Can artificial intelligence and robotic nurses replace operating room nurses? The quasi-experimental research

Operating room nurses, who have an important place in the professional healthcare team, should be able to use technology effectively and adapt to innovations. This study is expected to shed light on how effective the development of robotic technologies and artificial intelligence and their integration into operating room nursing will be in fulfilling the requirements of contemporary nursing philosophy. This study was designed as a single group pre- and post-test quasi-experimental study. The quasi-experimental (pretest-posttest) research design was utilized to conduct the study in a Training and Research Hospital in Western Turkey. The nurses (n = 35) working in the operating room of the aforementioned hospital were included in the study. In this study, we aimed to determine whether operating room nurses experienced anxiety due to the use of artificial intelligence and robotic nurses, and the effectiveness of the training given to them in order to raise their awareness. The following three tools were used for data collection: The Nurses' Descriptive Characteristics Form, Artificial Intelligence Knowledge Questionnaire, and Artificial Intelligence Anxiety Scale. Data extraction and analysis were performed in a narrative and tabular way. According to this study, the training given to the operating room nurses significantly increased their knowledge levels about artificial intelligence and robotic nurses, and increased their artificial intelligence- and robotic nurse-related anxiety significantly (p < 0.05). The participating operating room nurses experienced limitations regarding current information, training programs and learning opportunities on robotic surgery. We recommend that the operating room nurses should be provided with trainings on artificial intelligence technologies and robotic nurses, and that they should be enabled to use these information technologies regarding future technologies actively.

Deep-learning-based instrument detection for intra-operative robotic assistance

Purpose:

Robotic scrub nurses have the potential to become an attractive solution for the operating room. Surgical instrument detection is a fundamental task for these systems, which is the focus of this work. We address the detection of the complete surgery set for wisdom teeth extraction, and propose a data augmentation technique tailored for this task.

Methods:

Using a robotic scrub nurse system, we create a dataset of 369 unique multi-instrument images with manual annotations. We then propose the Mask-Based Object Insertion method, capable of automatically generating a large amount of synthetic images. By using both real and artificial data, different Mask R-CNN models are trained and evaluated.

Results:

Our experiments reveal that models trained on the synthetic data created with our method achieve comparable performance to that of models trained on real images. Moreover, we demonstrate that the combination of real and our artificial data can lead to a superior level of generalization.

Conclusion:

The proposed data augmentation technique is capable of dramatically reducing the labelling work required for training a deep-learning-based detection algorithm. A dataset for the complete instrument set for wisdom teeth extraction is made available for the scientific community, as well as the raw information required for the generation of the synthetic data (https://github.com/Jorebs/Deep-learning-based-instrument-detection-for-intra operative-robotic-assistance).