Virtual Anatomical and Endoscopic Exploration Method of Internal Human Body for Training Simulator

Lymph Node Stations of Pancreas Which Are Identified in Real Color Sectioned Images of a Cadaver With Pancreatic Cancer

BACKGROUND

In pancreatic cancer surgery, anatomical understanding of lymph node metastases is required. Distinguishing lymph nodes in computed tomography or magnetic resonance imaging is challenging for novice doctors and medical students because of their small size and similar color to surrounding tissues. This study aimed to enhance our understanding of the clinical anatomy of lymph node stations relevant to pancreatic cancer using newly sectioned images of a cadaver with true color and high resolution and their three-dimensional (3D) models.

METHODS

An 88-year-old female cadaver who died of pancreatic cancer was serially sectioned. Among the sectioned images of the whole body (0.05 mm-sized pixel, 48 bits color), images of the abdomen were selected, and examined to identify lymph nodes and nearby structures. 34 structures (9 in digestive system; 1 in urinary system; 2 in cardiovascular system; 22 in lymphatic system) were segmented on the sectioned images. Based on the sectioned and segmented images, volume and surface models were produced.

RESULTS

Among the known 28 lymph node stations, 21 stations were identified through location, size, and color of normal and abnormal structures in the sectioned images and 3D models. Two near the splenic artery could not be separated from the cancer tissue, and the remaining five were not clearly identified. In the surface models, the shape and location of lymph node stations could be confirmed with nearby structures.

CONCLUSION

The lymph node stations relevant to pancreatic cancer can be anatomically understood by using the sectioned images and 3D models which contain true color and high resolution.

A virtual surgical prototype system based on gesture recognition for virtual surgical training in maxillofacial surgery

Background

Virtual reality (VR) technology is an ideal alternative of operation training and surgical teaching. However, virtual surgery is usually carried out using the mouse or data gloves, which affects the authenticity of virtual operation. A virtual surgery system with gesture recognition and real-time image feedback was explored to realize more authentic immersion.

Method

Gesture recognition technology proposed with an efficient and real-time algorithm and high fidelity was explored. The recognition of hand contour, palm and fingertip was firstly realized by hand data extraction. Then, an Support Vector Machine classifier was utilized to classify and recognize common gestures after extraction of feature recognition. The algorithm of collision detection adopted Axis Aligned Bounding Box binary tree to build hand and scalpel collision models. What’s more, nominal radius theorem (NRT) and separating axis theorem (SAT) were applied for speeding up collision detection. Based on the maxillofacial virtual surgical system we proposed before, the feasibility of integration of the above technologies in this prototype system was evaluated.

Results

Ten kinds of signal static gestures were designed to test gesture recognition algorithms. The accuracy of gestures recognition is more than 80%, some of which were over 90%. The generation speed of collision detection model met the software requirements with the method of NRT and SAT. The response time of gesture] recognition was less than 40 ms, namely the speed of hand gesture recognition system was greater than 25 Hz. On the condition of integration of hand gesture recognition, typical virtual surgical procedures including grabbing a scalpel, puncture site selection, virtual puncture operation and incision were carried out with realization of real-time image feedback.

Conclusion

Based on the previous maxillofacial virtual surgical system that consisted of VR, triangular mesh collision detection and maxillofacial biomechanical model construction, the integration of hand gesture recognition was a feasible method to improve the interactivity and immersion of virtual surgical operation training.

Virtual Reality in Medical Students' Education: Scoping Review

BACKGROUND

Virtual reality (VR) produces a virtual manifestation of the real world and has been shown to be useful as a digital education modality. As VR encompasses different modalities, tools, and applications, there is a need to explore how VR has been used in medical education.

OBJECTIVE

The objective of this scoping review is to map existing research on the use of VR in undergraduate medical education and to identify areas of future research.

METHODS

We performed a search of 4 bibliographic databases in December 2020. Data were extracted using a standardized data extraction form. The study was conducted according to the Joanna Briggs Institute methodology for scoping reviews and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.

RESULTS

Of the 114 included studies, 69 (60.5%) reported the use of commercially available surgical VR simulators. Other VR modalities included 3D models (15/114, 13.2%) and virtual worlds (20/114, 17.5%), which were mainly used for anatomy education. Most of the VR modalities included were semi-immersive (68/114, 59.6%) and were of high interactivity (79/114, 69.3%). There is limited evidence on the use of more novel VR modalities, such as mobile VR and virtual dissection tables (8/114, 7%), as well as the use of VR for nonsurgical and nonpsychomotor skills training (20/114, 17.5%) or in a group setting (16/114, 14%). Only 2.6% (3/114) of the studies reported the use of conceptual frameworks or theories in the design of VR.

CONCLUSIONS

Despite the extensive research available on VR in medical education, there continue to be important gaps in the evidence. Future studies should explore the use of VR for the development of nonpsychomotor skills and in areas other than surgery and anatomy.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2020-046986.

Advanced-sectioned images obtained by microsectioning of the entire male body

Realistic two-dimensional (2D) and three-dimensional (3D) applications for anatomical studies are being developed from true-colored sectioned images. We generated advanced-sectioned images of the entire male body and verified that anatomical structures of both normal and abnormal shapes could be visualized in them. The cadaver was serially sectioned at constant intervals using a cryomacrotome. The sectioned surfaces were photographed using a digital camera to generate horizontal advanced-sectioned images in which normal and abnormal structures were classified. Advanced-sectioned images of the entire male body were generated. The image resolution was 3.3 × 3.3 fold better than that of the first sectioned images obtained in 2002. In the advanced-sectioned images, normal and abnormal structures ranging from microscopic (≥0.06 mm × 0.06 mm; pixel size) to macroscopic (≤473.1 mm × 202 mm; body size) could be identified. Furthermore, the real shapes and actual sites of lung cancer and lymph node enlargement were ascertained in them. Such images will be useful because of their true color and high resolution in digital 2D and 3D applications for gross anatomy and clinical anatomy. In future, we plan to generate new advanced-sectioned images of abnormal cadavers with different diseases for clinical anatomy studies.

Adaptations in Anatomy Education during COVID-19

BACKGROUND

The impact of coronavirus disease 2019 (COVID-19) has profoundly affected education, with most universities changing face-to-face classes to online formats. To adapt to the COVID-19 pandemic situation, we adopted a blended learning approach to anatomy instruction that included online lectures, pre-recorded laboratory dissection videos, and 3D anatomy applications, with condensed offline cadaver dissection.

METHODS

We aimed to examine the learning outcomes of a newly adopted anatomy educational approach by 1) comparing academic achievement between the blended learning group (the 2020 class, 108 students) and the traditional classroom learning group (the 2019 class, 104 students), and 2) an online questionnaire survey on student preference on the learning method and reasons of preference.

RESULTS

The average anatomy examination scores of the 2020 class, who took online lectures and blended dissection laboratories, were significantly higher than those of the 2019 class, who participated in an offline lecture and dissection laboratories. The questionnaire survey revealed that students preferred online lectures over traditional large group lecture-based teaching because it allowed them to acquire increased self-study time, study according to their individual learning styles, and repeatedly review lecture videos.

CONCLUSION

This study suggests that a blended learning approach is an effective method for anatomy learning, and the advantage may result from increased self-directed study through online learning.