Evidence map›Paper›PMID 42226131›Full record

Observational studyBMC anesthesiology2026

A two-step deep learning framework for predicting difficult video laryngoscopy from ultrasound images: a prospective cohort study.

Chenyu Jin, Bei Pei, Ren Zhou, Shuang Cao, Yu Sun, Zhiliang Lin, Mingxu Zhao, Yaokun Zheng, Qian Wang, Maosong Cao and 2 more

Registry-linked trialAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMC anesthesiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05207254 (Research on the Application of Artificial Intelligence Ultrasonic Recognition), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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.

NCT05207254 unknown statusnot on this map

Research on the Application of Artificial Intelligence Ultrasonic Recognition

TypeobservationalSponsorShanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong UniversityRan2021 to 2025Enrolled4,000ConditionsDifficult AirwaysArmsUltrasonic test
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Chenyu Jin *Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Bei Pei *Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ren ZhouDepartment of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shuang CaoDepartment of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yu SunDepartment of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zhiliang LinState Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
Mingxu ZhaoState Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
Yaokun ZhengState Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
Qian WangSchool of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
Maosong CaoSchool of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
Jia Yan *Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. mzkyanj@163.com.
Hong Jiang *Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. jianghongjiuyuan@163.com.

Funding

Beijing Health Alliance Charitable Foundation No. MZ-20240924-03Shanghai Hospital Deveopment Center No. SHDC22023219Shanghai Municipal Health Commission No. 2022JC024Shanghai Ninth People's Hospital No. JYLJ202308Yangtze River Pharmaceutical Group No. JYHX2025028
6 · The paper itself

Abstract

backgroundDeep learning integrated with ultrasound systems may assist in predicting difficult airway, a life-threatening complication in anesthesia. The study aimed to assess the feasibility of AI-based ultrasound for predicting difficult video laryngoscopy (DVL); and identify ultrasonographic measurements associated with DVL using AI-generated heatmaps.

methodsPatients who underwent ultrasonographic airway examinations were enrolled. DVL were classified as Cormack-Lehane grade 3 or 4. The methodology involved two steps: In step 1, ResNet-18 extracted features from ultrasound scans, which were then used to develop predictive models with various machine learning algorithms. The area under the receiver operating characteristic curves (AUROC) were used to assess the performance of models. In step 2, heatmaps identified critical regions for DVL, and multivariable logistic regression was used to assess the association between ultrasonographic measurements and DVL.

results1,474 patients were included, of whom 95 had DVL. In step 1, the combined model (including section of the hyoid bone, sagittal section of the tongue and thyromental distance) with LightGBM algorithm showed an AUROC value of 0.804, with a sensitivity of 0.889, a specificity of 0.657 in the test set. In step 2, distance from the skin to the end of the tongue root (DSTR) was significantly associated with DVL [odds ratios (OR): 4.83, 95% CI: 2.99-8.01]. Restricted cubic spline revealed a nonlinear association between DSTR and DVL, with a significant rise in the OR for DVL, particularly at DSTR ≥ 3.89 cm.

conclusionAI-based ultrasound technology can predict DVL, and DSTR is associated with DVL.

trial registrationThis study was registered at ClinicalTrials.gov (NCT05207254).

Indexed as

Deep LearningLaryngoscopyAgedCohort StudiesFeasibility StudiesFemaleHumansMaleMiddle AgedPredictive Learning ModelsProspective StudiesUltrasonographyArtificial IntelligenceDeep learningIntubation, IntratrachealLaryngoscopyUltrasonography

Identifiers

PMID42226131
PMCPMC13435595

What OpenQuestion holds

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Registered trials

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.