Evidence map›Paper›PMID 42082836›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

Imitation learning for supervised autonomous tumor resection in central airway obstruction.

Nural Yilmaz, Juo-Tung Chen, Hao Ding, Chenhao Yu, Spencer Huang, Ji Woong Kim, Mariana Smith, Naren Nimmagadda, Alejandro Chara, Brendan Burkhart and 3 more

Abstract read
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

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

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

13 authors.

Nural YilmazJohns Hopkins University, Baltimore, MD, USA. nyilmaz2@jhu.edu.
Juo-Tung ChenJohns Hopkins University, Baltimore, MD, USA.
Hao DingJohns Hopkins University, Baltimore, MD, USA.
Chenhao YuJohns Hopkins University, Baltimore, MD, USA.
Spencer HuangJohns Hopkins University, Baltimore, MD, USA.
Ji Woong KimStanford University, Stanford, CA, USA.
Mariana SmithJohns Hopkins University, Baltimore, MD, USA.
Naren NimmagaddaJohns Hopkins School of Medicine, Baltimore, MD, USA.
Alejandro CharaVanderbilt University Medical Center, Nashville, TN, USA.
Brendan BurkhartJohns Hopkins University, Baltimore, MD, USA.
Anton DeguetJohns Hopkins University, Baltimore, MD, USA.
Mathias UnberathJohns Hopkins University, Baltimore, MD, USA.
Axel KriegerJohns Hopkins University, Baltimore, MD, USA.

Funding

Advanced Research Projects Agency for Health D24AC00415
6 · The paper itself

Abstract

purposeCentral airway obstruction (CAO) procedures require precise, repetitive dual-arm coordination under progressively degraded visibility from smoke and charring. Model-based control struggles with deformable-tissue variability and real-time constraints, while existing data-driven methods emphasize short, single-skill tasks rather than cyclic workflows. We investigate whether imitation learning can achieve fully autonomous task-level and high-level for CAO tumor resection on da Vinci Research Kit.

methodsWe propose a hierarchical framework combining low-level action chunking with transformers (ACT) policies for autonomous task-level execution with finite state machine (FSM)-based high-level coordination for procedure-level sequencing. The workflow decomposes into five recurring tasks. Policies are trained on synchronized multi-view video and kinematics with hybrid-relative actions. We also analyze task-level policies with different data regimes through an ablation study.

resultsLow-level policies achieved fully autonomous execution with 0.938mm RMSE without intervention, comparable to an operator (0.968 mm) and surgeon (1.086 mm). High-level coordination enabled supervised autonomous full procedure execution with minimal interventions resulting in 1.232mm accuracy. Ablation studies showed balanced task coverage with minimal data (3%) maintained coherent behavior (1.614mm), while random 60% sampling yielded 1.386mm. Blinded evaluation by expert surgeons confirmed comparable resection quality and superior retraction performance relative to human operators.

conclusionsThis demonstrates the first learning-based supervised autonomy for CAO removal procedure with expert-level accuracy. The hierarchical framework enables full procedure execution requiring supervision only at task transitions. We publicly release the training datasets including multi-view imagery and robot kinematics as a benchmark for surgical automation research.

Indexed as

Airway ObstructionRobotic Surgical ProceduresAutonomous RobotsHumansIntelligent SystemsAutonomous surgeryImitation learningRobotic surgeryTumor resection

Identifiers

PMID42082836

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.