Technical improvements in preparing 3D printed anatomical models for comminuted fracture preoperative planning

AI-powered techniques in anatomical imaging: Impacts on veterinary diagnostics and surgery

BACKGROUND

Artificial intelligence (AI) is rapidly transforming veterinary diagnostic imaging, offering improved accuracy, speed, and efficiency in analyzing complex anatomical structures. AI-powered systems, including deep learning and convolutional neural networks, show promise in interpreting medical images from various modalities like X-rays, ultrasounds, CT scans, and MRI/mammography.

STUDY DESIGN

Narrative review OBJECTIVE: This review aims to explore the innovations and challenges of AI-enabled imaging tools in veterinary diagnostics and surgery, highlighting their potential impact on diagnostic accuracy, surgical risk mitigation, and personalized veterinary healthcare.

METHODS

We reviewed recent literature on AI applications in veterinary diagnostic imaging, focusing on their benefits, limitations, and future directions.

CONCLUSION

AI-enabled imaging tools hold immense potential for revolutionizing veterinary diagnostics and surgery. By enhancing diagnostic accuracy, enabling precise surgical planning, and supporting personalized treatment strategies, AI can significantly improve animal health outcomes. However, addressing challenges related to data privacy, algorithm bias, and integration into clinical workflows is crucial for the widespread adoption and success of these transformative technologies.

The effectiveness of three-dimensional printing in undergraduate and postgraduate anatomy education: A review of reviews

PURPOSE

Several reviews and meta-analyses about the value of three-dimensional (3D) printing in anatomy education have been published in the last years, with variable-and sometimes confusing- outcomes. We performed a review of those reviews, in order to shed light on the results concerning the effectiveness of 3D printing in anatomy education, compared to specific traditional methods and other technologies.

METHODS

The electronic databases PubMed, ERIC and Cochrane library were searched for reviews or meta-analyses with purpose to investigate the effectiveness of 3D printing in undergraduate and postgraduate anatomy education.

RESULTS

Seven papers were included: four systematic reviews with meta-analysis, one narrative, one scoping and one systematic review. Overall, it has been shown that 3D printing is more effective than two-dimensional (2D) images for undergraduate health science students, but not for medical residents. Also, it seems to be more effective than 2D methods for teaching anatomy of some relatively complex structures, such as the nervous system. However, there is generally lack of evidence about the effectiveness of 3D printing in comparison with other 3D visualization methods.

CONCLUSIONS

For students, the effectiveness of 3D printing in anatomy education is higher than 2D methods. There is need for studies to investigate the effectiveness of 3D printing in comparison with other 3D visualization methods, such as cadaveric dissection, prosection and virtual reality. There is also need for research to explore if 3D printing is effective as a supplementary tool in a blended anatomy learning approach.