An extended algorithm for autonomous grasping of soft tissues during robotic surgery

Amirkhani, Golchehr; Farahmand, Farzam; Yazdian, Seied Muhammad; Mirbagheri, Alireza

doi:10.1002/rcs.2122

Cited by 11 publications

(7 citation statements)

References 30 publications

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“…In other cases, an algorithm for autonomous grasping of grasp soft tissues in different modes is damage, slip, or safe grasp. On testing, no signs of tissue tear or slippage were found [15]. The HEARO robotic system is another surgical robot to assist the surgeon with orientation, reference information to anatomical structures, and drill trajectory during otological and neurosurgical procedures.…”

Section: A Assistive Tasksmentioning

confidence: 99%

Roadmap to Autonomous Surgery -- A Framework to Surgical Autonomy

Singh¹

2022

Preprint

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Section: A Assistive Tasksmentioning

confidence: 99%

Roadmap to Autonomous Surgery -- A Framework to Surgical Autonomy

Singh¹

2022

Preprint

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“…The assessment and prediction of the geometric characteristics of the spinopelvic complex have garnered significant interest among both the clinical and research communities [ 1 – 3 ]. Radiological examination of the spine and pelvis plays a crucial role in both surgical and non-surgical treatments of spinal disorders [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Landet: an efficient physics-informed deep learning approach for automatic detection of anatomical landmarks and measurement of spinopelvic alignment

MohammadiNasrabadi,

Moammer,

Quateen

et al. 2024

J Orthop Surg Res

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Purpose: An efficient physics-informed deep learning approach for extracting spinopelvic measures from X-ray images is introduced and its performance is evaluated against manual annotations. Methods: Two datasets, comprising a total of 1470 images, were collected to evaluate the model’s performance. We propose a novel method of detecting landmarks as objects, incorporating their relationships as constraints (LanDet). Using this approach, we trained our deep learning model to extract five spine and pelvis measures: Sacrum Slope (SS), Pelvic Tilt (PT), Pelvic Incidence (PI), Lumbar Lordosis (LL), and Sagittal Vertical Axis (SVA). The results were compared to manually labelled test dataset (GT) as well as measures annotated separately by three surgeons. Results: The LanDet model was evaluated on the two datasets separately and on an extended dataset combining both. The final accuracy for each measure is reported in terms of Mean Absolute Error (MAE), Standard Deviation (SD), and R Pearson correlation coefficient as follows: $$[SS^\circ : 3.7 (2.7), R = 0.89]$$ [ S S ∘ : 3.7 ( 2.7 ) , R = 0.89 ] , $$[PT^\circ : 1.3 (1.1), R = 0.98], [PI^\circ : 4.2 (3.1), R = 0.93], [LL^\circ : 5.1 (6.4), R=0.83], [SVA(mm): 2.1 (1.9), R = 0.96]$$ [ P T ∘ : 1.3 ( 1.1 ) , R = 0.98 ] , [ P I ∘ : 4.2 ( 3.1 ) , R = 0.93 ] , [ L L ∘ : 5.1 ( 6.4 ) , R = 0.83 ] , [ S V A ( m m ) : 2.1 ( 1.9 ) , R = 0.96 ] . To assess model reliability and compare it against surgeons, the intraclass correlation coefficient (ICC) metric is used. The model demonstrated better consistency with surgeons with all values over 0.88 compared to what was previously reported in the literature. Conclusion: The LanDet model exhibits competitive performance compared to existing literature. The effectiveness of the physics-informed constraint method, utilized in our landmark detection as object algorithm, is highlighted. Furthermore, we addressed the limitations of heatmap-based methods for anatomical landmark detection and tackled issues related to mis-identifying of similar or adjacent landmarks instead of intended landmark using this novel approach.

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“…However, the surgeon’s capacity to simultaneously control these tools is limited, usually can only control 1 or 2 instruments. This limitation can be mitigated by leveraging surgical robotic systems to autonomously manage tasks such as holding endoscope or manipulating tissues [ 11 – 13 ]. Furthermore, robots are often more stable than medical staff when performing auxiliary operations [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Collision Avoidance Method for Robot-Assisted Minimally Invasive Surgery

Li²,

Ouyang³

et al. 2023

Cyborg Bionic Syst

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In the robot-assisted minimally invasive surgery, if a collision occurs, the robot system program could be damaged, and normal tissues could be injured. To avoid collisions during surgery, a 3-dimensional collision avoidance method is proposed in this paper. The proposed method is predicated on the design of 3 strategic vectors: the collision-with-instrument-avoidance (CI) vector, the collision-with-tissues-avoidance (CT) vector, and the constrained-control (CC) vector. The CI vector demarcates 3 specific directions to forestall collision among the surgical instruments. The CT vector, on the other hand, comprises 2 components tailored to prevent inadvertent contact between the robot-controlled instrument and nontarget tissues. Meanwhile, the CC vector is introduced to guide the endpoint of the robot-controlled instrument toward the desired position, ensuring precision in its movements, in alignment with the surgical goals. Simulation results verify the proposed collision avoidance method for robot-assisted minimally invasive surgery. The code and data are available at https://github.com/cynerelee/collision-avoidance .

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An extended algorithm for autonomous grasping of soft tissues during robotic surgery

Cited by 11 publications

References 30 publications

Roadmap to Autonomous Surgery -- A Framework to Surgical Autonomy

Roadmap to Autonomous Surgery -- A Framework to Surgical Autonomy

Landet: an efficient physics-informed deep learning approach for automatic detection of anatomical landmarks and measurement of spinopelvic alignment

Three-Dimensional Collision Avoidance Method for Robot-Assisted Minimally Invasive Surgery

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