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Related Experiment Video

Updated: May 31, 2026

C-arm-Free Simultaneous OLIF51 and Percutaneous Pedicle Screw Fixation in a Single Lateral Position
12:25

C-arm-Free Simultaneous OLIF51 and Percutaneous Pedicle Screw Fixation in a Single Lateral Position

Published on: September 16, 2022

Please follow the rules: surgical workflow recognition constrained by linear temporal logic.

Dario Tayupo1, Arnaud Huaulmé2, Krystel Nyangoh Timoh2

  • 1University of Rennes, INSERM LTSI - UMR 1099, 35000, Rennes, France. dario.tayupo@univ-rennes.fr.

International Journal of Computer Assisted Radiology and Surgery
|May 28, 2026
PubMed
Summary

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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

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Integrating procedural knowledge with logic-based rules improves surgical workflow recognition. This enhances temporal consistency in AI predictions, aiding explainability in surgical AI systems.

Area of Science:

  • Artificial Intelligence
  • Robotics
  • Medical Informatics

Background:

  • Surgical workflow recognition uses deep learning for action identification.
  • Current methods may produce unrealistic predictions due to data reliance.
  • Incorporating procedural knowledge can improve temporal consistency and model robustness.

Purpose of the Study:

  • To apply differentiable temporal logic (DTL) for procedural constraints in surgical action recognition.
  • To introduce new operators from Allen's Interval Algebra (AIA) for finer-grained constraints.
  • To enhance the temporal consistency and interpretability of AI-driven surgical workflow recognition.

Main Methods:

  • Implemented DTL and AIA operators to define and enforce procedural constraints during model training.
Keywords:
Deep learningLogic operatorsProcedural knowledgeSurgical workflow recognition

Related Experiment Videos

Last Updated: May 31, 2026

C-arm-Free Simultaneous OLIF51 and Percutaneous Pedicle Screw Fixation in a Single Lateral Position
12:25

C-arm-Free Simultaneous OLIF51 and Percutaneous Pedicle Screw Fixation in a Single Lateral Position

Published on: September 16, 2022

  • Evaluated methods using accuracy, Edit score, and segmental F1-score on hysterectomy, AutoLaparo, and Cholec80 datasets.
  • Compared performance across Bi-LSTMs, MS-TCNs, and ASFormer (transformer-based) models.

    • Main Results:

    • DTL and AIA constraints showed a moderate impact on accuracy but increased segment-based metrics.
    • A trend towards higher temporal consistency was observed across datasets and models.
    • Statistical testing indicated significant differences in performance under certain conditions.

    Conclusions:

    • Logic formulas effectively define procedural constraints for surgical actions.
    • Integrating procedural knowledge improves temporal consistency in AI predictions.
    • This approach supports explainability in surgical AI systems and enhances sequence-level predictions.