Evidence map›Paper›PMID 41839653›Full record

ArticleHead & neck2026

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

Yurong He, Jugao Fang, Lizhen Hou, Zufei Li, Shizhi He, Yanming Zhao, Qijia Li, Rongjia Li, Xiaoyu Shi, Jiayi Yang and 1 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Head & neck, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yurong HeDepartment of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.ORCID 0000-0002-7809-4153
Jugao FangDepartment of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.ORCID 0000-0003-1372-7228
Lizhen HouDepartment of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Zufei LiDepartment of Otorhinolaryngology, Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Shizhi HeDepartment of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Yanming ZhaoDepartment of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Qijia LiDepartment of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Rongjia LiDepartment of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Xiaoyu ShiDepartment of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Jiayi YangDepartment of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Qi ZhongDepartment of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.

Funding

Beijing Natural Science Foundation 7242039Cross - cooperation project of Beijing Science and Technology New Star Program 20230484484
6 · The paper itself

Abstract

backgroundVenous insufficiency is a major cause of flap failure in head and neck reconstruction. AI provides a reliable, convenient solution for early detection.

methodsClinical data and postoperative flap photos of head and neck cancer patients (2018-2024) at our center were retrospectively collected, categorized into normal and venous-insufficient groups. Eight machine learning classifiers and three deep learning models (ResNet, GoogleNet, Densenet) were built. SHAP and Grad-CAM visualization were used for feature analysis and validation.

resultsA total of 2575 flap images from 576 patients (2010 normal, 565 venous-insufficient) were analyzed. Random Forest performed best in machine learning (accuracy 90.25%, AUC 0.759), with SHAP identifying Hue_mean and Green_median as key features. ResNet outperformed in deep learning (accuracy 95.23%, sensitivity 84.81%, specificity 97.27%, AUC 0.940).

conclusionThe deep learning model shows good value in identifying flap venous insufficiency, serving as an auxiliary tool for postoperative monitoring.

Indexed as

Deep LearningHead and Neck NeoplasmsMachine LearningPlastic Surgery ProceduresSurgical FlapsVenous InsufficiencyFemaleHumansMaleMiddle AgedPredictive Learning ModelsRandom ForestRetrospective Studiesartificial intelligencedeep learningflap failureflap monitoringmachine learning

Identifiers

PMID41839653
PMCPMC13332573

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.