Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical robotics. Current devices possess no intelligence whatsoever and are merely advanced and expensive instruments.

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Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions.

We present the first autonomous endoscope for the visual inspection of very small ducts and cavities, up to a 6-mm diameter. The system has been designed, implemented, and tested in a challenging industrial scenario and in strict collaboration with an avionic industry partner. The inspected objects are metallic gearboxes eventually presenting different residuals (e.g., sand, machining swarfs, and metallic dust) inside the oil ducts. The automatic system is actuated by a robotic arm that moves the endoscope with a microcamera inside the gearbox duct, while a deep-learning-based spatio-temporal image analysis module detects, classifies, and localizes defects in real time. Feedback is given to the robotic arm in order to move or extract the endoscope given the detected anomalies. Evaluation provides a detection rate of nearly $98$ % given different tests with different types of residuals and duct structures.

Deep Endoscope: Intelligent Duct Inspection for the Avionic Industry

瞄准：为了探索肿瘤的影响，与先进胃的癌症在病人在结果上缩放在更低第三个胃。方法：我们回顾地与先进胃的癌症分析了 430 个病人的临床的记录在更低第三个胃从 1998 年 1 月在我们的医院里经历了远侧的小计 gastrectomy 和 D2 lymphadenectomy 到 2004 年 6 月。操作接收装置的特征(巨鸟) 曲线分析被用来为肿瘤尺寸决定适当截止价值，它作为最大的肿瘤直径被测量。基于这截止价值，病人们被划分成二个组：那些与有小型的肿瘤(SST ) 的大尺寸的肿瘤(LST ) 和那些。在另外的 clinicopathologic 因素和肿瘤尺寸之间的关联被调查，并且 5 年的全面幸存(OS ) 率在二个组之间被比较。潜在的预示的因素被 univariate Kaplan-Meier 幸存分析和 multivariate 艇长比例的危险模型分析评估。在二个组的 5 年的 OS 率根据磅舞台和 pN 舞台被比较。结果：在有先进胃的癌症在的 430 个病人的 5 年的 OS 率更低第三个胃是 53.7% 。平均数 ???? 跱?????? t

Tumor size as a prognostic factor in patients with advanced gastric cancer in the lower third of the stomach

The invention relates to a method and a system for computer guided surgery comprising a transposition of an action, planned in a virtual environment with respect to a virtual referential Rp, to a physical action performed with a surgical tool (20) in a real operating theatre environment for orthopedic surgery of a patient.

System for computer guided surgery

Below we present a new method to obtain quantitative measurements in medical images of a tracked video endoscope. Such quantitative measurements can be critical biomarkers during endoscopic diagnosis, e.g. for the classification of tumors and polyps. Size-based classifications are often achieved by mere visual estimation which can be imprecise and subjective. To this day established systems of quantitative size measurement are restricted to specific anatomical structures and require additional tools next to the endoscope. We present a system of quantitative measurement during an endoscopic procedure based on a virtual model by utilizing methods from computer vision as well as structure from motion and combining those with an optical tracking system in a novel way. Our experiments show that quantitative measurements of anatomic structures can be reliably attained by our system with an accuracy which exceeds the reported accuracy of visual estimation.

Quantitative measurements of soft tissue structures using image-features in navigated endoscopy

Laparoscopic resection of kidney cancer has become more and more popular. Still, intraoperatively the surgeon has little information on the inside of the surgical field, increasing the risk of not completely resecting the tumor. Ultrasonography can overcome this problem. Therefore, we proposed a flat-panel ultrasound robot to motorize a transcutaneous ultrasound probe. In this manuscript we present three methods to steer the robot: a) telemanipulation, b) offline control, and c) autonomous mode. For telemanipulation we developed a joystick console. In offline control mode the robot is controlled by the computer and follows a predefined path. In autonomous mode the robot aligns it ultrasound probe automatically with the surgical field. In an experiment we were able to show that the robot can be manually steered significantly faster by a computer/mouse than with the joystick console. Additionally, the experiment unveiled that information on the current position of the the ultrasound probe helps the user significantly to pinpoint a target object with the robot.

A modular control concept for a flat-panel ultrasound robot

Computer assisted surgery requires a registration process that identifies the position of the patient within the operating room. A common approach is to attach reflective markers to the patient which are illuminated and detected by a stereo camera. In order to reduce the logistic effort of operation planning, it is desirable to perform an automatically marker-less registration. In order to implement a reliable system for surface scanning the measurement precision may neither vary significantly throughout the scanning volume, nor be influenced by the angle of incidence. Moreover, the system precision should not depend on the skin pigmentation. Therefore, we assessed the influence of different parameters on the precision at which laser spots can be detected on skin tissue with an NDI stereo camera. In this paper, we present novel results of coupling an NDI stereo camera with an 850nm laser beam and the precision that can be achieved with this approach.

Precision of near-infrared laser spot detection on different skin tissues featuring an NDI stereo camera

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Quantitative measurements of soft tissue structures using image-features in navigated endoscopy

A modular control concept for a flat-panel ultrasound robot

Precision of near-infrared laser spot detection on different skin tissues featuring an NDI stereo camera