[Intraoperative augmented reality visualization. Current state of development and initial experiences with the CamC]

Medical Augmented Reality: Definition, Principle Components, Domain Modeling, and Design-Development-Validation Process

Three decades after the first set of work on Medical Augmented Reality (MAR) was presented to the international community, and ten years after the deployment of the first MAR solutions into operating rooms, its exact definition, basic components, systematic design, and validation still lack a detailed discussion. This paper defines the basic components of any Augmented Reality (AR) solution and extends them to exemplary Medical Augmented Reality Systems (MARS). We use some of the original MARS applications developed at the Chair for Computer Aided Medical Procedures and deployed into medical schools for teaching anatomy and into operating rooms for telemedicine and surgical guidance throughout the last decades to identify the corresponding basic components. In this regard, the paper is not discussing all past or existing solutions but only aims at defining the principle components and discussing the particular domain modeling for MAR and its design-development-validation process, and providing exemplary cases through the past in-house developments of such solutions.

Mobile, real-time, and point-of-care augmented reality is robust, accurate, and feasible: a prospective pilot study

BACKGROUND

Augmented reality (AR) systems are currently being explored by a broad spectrum of industries, mainly for improving point-of-care access to data and images. Especially in surgery and especially for timely decisions in emergency cases, a fast and comprehensive access to images at the patient bedside is mandatory. Currently, imaging data are accessed at a distance from the patient both in time and space, i.e., at a specific workstation. Mobile technology and 3-dimensional (3D) visualization of radiological imaging data promise to overcome these restrictions by making bedside AR feasible.

METHODS

In this project, AR was realized in a surgical setting by fusing a 3D-representation of structures of interest with live camera images on a tablet computer using marker-based registration. The intent of this study was to focus on a thorough evaluation of AR. Feasibility, robustness, and accuracy were thus evaluated consecutively in a phantom model and a porcine model. Additionally feasibility was evaluated in one male volunteer.

RESULTS

In the phantom model (n = 10), AR visualization was feasible in 84% of the visualization space with high accuracy (mean reprojection error ± standard deviation (SD): 2.8 ± 2.7 mm; 95th percentile = 6.7 mm). In a porcine model (n = 5), AR visualization was feasible in 79% with high accuracy (mean reprojection error ± SD: 3.5 ± 3.0 mm; 95th percentile = 9.5 mm). Furthermore, AR was successfully used and proved feasible within a male volunteer.

CONCLUSIONS

Mobile, real-time, and point-of-care AR for clinical purposes proved feasible, robust, and accurate in the phantom, animal, and single-trial human model shown in this study. Consequently, AR following similar implementation proved robust and accurate enough to be evaluated in clinical trials assessing accuracy, robustness in clinical reality, as well as integration into the clinical workflow. If these further studies prove successful, AR might revolutionize data access at patient bedside.

Effects of mobile augmented reality learning compared to textbook learning on medical students: randomized controlled pilot study

BACKGROUND

By adding new levels of experience, mobile Augmented Reality (mAR) can significantly increase the attractiveness of mobile learning applications in medical education.

OBJECTIVE

To compare the impact of the heightened realism of a self-developed mAR blended learning environment (mARble) on learners to textbook material, especially for ethically sensitive subjects such as forensic medicine, while taking into account basic psychological aspects (usability and higher level of emotional involvement) as well as learning outcomes (increased learning efficiency).

METHODS

A prestudy was conducted based on a convenience sample of 10 third-year medical students. The initial emotional status was captured using the "Profile of Mood States" questionnaire (POMS, German variation); previous knowledge about forensic medicine was determined using a 10-item single-choice (SC) test. During the 30-minute learning period, the students were randomized into two groups: the first group consisted of pairs of students, each equipped with one iPhone with a preinstalled copy of mARble, while the second group was provided with textbook material. Subsequently, both groups were asked to once again complete the POMS questionnaire and SC test to measure changes in emotional state and knowledge gain. Usability as well as pragmatic and hedonic qualities of the learning material was captured using AttrakDiff2 questionnaires. Data evaluation was conducted anonymously. Descriptive statistics for the score in total and the subgroups were calculated before and after the intervention. The scores of both groups were tested against each other using paired and unpaired signed-rank tests. An item analysis was performed for the SC test to objectify difficulty and selectivity.

RESULTS

Statistically significant, the mARble group (6/10) showed greater knowledge gain than the control group (4/10) (Wilcoxon z=2.232, P=.03). The item analysis of the SC test showed a difficulty of P=0.768 (s=0.09) and a selectivity of RPB=0.2. For mARble, fatigue (z=2.214, P=.03) and numbness (z=2.07, P=.04) decreased with statistical significance when comparing pre- and post-tests. Vigor rose slightly, while irritability did not increase significantly. Changes in the control group were insignificant. Regarding hedonic quality (identification, stimulation, attractiveness), there were significant differences between mARble (mean 1.179, CI -0.440 to 0.440) and the book chapter (mean -0.982, CI -0.959 to 0.959); the pragmatic quality mean only differed slightly.

CONCLUSIONS

The mARble group performed considerably better regarding learning efficiency; there are hints for activating components of the mAR concept that may serve to fascinate the participants and possibly boost interest in the topic for the remainder of the class. While the small sample size reduces our study's conclusiveness, its design seems appropriate for determining the effects of interactive eLearning material with respect to emotions, learning efficiency, and hedonic and pragmatic qualities using a larger group.

TRIAL REGISTRATION

German Clinical Trial Register (DRKS), DRKS-ID: DRKS00004685; https://drks-neu.uniklinik-freiburg.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00004685.