Concepts and Trends in Autonomy for Robot-Assisted Surgery

Fiorini, Paolo; Goldberg, Ken; Liu, Yun-Hui; Taylor, Russell H.

doi:10.1109/jproc.2022.3176828

Cited by 31 publications

(13 citation statements)

References 121 publications

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“…These were the first autonomous systems used to assist with MIS intervention. The autonomy is implemented via feature tracking algorithms that maintain the surgical instrument in the endoscope’s visual field ( 38 ).…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer

Avram

Lazăr²,

Mariş³

et al. 2023

Front. Oncol.

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BackgroundA considerable number of recent research have used artificial intelligence (AI) in the area of colorectal cancer (CRC). Surgical treatment of CRC still remains the most important curative component. Artificial intelligence in CRC surgery is not nearly as advanced as it is in screening (colonoscopy), diagnosis and prognosis, especially due to the increased complexity and variability of structures and elements in all fields of view, as well as a general shortage of annotated video banks for utilization.MethodsA literature search was made and relevant studies were included in the minireview.ResultsThe intraoperative steps which, at this moment, can benefit from AI in CRC are: phase and action recognition, excision plane navigation, endoscopy control, real-time circulation analysis, knot tying, automatic optical biopsy and hyperspectral imaging. This minireview also analyses the current advances in robotic treatment of CRC as well as the present possibility of automated CRC robotic surgery.ConclusionsThe use of AI in CRC surgery is still at its beginnings. The development of AI models capable of reproducing a colorectal expert surgeon’s skill, the creation of large and complex datasets and the standardization of surgical colorectal procedures will contribute to the widespread use of AI in CRC surgical treatment.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer

Avram

Lazăr²,

Mariş³

et al. 2023

Front. Oncol.

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“…Learning-based intelligent systems have gained increasing prominence in recent years as computational resources and the maturity of artificial intelligence (AI) algorithms enable deployment in multiple areas of medicine [30,31]. AI already enjoys wide application in radiology, where training benefits from the fact that data needed for diagnostic reasoning-radiographs as input and clinical annotations as output-coincide with information already recorded in standard practice [32,33].…”

Section: Introductionmentioning

confidence: 99%

“…Given the differences in application, reviews of simulation training for general robotics, such as [47], focus on the use of RGB(-D) imaging, which generally does not apply in the context of MIS. Further, although previous reviews include recent advances in MIS [48][49][50][51][52][53][54][55][56][57][58][59], robotic-assisted MIS [55,[60][61][62][63], machine learning in surgical interventions [34,35,[64][65][66][67][68][69][70][71][72], or surgical simulation for human training purposes [73][74][75][76], in silico training specifically for intelligent MIS systems remains an emerging area deserving of an introduction. We focus this review on frameworks and successful applications in three imaging modalities which have received the bulk of researchers' attention, namely endoscopy, ultrasound (US), and x-ray.…”

Section: Introductionmentioning

confidence: 99%

In silico simulation: a key enabling technology for next-generation intelligent surgical systems

et al. 2023

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To mitigate the challenges of operating through narrow incisions under image guidance, there is a desire to develop intelligent systems that assist decision making and spatial reasoning in minimally invasive surgery (MIS). In this context, machine learning-based systems for interventional image analysis are receiving considerable attention because of their flexibility and the opportunity to provide immediate, informative feedback to clinicians. It is further believed that learning-based image analysis may eventually form the foundation for semior fully automated delivery of surgical treatments. A significant bottleneck in developing such systems is the availability of annotated images with sufficient variability to train generalizable models, particularly the most recently favored deep convolutional neural networks or transformer architectures. A popular alternative to acquiring and manually annotating data from the clinical practice is the simulation of these data from human-based models. Simulation has many advantages, including the avoidance of ethical issues, precisely controlled environments, and the scalability of data collection. Here, we survey recent work that relies on in silico training of learning-based MIS systems, in which data are generated via computational simulation. For each imaging modality, we review available simulation tools in terms of compute requirements, image quality, and usability, as well as their applications for training intelligent systems. We further discuss open challenges for simulation-based development of MIS systems, such as the need for integrated imaging and physical modeling for non-optical modalities, as well as generative patient models not dependent on underlying CT, MRI, or other patient data. In conclusion, as the capabilities of in silico training mature, with respect to sim-to-real transfer, computational efficiency, and degree of control, they are contributing toward the next generation of intelligent surgical systems.

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“…An area of application where the integration of 3D US imaging and robotic guidance may lead to autonomous systems is the robotic prostate biopsy [15]. A complete review of the existing autonomous capabilities of surgical robots with the introduction of a practical scale to assess the level of autonomy of current and future surgical robots may be found in [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Robotic System for Breast Biopsy With Deformation Compensation

Ferrari

Tagliabue

Maris

et al. 2023

IEEE Robot. Autom. Lett.

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Image-guided biopsy is the most common technique for breast cancer diagnosis. Although magnetic resonance imaging (MRI) has the highest sensitivity in breast lesion detection, ultrasound (US) biopsy guidance is generally preferred due to its non-invasiveness and real-time image feedback during the insertion. In this work, we propose an autonomous robotic system for US-guided biopsy of breast lesions identified on pre-operative MRI. After initial MRI to breast registration, the US probe attached to the robotic manipulator compresses the breast tissues until a pre-determined force level is reached. This technique, known as preloading, will allow to minimize lesion displacement during the needle insertion. Our workflow integrates a deformation compensation strategy based on patient-specific biomechanical model to update the US probe orientation keeping the target lesion on the US image plane during compression. By relying on a deformation model, the proposed system does not require lesion visibility on US. Experimental evaluation is performed to assess the performance of the system on a realistic breast phantom with 15 internal lesions, considering different preloading forces. The deformation compensation strategy allows to improve localization accuracy, and as a consequence final probe positioning, for all considered lesions. Median lesion localization error is 3.3 mm, which is lower than the median lesion radius, when applying a preloading of 2 N, which guarantees both minimal needle insertion error and tissue stress.

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Concepts and Trends in Autonomy for Robot-Assisted Surgery

Cited by 31 publications

References 121 publications

Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer

Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer

In silico simulation: a key enabling technology for next-generation intelligent surgical systems

Autonomous Robotic System for Breast Biopsy With Deformation Compensation

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