conclusions:

Deep learning models show significant potential for accurately identifying flap venous insufficiency.
AI-based tools can serve as effective auxiliary aids for postoperative monitoring of head and neck flaps.
The findings support the integration of AI in clinical practice for enhanced patient care and reduced flap failure rates.