Autonomous Navigation for Robot-Assisted Intraluminal and Endovascular Procedures: A Systematic Review

Registration, Path Planning and Shape Reconstruction for Soft Tools in Robot-Assisted Intraluminal Procedures: A Review

BACKGROUND

Robot and navigation systems can relieve surgeon's difficulties in delicate and safe operation in tortuous lumens in traditional intraluminal procedures (IP). This paper aims to review the three key components of these systems: registration, path planning and shape reconstruction and highlight their limitations and future perspectives.

METHODS

An electronic search for relevant studies was performed in Web of Science and Google scholar databases until 2024.

RESULTS

As for 2D-3D registration in IP, we focused on analysing feature extraction. For path planning, this paper proposed a new classification method and focused on selection of planning space and the establishment of path cost. Regarding shape reconstruction, the pros and cons of existing methods are analysed and methods based on fibre optic sensors and electromagnetic (EM) tracking are focused on.

CONCLUSION

These three technologies in IP have made great progress, but there are still challenges that require further research.

Endovascular robotics: technical advances and future directions

Endovascular interventions excel in treating cardiovascular diseases in a minimally invasive manner, showing improved outcomes over open techniques. However, challenges related to precise navigation - still relying on 2D fluoroscopy - persist. This review examines the role of robotics, highlighting commercial and research platforms, while exploring emerging trends like MRI compatibility, enhanced navigation, and autonomy. MRI-compatible systems offer radiation-free 3D imaging. Human-robot interaction evolves with task-specific interfaces, while autonomy ranges from partial to full, aiding clinical operators. Challenges include complexity and cost, emphasizing compatibility and navigation advancements. Integrating MRI-compatible robots, refining human-robot interaction, and enhancing autonomy promise advancements in endovascular surgery, fueled by AI and innovative imaging.

Robust Path Planning via Learning From Demonstrations for Robotic Catheters in Deformable Environments

OBJECTIVE

Navigation through tortuous and deformable vessels using catheters with limited steering capability underscores the need for reliable path planning. State-of-the-art path planners do not fully account for the deformable nature of the environment.

METHODS

This work proposes a robust path planner via a learning from demonstrations method, named Curriculum Generative Adversarial Imitation Learning (C-GAIL). This path planning framework takes into account the interaction between steerable catheters and vessel walls and the deformable property of vessels.

RESULTS

In-silico comparative experiments show that the proposed network achieves a 38% higher success rate in static environments and 17% higher in dynamic environments compared to a state-of-the-art approach based on GAIL. In-vitro validation experiments indicate that the path generated by the proposed C-GAIL path planner achieves a targeting error of 1.26 0.55 mm and a tracking error of 5.18 3.48 mm. These results represent improvements of 41% and 40% over the conventional centerline-following technique for targeting error and tracking error, respectively.

CONCLUSION

The proposed C-GAIL path planner outperforms the state-of-the-art GAIL approach. The in-vitro validation experiments demonstrate that the path generated by the proposed C-GAIL path planner aligns better with the actual steering capability of the pneumatic artificial muscle-driven catheter utilized in this study. Therefore, the proposed approach can provide enhanced support to the user in navigating the catheter towards the target with greater accuracy, effectively meeting clinical accuracy requirements.

SIGNIFICANCE

The proposed path planning framework exhibits superior performance in managing uncertainty associated with vessel deformation, thereby resulting in lower tracking errors.

Multi-Objective Safety-Enhanced Path Planning for the Anterior Part of a Flexible Ureteroscope in Robot-Assisted Surgery

BACKGROUND

In robot-assisted flexible ureteroscopy, planning a safety-enhanced path facilitates ureteroscope reaching the target safely and quickly. However, current methods rarely consider the safety impact caused by body motion of the anterior part, such as impingement on the lumen wall and sweeping motion risk, or the method can only be used in collision-free situations.

METHODS

The kinematic model of the anterior part under C-shaped and S-shaped collision bending is first analysed. Considering the newly defined impingement cost and sweeping area, the differential evolution algorithm is adopted to optimise the path in the configuration space. Experiments were performed to verify the effectiveness of the method.

RESULTS

Compared with the competing algorithm, the proposed algorithm reduced impingement cost and sweeping area by an average of 31.0% and 8.64%. Force measurement experiments verified the rationality of the impingement cost expression.

CONCLUSION

The experimental results proved the feasibility of the proposed path planning algorithm.

Multi-Section Magnetic Soft Robot with Multirobot Navigation System for Vasculature Intervention

Magnetic soft robots have recently become a promising technology that has been applied to minimally invasive cardiovascular surgery. This paper presents the analytical modeling of a novel multi-section magnetic soft robot (MS-MSR) with multi-curvature bending, which is maneuvered by an associated collaborative multirobot navigation system (CMNS) with magnetic actuation and ultrasound guidance targeted for intravascular intervention. The kinematic and dynamic analysis of the MS-MSR's telescopic motion is performed using the optimized Cosserat rod model by considering the effect of an external heterogeneous magnetic field, which is generated by a mobile magnetic actuation manipulator to adapt to complex steering scenarios. Meanwhile, an extracorporeal mobile ultrasound navigation manipulator is exploited to track the magnetic soft robot's distal tip motion to realize a closed-loop control. We also conduct a quadratic programming-based optimization scheme to synchronize the multi-objective task-space motion of CMNS with null-space projection. It allows the formulation of a comprehensive controller with motion priority for multirobot collaboration. Experimental results demonstrate that the proposed magnetic soft robot can be successfully navigated within the multi-bifurcation intravascular environment with a shape modeling error 3.62 ± 1.28 ∘ and a tip error of 1.08 ± 0.45 mm under the actuation of a CMNS through in vitro ultrasound-guided vasculature interventional tests.

Medical Microrobots

Scientists around the world have long aimed to produce miniature robots that can be controlled inside the human body to aid doctors in identifying and treating diseases. Such microrobots hold the potential to access hard-to-reach areas of the body through the natural lumina. Wireless access has the potential to overcome drawbacks of systemic therapy, as well as to enable completely new minimally invasive procedures. The aim of this review is fourfold: first, to provide a collection of valuable anatomical and physiological information on the target working environments together with engineering tools for the design of medical microrobots; second, to provide a comprehensive updated survey of the technological state of the art in relevant classes of medical microrobots; third, to analyze currently available tracking and closed-loop control strategies compatible with the in-body environment; and fourth, to explore the challenges still in place, to steer and inspire future research.

Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model

BACKGROUND

Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Nevertheless, accurately identifying various lesion features, such as irregular sizes, shapes, colors, and textures, remains challenging in this field.

OBJECTIVE

Several computer vision algorithms have been introduced to tackle these challenges, but many relied on handcrafted features, resulting in inaccuracies in various instances.

METHODS

In this work, a novel Deep SS-Hexa model is proposed which is a combination two different deep learning structures for extracting two different features from the WCE images to detect various GIT ailment. The gathered images are denoised by weighted median filter to remove the noisy distortions and augment the images for enhancing the training data. The structural and statistical (SS) feature extraction process is sectioned into two phases for the analysis of distinct regions of gastrointestinal. In the first stage, statistical features of the image are retrieved using MobileNet with the support of SiLU activation function to retrieve the relevant features. In the second phase, the segmented intestine images are transformed into structural features to learn the local information. These SS features are parallelly fused for selecting the best relevant features with walrus optimization algorithm. Finally, Deep belief network (DBN) is used classified the GIT diseases into hexa classes namely normal, ulcer, pylorus, cecum, esophagitis and polyps on the basis of the selected features.

RESULTS

The proposed Deep SS-Hexa model attains an overall average accuracy of 99.16% in GIT disease detection based on KVASIR and KID datasets. The proposed Deep SS-Hexa model achieves high level of accuracy with minimal computational cost in the recognition of GIT illness.

CONCLUSIONS

The proposed Deep SS-Hexa Model progresses the overall accuracy range of 0.04%, 0.80% better than GastroVision, Genetic algorithm based on KVASIR dataset and 0.60%, 1.21% better than Modified U-Net, WCENet based on KID dataset respectively.