Force adaptive robotically assisted endomicroscopy for intraoperative tumour identification

Systematic review on visual aid technologies for surgical assistant robotic devices. PROGRESS IN BIOMEDICAL ENGINEERING (BRISTOL, ENGLAND) 2025;7:022006. [PMID: 39999565 DOI: 10.1088/2516-1091/adba20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

This review comprehensively analyzes the modern literature on including visual aids in diverse surgical assistant robotic systems. The review considered a deep analysis of diverse technical and scientific sources that provide precise information on how the more recent surgical systems, especially those considering robotic devices, perform automatic operations on patients. The search procedure and the corresponding analytics considered only those conditions where vision systems played a significant role in the surgical procedure, despite the type of end-effector and if only position or force were used as part of the feedback analysis. This review is organized considering the robot configuration, the type of end-effector, the vision systems considered for those cases, and the associated control actions, which must include the acquired image or video. The study analyzes the key contributions of the published cases. It provides a critical description of the advantages and shortcomings of the technological implementation of vision systems in surgical robotic devices. Finally, this review provides a general prospective view of ongoing research on vision aids for surgical robotic systems, which will become an ordinary actor in future surgical systems.

A novel contact optimization algorithm for endomicroscopic surface scanning

PURPOSE

Probe-based confocal laser endomicroscopy (pCLE) offers real-time, cell-level imaging and holds promise for early cancer diagnosis. However, a large area surface scanning for image acquisition is needed to overcome the limitation of field-of-view. Obtaining high-quality images during scanning requires maintaining a stable contact distance between the tissue and probe. This work presents a novel contact optimization algorithm to acquire high-quality pCLE images.

METHODS

The contact optimization algorithm, based on swarm intelligence of whale optimization algorithm, is designed to optimize the probe position, according to the quality of the image acquired by probe. An accurate image quality assessment of total co-occurrence entropy is introduced to evaluate the pCLE image quality. The algorithm aims to maintain a consistent probe-tissue contact, resulting in high-quality images acquisition.

RESULTS

Scanning experiments on sponge, ex vivo swine skin tissue and stomach tissue demonstrate the effectiveness of the contact optimization algorithm. Scanning results of the sponge with three different trajectories (spiral trajectory, circle trajectory, and raster trajectory) reveal high-quality mosaics with clear details in every part of the image and no blurred sections.

CONCLUSION

The contact optimization algorithm successfully identifies the optimal distance between probe and tissue, improving the quality of pCLE images. Experimental results confirm the high potential of this method in endomicroscopic surface scanning.

Review of surgical robotic systems for keyhole and endoscopic procedures: state of the art and perspectives

Minimally invasive surgery, including laparoscopic and thoracoscopic procedures, benefits patients in terms of improved postoperative outcomes and short recovery time. The challenges in hand-eye coordination and manipulation dexterity during the aforementioned procedures have inspired an enormous wave of developments on surgical robotic systems to assist keyhole and endoscopic procedures in the past decades. This paper presents a systematic review of the state-of-the-art systems, picturing a detailed landscape of the system configurations, actuation schemes, and control approaches of the existing surgical robotic systems for keyhole and endoscopic procedures. The development challenges and future perspectives are discussed in depth to point out the need for new enabling technologies and inspire future researches.

Image computing for fibre-bundle endomicroscopy: A review

Endomicroscopy is an emerging imaging modality, that facilitates the acquisition of in vivo, in situ optical biopsies, assisting diagnostic and potentially therapeutic interventions. While there is a diverse and constantly expanding range of commercial and experimental optical biopsy platforms available, fibre-bundle endomicroscopy is currently the most widely used platform and is approved for clinical use in a range of clinical indications. Miniaturised, flexible fibre-bundles, guided through the working channel of endoscopes, needles and catheters, enable high-resolution imaging across a variety of organ systems. Yet, the nature of image acquisition though a fibre-bundle gives rise to several inherent characteristics and limitations necessitating novel and effective image pre- and post-processing algorithms, ranging from image formation, enhancement and mosaicing to pathology detection and quantification. This paper introduces the underlying technology and most prevalent clinical applications of fibre-bundle endomicroscopy, and provides a comprehensive, up-to-date, review of relevant image reconstruction, analysis and understanding/inference methodologies. Furthermore, current limitations as well as future challenges and opportunities in fibre-bundle endomicroscopy computing are identified and discussed.

Hybrid Robot-assisted Frameworks for Endomicroscopy Scanning in Retinal Surgeries

High-resolution real-time intraocular imaging of retina at the cellular level is very challenging due to the vulnerable and confined space within the eyeball as well as the limited availability of appropriate modalities. A probe-based confocal laser endomicroscopy (pCLE) system, can be a potential imaging modality for improved diagnosis. The ability to visualize the retina at the cellular level could provide information that may predict surgical outcomes. The adoption of intraocular pCLE scanning is currently limited due to the narrow field of view and the micron-scale range of focus. In the absence of motion compensation, physiological tremors of the surgeons' hand and patient movements also contribute to the deterioration of the image quality. Therefore, an image-based hybrid control strategy is proposed to mitigate the above challenges. The proposed hybrid control strategy enables a shared control of the pCLE probe between surgeons and robots to scan the retina precisely, with the absence of hand tremors and with the advantages of an image-based auto-focus algorithm that optimizes the quality of pCLE images. The hybrid control strategy is deployed on two frameworks - cooperative and teleoperated. Better image quality, smoother motion, and reduced workload are all achieved in a statistically significant manner with the hybrid control frameworks.

A Novel Semi-Autonomous Control Framework for Retina Confocal Endomicroscopy Scanning

In this paper, a novel semi-autonomous control framework is presented for enabling probe-based confocal laser endomicroscopy (pCLE) scan of the retinal tissue. With pCLE, retinal layers such as nerve fiber layer (NFL) and retinal ganglion cell (RGC) can be scanned and characterized in real-time for an improved diagnosis and surgical outcome prediction. However, the limited field of view of the pCLE system and the micron-scale optimal focus distance of the probe, which are in the order of physiological hand tremor, act as barriers to successful manual scan of retinal tissue. Therefore, a novel sensorless framework is proposed for real-time semi-autonomous endomicroscopy scanning during retinal surgery. The framework consists of the Steady-Hand Eye Robot (SHER) integrated with a pCLE system, where the motion of the probe is controlled semi-autonomously. Through a hybrid motion control strategy, the system autonomously controls the confocal probe to optimize the sharpness and quality of the pCLE images, while providing the surgeon with the ability to scan the tissue in a tremor-free manner. Effectiveness of the proposed architecture is validated through experimental evaluations as well as a user study involving 9 participants. It is shown through statistical analyses that the proposed framework can reduce the work load experienced by the users in a statistically-significant manner, while also enhancing their performance in retaining pCLE images with optimized quality.

Transfer Recurrent Feature Learning for Endomicroscopy Image Recognition

Probe-based confocal laser endomicroscopy (pCLE) is an emerging tool for epithelial cancer diagnosis, which enables in-vivo microscopic imaging during endoscopic procedures and facilitates the development of automatic recognition algorithms to identify the status of tissues. In this paper, we propose a transfer recurrent feature learning framework for classification tasks on pCLE videos. At the first stage, the discriminative feature of single pCLE frame is learned via generative adversarial networks based on both pCLE and histology modalities. At the second stage, we use recurrent neural networks to handle the varying length and irregular shape of pCLE mosaics taking the frame-based features as input. The experiments on real pCLE data sets demonstrate that our approach outperforms, with statistical significance, state-of-the-art approaches. A binary classification accuracy of 84.1% has been achieved.

A cable-driven parallel manipulator with force sensing capabilities for high-accuracy tissue endomicroscopy

PURPOSE

Endomicroscopy (EM) provides high resolution, non-invasive histological tissue information and can be used for scanning of large areas of tissue to assess cancerous and pre-cancerous lesions and their margins. However, current robotic solutions do not provide the accuracy and force sensitivity required to perform safe and accurate tissue scanning.

METHODS

A new surgical instrument has been developed that uses a cable-driven parallel mechanism (CPDM) to manipulate an EM probe. End-effector forces are determined by measuring the tensions in each cable. As a result, the instrument allows to accurately apply a contact force on a tissue, while at the same time offering high resolution and highly repeatable probe movement.

RESULTS

0.2 and 0.6 N force sensitivities were found for 1 and 2 DoF image acquisition methods, respectively. A back-stepping technique can be used when a higher force sensitivity is required for the acquisition of high quality tissue images. This method was successful in acquiring images on ex vivo liver tissue.

CONCLUSION

The proposed approach offers high force sensitivity and precise control, which is essential for robotic EM. The technical benefits of the current system can also be used for other surgical robotic applications, including safe autonomous control, haptic feedback and palpation.

Endomicroscopy for Computer and Robot Assisted Intervention

Endomicroscopy is a new technique that allows human tissue to be characterized in vivo and in situ, circumventing the need for conventional biopsy and histology. Despite increased application and growing research interests in this area, the clinical application of endomicroscopy, however, is limited by difficulties in ergonomic control, consistent probe-tissue contact, large area surveillance, and retargeting. Recently, advances in high-speed imaging, mosaicing, and robotics have aimed to address these difficulties. The development of robot-assisted devices in particular has shown great promises in extending the clinical potential of endomicroscopy. Issues related to miniaturization, adaptation to tissue deformation, control stability, force and position compensation, cost, and sterility are being pursued by both research and commercial communities. In this review, recent clinical and technical developments in different aspects of computer and robotic assisted endomicroscopy interventions including instrumentation, multiscale integration, and high-speed imaging techniques are presented. We further address emerging trends and new research opportunities toward more widespread clinical acceptance of robotically assisted endomicroscopy technologies.

Line-scanning fiber bundle endomicroscopy with a virtual detector slit

Coherent fiber bundles can be used to relay the image plane from the distal tip of an endomicroscope to an external confocal microscopy system. The frame rate is therefore determined by the speed of the microscope's laser scanning system which, at 10-20 Hz, may be undesirably low for in vivo clinical applications. Line-scanning allows an increase in the frame rate by an order of magnitude in exchange for some loss of optical sectioning, but the width of the detector slit cannot easily be adapted to suit different imaging conditions. The rolling shutter of a CMOS camera can be used as a virtual detector slit for a bench-top line-scanning confocal microscope, and here we extend this idea to endomicroscopy. By synchronizing the camera rolling shutter with a scanning laser line we achieve confocal imaging with an electronically variable detector slit. This architecture allows us to acquire every other frame with the detector slit offset by a known distance, and we show that subtracting this second image leads to improved optical sectioning.