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PurposeIn the era of patient-centered medicine, clinical procedures, tools and instruments should be individually adapted to the patient. In this context, the presented 3D-printed Single-Port Overtube Manipulator System follows the aims to provide patient- and task-specific disposable manipulators for minimally invasive surgery. In a first experiment, the robustness of the monolithic flexure hinge structures in use as robotic manipulators will be investigated.MethodsCustomizable monolithic manipulator structures designed by means of an automated design process and manufactured with selective laser sintering were investigated with regard to long-term stability in an endurance test. Therefore, a bare manipulator arm, an arm equipped with a standard instrument and finally loaded with an additional load of 0.5 N were evaluated by continuously following a trajectory within the workspace of the manipulator arms over a period of 90 min.ResultsThe unloaded manipulator as well as the manipulator arm equipped with a standard instrument showed a sufficient reproducibility (deviation of 1.5 mm and 2.5 mm, respectively, on average) with regard to an application as telemanipulated master–slave surgical robotic system. The 3D-printed manipulators showed no damage and maintained integrity after the experiment.ConclusionIt has been shown that 3D-printed manipulators in principle are suitable for use as disposable surgical manipulator systems and offer a long-term stability over at least 90 min. The developed manipulator design shows great potential for the production of patient-, task- and user-specific robot systems. However, the manipulator geometries as well as the control strategies still show room for improvements.

Purpose Autonomously self-navigating clinical assistance systems (ASCAS) seem highly promising for improving clinical workflows. There is great potential for easing staff workload and improving overall efficiency by reducing monotonous and physically demanding tasks. However, a seamless integration of such systems into complex human-supervised clinical workflows is challenging. As of yet, guiding principles and specific approaches for solving this problem are lacking. Methods We propose to treat ASCAS orchestration as a scheduling problem. However, underlying objectives and constraints for this scheduling problem differ considerably from those found in other domains (e.g., manufacturing, logistics). We analyze the clinical environment to deduce unique needs and conclude that existing scheduling approaches are not sufficient to overcome these challenges. Results We present four guiding principles, namely human precedence, command structure, emergency context and immediacy, that govern the integration of self-navigating assistance systems into clinical workflows. Based on these results, we propose our approach, namely Auto-Navigation Task Scheduling for Operating Rooms (ANTS-OR), for solving the ASCAS orchestration problem in a surgical application scenario, employing a score-based scheduling strategy. Conclusion The proposed approach is a first step toward addressing the ASCAS orchestration problem for the OR wing. We are currently advancing and validating our concept using a simulation environment and aim at realizing a dynamic end-to-end ASCAS orchestration platform in the future.

Purpose Minimally invasive surgery (MIS) has become the standard for many surgical procedures as it minimizes trauma, reduces infection rates and shortens hospitalization. However, the manipulation of objects in the surgical workspace can be difficult due to the unintuitive handling of instruments and limited range of motion. Apart from the advantages of robot-assisted systems such as augmented view or improved dexterity, both robotic and MIS techniques introduce drawbacks such as limited haptic perception and their major reliance on visual perception. Methods In order to address the above-mentioned limitations, a perception study was conducted to investigate whether the transmission of intra-abdominal acoustic signals can potentially improve the perception during MIS. To investigate whether these acoustic signals can be used as a basis for further automated analysis, a large audio data set capturing the application of electrosurgery on different types of porcine tissue was acquired. A sliding window technique was applied to compute log-mel-spectrograms, which were fed to a pre-trained convolutional neural network for feature extraction. A fully connected layer was trained on the intermediate feature representation to classify instrument–tissue interaction. Results The perception study revealed that acoustic feedback has potential to improve the perception during MIS and to serve as a basis for further automated analysis. The proposed classification pipeline yielded excellent performance for four types of instrument–tissue interaction (muscle, fascia, liver and fatty tissue) and achieved top-1 accuracies of up to 89.9%. Moreover, our model is able to distinguish electrosurgical operation modes with an overall classification accuracy of 86.40%. Conclusion Our proof-of-principle indicates great application potential for guidance systems in MIS, such as controlled tissue resection. Supported by a pilot perception study with surgeons, we believe that utilizing audio signals as an additional information channel has great potential to improve the surgical performance and to partly compensate the loss of haptic feedback.

In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences. A novel attention regularization loss encourages the model to focus on high-quality frames during training. Moreover, the attention weights are utilized to identify characteristic high attention frames for each surgical phase, which could further be used for surgery summarization. OperA is thoroughly evaluated on two datasets of laparoscopic cholecystectomy videos, outperforming various state-of-the-art temporal refinement approaches.

PurposeWhile demand for telemedicine is increasing, patients are currently restricted to tele-consultation for the most part. Fundamental diagnostics like the percussion still require the in person expertize of a physician. To meet today’s challenges, a transformation of the manual percussion into a standardized, digital version, ready for telemedical execution is required.MethodsIn conjunction with a comprehensive telemedical diagnostic system, in which patients can get examined by aremote-physician, a series of three robotic end-effectors for mechanical percussion were developed. Comprising a motor, a magnetic and a pneumatic-based version, the devices strike a pleximeter to perform the percussion. Emitted sounds were captured using a microphone-equipped stethoscope. The 84 recordings were further integrated into a survey in order to classify lung and non-lung samples.ResultsThe study with 21 participants comprised physicians, medical students and non-medical-related raters in equal parts. With 71.4% correctly classified samples, the ventral motorized device prevailed. While the result is significantly better compared to a manual or pneumatic percussion in this very setup, it only has a small edge over the magnetic devices. In addition, for all ventral versions non-lung regions were rather correctly identified than lung regions.ConclusionThe overall setup proves the feasibility of a telemedical percussion. Despite the fact, that produced sounds differ compared to today’s manual technique, the study shows that a standardized mechanical percussion has the potential to improve the gold standard’s accuracy. While further extensive medical evaluation is yet to come, the system paves the way for future uncompromised remote examinations.

In a highly competitive environment, surgery is forced to continuously improve the outcome and, simultaneously to reduce costs. These contradicting aims can only be reached by the combined use of cyber-physical systems . Digitalization of surgery may be denominated as “surgery 4.0 ”. This process will be primarily focussed on the surgical operation room which is the “profit centre” of any surgical unit. The aim is to transform it into a “collaborative environment”. Based upon a multitude of continuous real-time data, a support system should be capable to interpret the actual situation (context sensivity) and to predict the next steps required. In addition to the necessary medical and organizational structured knowledge which has to be provided before, the system should be able to learn from repeated procedures. Thus, it should offer active assistance to the surgical team to use the technical environment adequately, to smoothen the workflow, to avoid mistakes, and to improve the safety level. To reach this goal, some preconditions have still to be met: Comprehensive systems integration, the development of surgical and patient models, and a perfect communication not only between the devices and instruments but also with the human user. Making this vision mature for regular clinical care is challenging but first promising approaches have already been developed.