Animal research models in cardiovascular research are highly complex and surgically challenging. The induction of, for example, a myocardial infarction, stent implantation or arterial ligation is highly invasive and requires surgical precision, routine and perfect knowledge of animal anatomy. More often than not, surgical novices struggle significantly during the learning phase due to insufficient instructional material prior to their first surgery, poor guidance/supervision during surgery, and lack of error analysis following the surgery. As a result, learning curves of researchers and students using traditional teaching methods (learning-by-doing) are flat, requiring massive numbers of training animals before mastering one surgical method. Furthermore, the number of training animals scales with the complexity and invasiveness of the surgical procedure, leading to the high dropout rates.
We propose a digitally assisted learning application, which improves on all drawbacks of traditional learning methods using innovations in media design, surgical assistance, software development and microscopy. Presurgical method dissection will be supported by interactive instructional (3D-)videos, which present a detailed surgical workflow, most frequent mistakes, DOs and DON’Ts, complications, risks and their mitigations. During surgery, 3D-Augmented Reality Microscopes will be employed to support surgery novices with depth vision and by allowing remote supervision by a surgical expert with feedback in the microscopy viewfinder. Depth perception generates better contrast and anatomical information, thereby reducing the risk of malpractice and complications. Video streaming and remote supervision reduces the personnel requirements and parallelizes personal guidance during simultaneous surgeries. Additionally, Augmented Reality Microscopes provide the surgery supervisors the chance to assist digitally in real-time by inserting annotations and drawings into the field of view of the surgeon, greatly enhancing their learning experience. Postsurgical examination are accomplished using the same video streaming platform, recording and assigning surgeon specific video databases.
In this multi-phase approach, we aim to 1) replace animals for surgical demonstrations using smart and interactive instruction videos, 2) refine the handling thanks to our multilayered teaching platform by simultaneously personalizing and parallelizing the teaching process and as a result 3) drastically reducing the number of required training animals.