A cognitive engineering framework for the specification of information requirements in medical imaging: application in image-guided neurosurgery

Registration Sanity Check for AR-guided Surgical Interventions: Experience From Head and Face Surgery

Achieving and maintaining proper image registration accuracy is an open challenge of image-guided surgery. This work explores and assesses the efficacy of a registration sanity check method for augmented reality-guided navigation (AR-RSC), based on the visual inspection of virtual 3D models of landmarks. We analyze the AR-RSC sensitivity and specificity by recruiting 36 subjects to assess the registration accuracy of a set of 114 AR images generated from camera images acquired during an AR-guided orthognathic intervention. Translational or rotational errors of known magnitude up to ±1.5 mm/±15.5°, were artificially added to the image set in order to simulate different registration errors. This study analyses the performance of AR-RSC when varying (1) the virtual models selected for misalignment evaluation (e. g., the model of brackets, incisor teeth, and gingival margins in our experiment), (2) the type (translation/rotation) of registration error, and (3) the level of user experience in using AR technologies. Results show that: 1) the sensitivity and specificity of the AR-RSC depends on the virtual models (globally, a median true positive rate of up to 79.2% was reached with brackets, and a median true negative rate of up to 64.3% with incisor teeth), 2) there are error components that are more difficult to identify visually, 3) the level of user experience does not affect the method. In conclusion, the proposed AR-RSC, tested also in the operating room, could represent an efficient method to monitor and optimize the registration accuracy during the intervention, but special attention should be paid to the selection of the AR data chosen for the visual inspection of the registration accuracy.

Task management skills and their deficiencies during care delivery in simulated medical emergency situation: A classification

OBJECTIVES

Our objective was to develop the analysis of task management skills by proposing a framework classifying task management stages and deficiencies. Few studies of non-technical skills have detailed the components of task management skills through behavioural markers, despite their central role in care delivery.

RESEARCH METHODOLOGY/DESIGN

A post hoc qualitative behavioural analysis was performed of recordings made of professional training sessions based upon simulated scenarios.

SETTING

Four recorded sessions in a high-fidelity simulation setting were observed and recorded. Two scenarios were used (cardiac arrest and respiratory failure), and there were two training sessions per scenario.

MAIN OUTCOMES MEASURES

Four types of task management deficiencies were identified with regards to task constraints: constraint relaxation, unsatisfied constraints, additional constraints and constraint transgression. Both equipment and space constraints were also identified.

RESULTS

The lack of prerequisite actions when preparing the environment, corequisite actions for equipment and protocol monitoring, or postrequisite actions to restore the environment were associated with task management deficiencies.

CONCLUSION

Deficiencies in task management behaviours can be identified in simulated as well as actual medical emergency settings. This framework opens perspectives for both training caregivers and designing ergonomic work situations.

Work domain constraints for modelling surgical performance

PURPOSE

Three main approaches can be identified for modelling surgical performance: a competency-based approach, a task-based approach, both largely explored in the literature, and a less known work domain-based approach. The work domain-based approach first describes the work domain properties that constrain the agent's actions and shape the performance. This paper presents a work domain-based approach for modelling performance during cervical spine surgery, based on the idea that anatomical structures delineate the surgical performance. This model was evaluated through an analysis of junior and senior surgeons' actions.

METHOD

Twenty-four cervical spine surgeries performed by two junior and two senior surgeons were recorded in real time by an expert surgeon. According to a work domain-based model describing an optimal progression through anatomical structures, the degree of adjustment of each surgical procedure to a statistical polynomial function was assessed.

RESULTS

Each surgical procedure showed a significant suitability with the model and regression coefficient values around 0.9. However, the surgeries performed by senior surgeons fitted this model significantly better than those performed by junior surgeons. Analysis of the relative frequencies of actions on anatomical structures showed that some specific anatomical structures discriminate senior from junior performances.

CONCLUSION

The work domain-based modelling approach can provide an overall statistical indicator of surgical performance, but in particular, it can highlight specific points of interest among anatomical structures that the surgeons dwelled on according to their level of expertise.