Simulation-based training is increasingly being used for assessment and training of psychomotor skills involved in medicine. The application of artificial intelligence and machine learning technologies has provided new methodologies to utilize large amounts of data for educational purposes. A significant criticism of the use of artificial intelligence in education has been a lack of transparency in the algorithms’ decision-making processes. This study aims to 1) introduce a new framework using explainable artificial intelligence for simulation-based training in surgery, and 2) validate the framework by creating the Virtual Operative Assistant, an automated educational feedback platform. Twenty-eight skilled participants (14 staff neurosurgeons, 4 fellows, 10 PGY 4–6 residents) and 22 novice participants (10 PGY 1–3 residents, 12 medical students) took part in this study. Participants performed a virtual reality subpial brain tumor resection task on the NeuroVR simulator using a simulated ultrasonic aspirator and bipolar. Metrics of performance were developed, and leave-one-out cross validation was employed to train and validate a support vector machine in Matlab. The classifier was combined with a unique educational system to build the Virtual Operative Assistant which provides users with automated feedback on their metric performance with regards to expert proficiency performance benchmarks. The Virtual Operative Assistant successfully classified skilled and novice participants using 4 metrics with an accuracy, specificity and sensitivity of 92, 82 and 100%, respectively. A 2-step feedback system was developed to provide participants with an immediate visual representation of their standing related to expert proficiency performance benchmarks. The educational system outlined establishes a basis for the potential role of integrating artificial intelligence and virtual reality simulation into surgical educational teaching. The potential of linking expertise classification, objective feedback based on proficiency benchmarks, and instructor input creates a novel educational tool by integrating these three components into a formative educational paradigm.

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The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine

‘Global surgery’ is the term adopted to describe a rapidly developing multidisciplinary field aiming to provide improved and equitable surgical care across international health systems. Sitting at the interface between numerous clinical and non-clinical specialisms, it encompasses multiple aspects that surround the treatment of surgical disease and its equitable provision across health systems globally. From defining the role of, and need for, optimal surgical care through to identifying barriers and implementing improvement, global surgery has an expansive remit. Advocacy, education, research and clinical components can all involve surgeons, anaesthetists, nurses and allied healthcare professionals working together with non-clinicians, including policy makers, epidemiologists and economists. Long neglected as a topic within the global and public health arenas, an increasing awareness of the extreme disparities internationally has driven greater engagement. Not necessarily restricted to specific diseases, populations or geographical regions, these disparities have led to a particular focus on surgical care in low-income and middle-income countries with the greatest burden and needs. This review considers the major factors defining the interface between surgery, anaesthesia and public health in these settings.

https://gh.bmj.com/content/bmjgh/4/5/e001808.full.pdf

What Is 'Global Surgery'? Defining the Multidisciplinary Interface Between Surgery, Anaesthesia and Public Health

Electrocorticographic brain computer interfaces (ECoG-BCIs) offer tremendous opportunities for restoring function in individuals suffering from neurological damage and for advancing basic neuroscience knowledge. ECoG electrodes are already commonly used clinically for monitoring epilepsy and have greater spatial specificity in recording neuronal activity than techniques such as electroencephalography (EEG). Much work to date in the field has focused on using ECoG signals recorded from cortex as control outputs for driving end effectors. An equally important but less explored application of an ECoG-BCI is directing input into cortex using ECoG electrodes for direct electrical stimulation (DES). Combining DES with ECoG recording enables a truly bidirectional BCI, where information is both read from and written to the brain. We discuss the advantages and opportunities, as well as the barriers and challenges presented by using DES in an ECoG-BCI. In this article, we review ECoG electrodes, the physics and physiology of DES, and the use of electrical stimulation of the brain for the clinical treatment of disorders such as epilepsy and Parkinson's disease. We briefly discuss some of the translational, regulatory, financial, and ethical concerns regarding ECoG-BCIs. Next, we describe the use of ECoG-based DES for providing sensory feedback and for probing and modifying cortical connectivity. We explore future directions, which may draw on invasive animal studies with penetrating and surface electrodes as well as non-invasive stimulation methods such as transcranial magnetic stimulation (TMS). We conclude by describing enabling technologies, such as smaller ECoG electrodes for more precise targeting of cortical areas, signal processing strategies for simultaneous stimulation and recording, and computational modeling and algorithms for tailoring stimulation to each individual brain.

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Direct Electrical Stimulation in Electrocorticographic Brain-Computer Interfaces: Enabling Technologies for Input to Cortex.

Closed-loop or intelligent neuromodulation allows adjustable, personalized neuromodulation which usually incorporates the recording of a biomarker, followed by implementation of an algorithm which decides the timing (when?) and strength (how much?) of stimulation. Closed-loop neuromodulation has been shown to have greater benefits compared to open-loop neuromodulation, particularly for therapeutic applications such as pharmacoresistant epilepsy, movement disorders and potentially for psychological disorders such as depression or drug addiction. However, an important aspect of the technique is selection of an appropriate, preferably neural biomarker. Neurochemical sensing can provide high resolution biomarker monitoring for various neurological disorders as well as offer deeper insight into neurological mechanisms. The chemicals of interest being measured, could be ions such as potassium (K+), sodium (Na+), calcium (Ca2+), chloride (Cl-), hydrogen (H+) or neurotransmitters such as dopamine, serotonin and glutamate. This review focusses on the different building blocks necessary for a neurochemical, closed-loop neuromodulation system including biomarkers, sensors and data processing algorithms. Furthermore, it also highlights the merits and drawbacks of using this biomarker modality.

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Closed-Loop Implantable Therapeutic Neuromodulation Systems Based on Neurochemical Monitoring.

Background Virtual reality surgical simulators provide a safe environment for trainees to practice specific surgical scenarios and allow for self-guided learning. Artificial intelligence technology, including artificial neural networks, offers the potential to manipulate large datasets from simulators to gain insight into the importance of specific performance metrics during simulated operative tasks. Objective To distinguish performance in a virtual reality-simulated anterior cervical discectomy scenario, uncover novel performance metrics, and gain insight into the relative importance of each metric using artificial neural networks. Methods Twenty-one participants performed a simulated anterior cervical discectomy on the novel virtual reality Sim-Ortho simulator. Participants were divided into 3 groups, including 9 post-resident, 5 senior, and 7 junior participants. This study focused on the discectomy portion of the task. Data were recorded and manipulated to calculate metrics of performance for each participant. Neural networks were trained and tested and the relative importance of each metric was calculated. Results A total of 369 metrics spanning 4 categories (safety, efficiency, motion, and cognition) were generated. An artificial neural network was trained on 16 selected metrics and tested, achieving a training accuracy of 100% and a testing accuracy of 83.3%. Network analysis identified safety metrics, including the number of contacts on spinal dura, as highly important. Conclusion Artificial neural networks classified 3 groups of participants based on expertise allowing insight into the relative importance of specific metrics of performance. This novel methodology aids in the understanding of which components of surgical performance predominantly contribute to expertise.

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Artificial Neural Networks to Assess Virtual Reality Anterior Cervical Discectomy Performance

Prediabetes was a condition where a person has abnormal blood sugar levels but was not yet categorized as diabetes (140-199 mg / dl). The increased incidence of prediabetes affects the increasing number of cases of diabetes, unhealthy diet and irregular exercise models are factors that cause weight problems and blood sugar levels that increase cases of prediabetes to have an impact on diabetes cases. The research purpose was the effect of diet menus and exercise models on reducing blood sugar levels and body weight in the prediabetes group. The method research was the analytical method with the design of quasi-experimental, the population was 20 people, and the sample was total population or sample of 20 people by dividing 2 groups with different interventions, the technique with accidental sampling, and tool with SPSS 20, the data analyzed with independent t test. The result of research was the influence in group jasmine (diet and exercise menu (jogging)) (P value = 0.001 <α = 0.05), and in group rose (diet and exercise menu (casual walk)) (P value = 0.004 <α = 0.05) to reduce blood sugar levels and body weight. The conclusion of research was that the diet control and exercise menu models were able to influence the decrease in blood sugar levels and body weight in the prediabetes group, group a had a more significant effect because of jogging so that more calories burned.

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Rina Wati

Papers

The Influence of Diet Menus and Sports Models on Decreased Blood Sugar Levels and Body Weight of Prediabetes Group