Evidence map›Paper›PMID 39799528›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

A real-time approach for surgical activity recognition and prediction based on transformer models in robot-assisted surgery.

Ketai Chen, D S V Bandara, Jumpei Arata

Abstract read
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Ketai ChenAdvanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.
D S V BandaraAdvanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan. d-sanjaya@mech.kyushu-u.ac.jp.
Jumpei ArataAdvanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.

Funding

Center for Clinical and Translational Research of Kyushu University Hospital 23ym0126816j0002
6 · The paper itself

Abstract

purposeThis paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.

methodsWe propose a modified Transformer model with the architecture comprising no positional encoding, 5 fully connected layers, 1 encoder, and 3 decoders. This model is specifically designed to address 3 primary tasks in surgical robotics: gesture recognition, prediction, and end-effector trajectory prediction. Notably, it operates solely on kinematic data obtained from the joints of robotic arm.

resultsThe model's performance was evaluated on JHU-ISI Gesture and Skill Assessment Working Set dataset, achieving highest accuracy of 94.4% for gesture recognition, 84.82% for gesture prediction, and significantly low distance error of 1.34 mm with a prediction of 1 s in advance. Notably, the computational time per iteration was minimal recorded at only 4.2 ms.

conclusionThe results demonstrated the excellence of our proposed model compared to previous studies highlighting its potential for integration in real-time systems. We firmly believe that our model could significantly elevate realms of surgical activity recognition and prediction within RAS and make a substantial and meaningful contribution to the healthcare sector.

Indexed as

Deep LearningMinimally Invasive Surgical ProceduresRobotic Surgical ProceduresGesturesHumansDeep learningRobot-assisted surgerySurgical activity recognitionTrajectory predictionTransformer model

Identifiers

PMID39799528

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.