Origins of Surgical Robotics: From Space to the Operating Room

Takács, Árpád; Nagy, Dénes Ákos; Rudas, Imre J.; Haidegger, Tamás

doi:10.12700/aph.13.1.2016.1.3

Cited by 22 publications

(3 citation statements)

References 25 publications

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“…So far, there have been five generations of dVSS; da Vinci Classic, da Vinci S, da Vinci Si, da Vinci X, and da Vinci Xi. The basic concept has remained the same in all generations, however, the platform has improved with each model [ 26 ]. All systems comprise a surgeon’s cart, where the surgeon sits; a patient cart, where the instruments are fixed; and a vision cart that links all components together [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

Acquisition and usage of robotic surgical data for machine learning analysis

et al. 2023

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Background The increasing use of robot-assisted surgery (RAS) has led to the need for new methods of assessing whether new surgeons are qualified to perform RAS, without the resource-demanding process of having expert surgeons do the assessment. Computer-based automation and artificial intelligence (AI) are seen as promising alternatives to expert-based surgical assessment. However, no standard protocols or methods for preparing data and implementing AI are available for clinicians. This may be among the reasons for the impediment to the use of AI in the clinical setting. Method We tested our method on porcine models with both the da Vinci Si and the da Vinci Xi. We sought to capture raw video data from the surgical robots and 3D movement data from the surgeons and prepared the data for the use in AI by a structured guide to acquire and prepare video data using the following steps: ‘Capturing image data from the surgical robot’, ‘Extracting event data’, ‘Capturing movement data of the surgeon’, ‘Annotation of image data’. Results 15 participant (11 novices and 4 experienced) performed 10 different intraabdominal RAS procedures. Using this method we captured 188 videos (94 from the surgical robot, and 94 corresponding movement videos of the surgeons’ arms and hands). Event data, movement data, and labels were extracted from the raw material and prepared for use in AI. Conclusion With our described methods, we could collect, prepare, and annotate images, events, and motion data from surgical robotic systems in preparation for its use in AI.

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Section: Methodsmentioning

confidence: 99%

Acquisition and usage of robotic surgical data for machine learning analysis

et al. 2023

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“…MIS requires extensive skills, so that surgeons are able to master the specifically designed precision tools. Multiple publications [7][8][9][10] indicate that the use of these tools in tandem with the endoscopic camera results in sub-optimal ergonomics, due to the limitations of human joints or because of the increased complexity in hand-eye coordination. The monitoring screen is also hard to place ergonomically.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Endoscopic Image-Based Skill Assessment in Robot-Assisted Minimally Invasive Surgery

Lajkó

Elek

Haidegger

2021

Sensors

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Objective skill assessment-based personal performance feedback is a vital part of surgical training. Either kinematic—acquired through surgical robotic systems, mounted sensors on tooltips or wearable sensors—or visual input data can be employed to perform objective algorithm-driven skill assessment. Kinematic data have been successfully linked with the expertise of surgeons performing Robot-Assisted Minimally Invasive Surgery (RAMIS) procedures, but for traditional, manual Minimally Invasive Surgery (MIS), they are not readily available as a method. 3D visual features-based evaluation methods tend to outperform 2D methods, but their utility is limited and not suited to MIS training, therefore our proposed solution relies on 2D features. The application of additional sensors potentially enhances the performance of either approach. This paper introduces a general 2D image-based solution that enables the creation and application of surgical skill assessment in any training environment. The 2D features were processed using the feature extraction techniques of a previously published benchmark to assess the attainable accuracy. We relied on the JHU–ISI Gesture and Skill Assessment Working Set dataset—co-developed by the Johns Hopkins University and Intuitive Surgical Inc. Using this well-established set gives us the opportunity to comparatively evaluate different feature extraction techniques. The algorithm reached up to 95.74% accuracy in individual trials. The highest mean accuracy—averaged over five cross-validation trials—for the surgical subtask of Knot-Tying was 83.54%, for Needle-Passing 84.23% and for Suturing 81.58%. The proposed method measured well against the state of the art in 2D visual-based skill assessment, with more than 80% accuracy for all three surgical subtasks available in JIGSAWS (Knot-Tying, Suturing and Needle-Passing). By introducing new visual features—such as image-based orientation and image-based collision detection—or, from the evaluation side, utilising other Support Vector Machine kernel methods, tuning the hyperparameters or using other classification methods (e.g., the boosted trees algorithm) instead, classification accuracy can be further improved. We showed the potential use of optical flow as an input for RAMIS skill assessment, highlighting the maximum accuracy achievable with these data by evaluating it with an established skill assessment benchmark, by evaluating its methods independently. The highest performing method, the Residual Neural Network, reached means of 81.89%, 84.23% and 83.54% accuracy for the skills of Suturing, Needle-Passing and Knot-Tying, respectively.

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“…Medical ultrasound (US) has quickly gained popularity as a primary diagnostic imaging modality, since it is non-invasive and widely available. It played a major role in the rapid advancement of Computer-Integrated Surgery [1]. The US devices developed in the last years are getting smaller and more portable, relying on revolutionary multi-transducer matrices and crystal arrays; however their usage, and especially the interpretation of the images still relies heavily on the personal qual-ities of the human sonographer.…”

Section: Introductionmentioning

confidence: 99%

Simulated Medical Ultrasound Trainers A Review of Solutions and Applications

Urbán¹,

Galambos²,

Györök³

et al. 2018

ACTA POLYTECH HUNG

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Ultrasound is one of the most widely employed real-time diagnostic imaging modalities in modern medicine. To use it efficiently, and to correctly interpret the images, the medical staff needs to acquire sophisticated skills. In this article, a review is provided on the devices and methods of modern ultrasonography training employing high-end information technology tools. It spans from the most critical moments, examination, to image-based training methods. Hardware and software based solutions are introduced along their current limitations. A comprehensive overview is provided about the most popular ultrasound simulators based on a common set of criteria, including their basic features, simulation methods, training concept and the supported scanning protocols. Tutors shall be able to make better informed decisions based on the enlisted characteristics of the various systems. The principles of simulation methods and techniques are also discussed in details along with the challenges of the field.

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Origins of Surgical Robotics: From Space to the Operating Room

Cited by 22 publications

References 25 publications

Acquisition and usage of robotic surgical data for machine learning analysis

Acquisition and usage of robotic surgical data for machine learning analysis

Endoscopic Image-Based Skill Assessment in Robot-Assisted Minimally Invasive Surgery

Simulated Medical Ultrasound Trainers A Review of Solutions and Applications

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