A Thorough Insight to Techniques for Performance Evaluation in Biological Sensors

The biological sensor has played a significant and contributory role in the area of medical science and healthcare industry. Owing to critical healthcare usage, it is essential that such type of sensors should be highly robust, sustainable under the adverse condition and highly fault tolerant against any forms of possible system failure in future. A massive amount of research work has been done in the area of the sensor network. However, works done in biological sensors are quite less in number. Hence, this manuscript highlights all the significant research work towards the line of discussion for evaluating the effective in the techniques for performance evaluation of biological sensor. The study finally explores the problems and discusses it under research gap. Finally, the manuscript gives highlights of the future direction of the work to solve the research gap explored from the proposed review of the existing system

A Suture Training System with Synchronized Force, Motion and Video Data Collection

Suturing is a common surgical task where surgeons stitch a particular tissue. There is an increasing demand for a tool to objectively quantify and train surgical skills. Suturing is particularly difficult to teach due to various multi-modal aspects involved in the task including applied forces, hand motion and optimal time for suturing. Towards quantifying the task of suturing, a platform is required to capture force, motion and video data while performing surgical suturing. This objective data can potentially be used to evaluate performance of a trainee and provide feedback regarding improving suturing skill. In the previous prototype of the platform, 3 key issues faced were synchronization of the three sensors, inadequate construction of the platform and the lack of a framework for image processing towards real-time assessment of suturing skill. In order to improve the platform, the aforementioned issues have been addressed in specific ways. The data collected in the system is synchronized in real-time along with a video recording for image processing and the noise due to the platform is considerably reduced by making modification to the platform construction. Data was collected on the platform with 15 novice participants. Initial analysis validates the synchronization of the sensor data. In the future, the suture skill of experts and novices will be analyzed using meaningful metrics and machine learning algorithms. This work has the potential of enabling objective and structured training and evaluation for next generation surgeons

Development of a Suturing Simulation Device for Synchronous Acqusition of Data

There have been tremendous technological advancements in the field of surgery with new devices and minimally invasive techniques rapidly being developed. As a result, there is a corresponding need to train novice surgeons and residents to use these new technologies. Due to new regulations in medical education, an increasing the amount of surgical skills training is designed for outside the operation room using surgical simulators. In this work, a device called the suture platform was conceptualized for assessing and training basic suturing skills of medical students and novice surgeons. In the traditional approach of “open” surgery, which has not benefitted as much from simulation, suturing is one of the most foundational surgical maneuvers. The specific task developed on the suture platform is called radial suturing and was prescribed by expert surgeons as one of five core “open” vascular skills. In the initial phase of the platform development, a six-axis force sensor was used to obtain data on the device and the procedure was video-recorded for analysis. Pilot data was analyzed using force-based parameters (e.g. peak force) and temporal parameters with the goal of examining if experts were distinguished from novices. During analysis, it became apparent that future development of the device should focus on obtaining synchronized data from video and other sensors. In the next phase of development, a motion sensor was added to capture wrist motion of the trainee and to obtain richer information of the suturing process. The current system consists of a graphical user interface (GUI) that captures data during a radial suturing task that can be analyzed using force, motion and vision metrics to assess and inform surgical suturing skill training

Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videos.

Ultrasound is a widely used imaging modality, yet it is well-known that scanning can be highly operator-dependent and difficult to perform, which limits its wider use in clinical practice. The literature on understanding what makes clinical sonography hard to learn and how sonography varies in the field is sparse, restricted to small-scale studies on the effectiveness of ultrasound training schemes, the role of ultrasound simulation in training, and the effect of introducing scanning guidelines and standards on diagnostic image quality. The Big Data era, and the recent and rapid emergence of machine learning as a more mainstream large-scale data analysis technique, presents a fresh opportunity to study sonography in the field at scale for the first time. Large-scale analysis of video recordings of full-length routine fetal ultrasound scans offers the potential to characterise differences between the scanning proficiency of experts and trainees that would be tedious and time-consuming to do manually due to the vast amounts of data. Such research would be informative to better understand operator clinical workflow when conducting ultrasound scans to support skills training, optimise scan times, and inform building better user-machine interfaces. This paper is to our knowledge the first to address sonography data science, which we consider in the context of second-trimester fetal sonography screening. Specifically, we present a fully-automatic framework to analyse operator clinical workflow solely from full-length routine second-trimester fetal ultrasound scan videos. An ultrasound video dataset containing more than 200 hours of scan recordings was generated for this study. We developed an original deep learning method to temporally segment the ultrasound video into semantically meaningful segments (the video description). The resulting semantic annotation was then used to depict operator clinical workflow (the knowledge representation). Machine learning was applied to the knowledge representation to characterise operator skills and assess operator variability. For video description, our best-performing deep spatio-temporal network shows favourable results in cross-validation (accuracy: 91.7%), statistical analysis (correlation: 0.98, p < 0.05) and retrospective manual validation (accuracy: 76.4%). For knowledge representation of operator clinical workflow, a three-level abstraction scheme consisting of a Subject-specific Timeline Model (STM), Summary of Timeline Features (STF), and an Operator Graph Model (OGM), was introduced that led to a significant decrease in dimensionality and computational complexity compared to raw video data. The workflow representations were learnt to discriminate between operator skills, where a proposed convolutional neural network-based model showed most promising performance (cross-validation accuracy: 98.5%, accuracy on unseen operators: 76.9%). These were further used to derive operator-specific scanning signatures and operator variability in terms of type, order and time distribution of constituent tasks

Motor learning induced neuroplasticity in minimally invasive surgery

Technical skills in surgery have become more complex and challenging to acquire since the introduction of technological aids, particularly in the arena of Minimally Invasive Surgery. Additional challenges posed by reforms to surgical careers and increased public scrutiny, have propelled identification of methods to assess and acquire MIS technical skills. Although validated objective assessments have been developed to assess motor skills requisite for MIS, they poorly understand the development of expertise. Motor skills learning, is indirectly observable, an internal process leading to relative permanent changes in the central nervous system. Advances in functional neuroimaging permit direct interrogation of evolving patterns of brain function associated with motor learning due to the property of neuroplasticity and has been used on surgeons to identify the neural correlates for technical skills acquisition and the impact of new technology. However significant gaps exist in understanding neuroplasticity underlying learning complex bimanual MIS skills. In this thesis the available evidence on applying functional neuroimaging towards assessment and enhancing operative performance in the field of surgery has been synthesized. The purpose of this thesis was to evaluate frontal lobe neuroplasticity associated with learning a complex bimanual MIS skill using functional near-infrared spectroscopy an indirect neuroimaging technique. Laparoscopic suturing and knot-tying a technically challenging bimanual skill is selected to demonstrate learning related reorganisation of cortical behaviour within the frontal lobe by shifts in activation from the prefrontal cortex (PFC) subserving attention to primary and secondary motor centres (premotor cortex, supplementary motor area and primary motor cortex) in which motor sequences are encoded and executed. In the cross-sectional study, participants of varying expertise demonstrate frontal lobe neuroplasticity commensurate with motor learning. The longitudinal study involves tracking evolution in cortical behaviour of novices in response to receipt of eight hours distributed training over a fortnight. Despite novices achieving expert like performance and stabilisation on the technical task, this study demonstrates that novices displayed persistent PFC activity. This study establishes for complex bimanual tasks, that improvements in technical performance do not accompany a reduced reliance in attention to support performance. Finally, least-squares support vector machine is used to classify expertise based on frontal lobe functional connectivity. Findings of this thesis demonstrate the value of interrogating cortical behaviour towards assessing MIS skills development and credentialing.Open Acces

Analyse der Lernkurve und der applizierten Kräfte am Gewebe in der Roboter-assistierten Chirurgie

Die Roboter-assistierte Laparoskopie besitzt das Potenzial einer disruptiven Technologie, dennoch verbleiben trotz vielversprechender Entwicklungen etliche Fragestellungen bislang ungeklärt. Eine dieser Hypothesen ist, dass Operationsteams mit robotischen Assistenzsystemen eine zeitintensive Lernkurve durchschreiten. Um dies genauer zu untersuchen, wurde die Lernkurve roboterunterstützter Operationen anhand der Prozedurzeiten bei den ersten 20 Fällen der Roboter-assistierten ventralen Rektopexie am Universitätsklinikum Tübingen beschrieben. Der Begriff Lernkurve wurde anhand des quantitativen Parameters Zeit als Umschwung einer Lern- in eine Plateauphase definiert. Für die Gesamtdauer konnte ein Erreichen dieses stabilen Zustands nach fünf Eingriffen gezeigt werden. Diese Ergebnisse sind nicht zwangsläufig auf andere Teams und andere Szenarien übertragbar, suggerieren dennoch eine zügig mögliche Etablierung von Robotiksystemen. Ein weiterer relevanter Aspekt der Roboter-assistierten Chirurgie ist der genau dosierte Einsatz von Kräften. Um die eingebrachten Kräfte in der Gewebe-Instrumenten-Interaktion objektivieren zu können, wurde in einer zweiten wissenschaftlichen Zielsetzung die Konzeption, Entwicklung, Konstruktion, Programmierung und Anwendungsdemonstration eines Messsystems zur Erfassung räumlicher Position und applizierter Kräfte in der Laparoskopie beschrieben. Der auf einem kardanischen Gelenk basierende Kraft- und Positionsmessstand für laparoskopische Rohrschaftinstrumente wurde zuerst CAD-konstruiert und in mehreren Entwicklungsstufen als Prototyp umgesetzt. Hierfür kamen subtraktive und additive Fertigungsverfahren zum Einsatz. Integrierte Wägezellen dienen der Kraftmessung, während Potentiometer die Absolutposition erfassen. Die Datenerfassung erfolgt durch einen programmierbaren Mikrokontroller und die Datenausgabe ist über eine Kontrollkonsole sowie optional über einen angeschlossenen PC möglich. In der realitätsnahen Anwendungsdemonstration am Organmodell wurden die applizierten Kräfte bis zum Kontaktverlust von Instrument und Gewebe bei Zugmanövern am Hartmann-Pouch im Rahmen einer Cholezystektomie erfasst. Insgesamt wurden an sechs Organmodellen jeweils sechs verschieden konfigurierte Fasszangen evaluiert. Die Fasszangen mit traumatischen Oberflächenprofil konnten hierbei signifikant die höchsten Maximalkräfte aufbringen. Neben der Evaluation von Instrumenten hinsichtlich der Eignung für chirurgische Eingriffe können die ermittelten Daten der Roboterchirurgie als Referenzwerte, beispielsweise für die Definition von Grenzwerten eingesetzter Maximalkräfte, dienen. Darüber hinaus stellt der Kraft- und Positionsmessstand Messparameter zur Verfügung, durch welche chirurgisches Training in Zukunft effektiver gestaltet werden kann

Increasing the efficiency of laparoscopic surgical training: assessing the effectiveness of training interventions

In the current project, our main focus was to test the effectiveness of different training interventions and their impact on skill acquisition and long-term retention of laparoscopic motor skills. Based on the research in this dissertation and the existing literature, I recommend instructors to design training with predetermined proficiency targets, patient-oriented (adaptive) feedback on a spaced schedule with intervals of a week instead of smaller time frames. Instructors may experiment with larger spacing intervals, but more research is needed to determine the effectiveness of more time in between training sessions. I urge instructors to be cautious in increasing training variability in training novices, since laparoscopy is an inherently complex task and can be overwhelming at the start of training. Fractionation of training of the different facets of laparoscopic surgery may be fine initially, but training focused on skill integration is desirable at a later stage. In examination, a dual-task setup can be used to assess the degree of automatization of the acquired skills. Trainers can implement the use of force measures as an additional metric, to ensure that trainees also learn how to handle different tissues safely.Gefinancierd vanuit het LUMC. Voor 1 studie is door MediShield B.V. tijdelijk een laparoscopie simulator ter beschikking gesteld. Dit staat bij het betreffende hoofdstuk in de tekst vermeld.LUMC / Geneeskunde Repositoriu

Assessment of Laparoscopic Skills Based on Force and Motion Parameters

Box trainers equipped with sensors may help in acquiring objective information about a trainee's performance while performing training tasks with real instruments. The main aim of this study is to investigate the added value of force parameters with respect to commonly used motion and time parameters such as path length, motion volume, and task time. Two new dynamic bimanual positioning tasks were developed that not only requiring adequate motion control but also appropriate force control successful completion. Force and motion data for these tasks were studied for three groups of participants with different experience levels in laparoscopy (i.e., 11 novices, 19 intermediates, and 12 experts). In total, 10 of the 13 parameters showed a significant difference between groups. When the data from the significant motion, time, and force parameters are used for classification, it is possible to identify the skills level of the participants with 100% accuracy. Furthermore, the force parameters of many individuals in the intermediate group exceeded the maximum values in the novice and expert group. The relatively high forces used by the intermediates argue for the inclusion of training and assessment of force application during tissue handling in future laparoscopic skills training programs.Biomechanical EngineeringMechanical, Maritime and Materials Engineerin