MedNeuro3D

Existing brain tumor surgical plans rely on 2D MRI images, making it difficult to fully understand the spatial structure of the tumor and limiting the prediction of surgical paths and safety assurance. This raises concerns about the risk of damage to important structures or inaccuracies in planning during surgery. Experts have pointed out the need for 3D simulations before complex procedures.

MedNeuro3D creates precise 3D brain tumor models based on multi-angle MRI. It visualizes tumor volume, extent, and shape, going beyond simple detection, and helps design optimized surgical paths and avoid key structures. In particular, it is characterized by increased medical accessibility through multinational remote collaboration capabilities and cloud-based workflows.

While existing solutions are limited to determining the presence or absence of tumors or simple segmentation, MedNeuro3D provides a 3D interface specialized for surgical planning.

Target customers are hospitals and medical institutions (neurosurgery). Neurosurgeons or radiology experts are likely to be the actual purchasers or adopters (B2B), and the situation is similar for government or public medical institutions (B2G). It is not intended for individual patients (B2C) and is likely to be introduced into hospital systems in the form of a license or subscription.

Due to the nature of cloud-based software, it can be expanded without being tied to specific countries or regulations. However, there are barriers to entry as medical devices may require FDA and CE certification.

The basic technology (3D reconstruction of medical images) can be applied to surgical planning for other brain diseases or body parts (heart, liver, etc.), making it highly scalable. However, it is optimized for brain-specific workflows, so additional development may be required when applied to other fields.

The technical completeness was recognized by winning the CES Innovation Award Honoree in the digital health category. Although the information is limited, the application specialized for brain tumors seems to have been highly evaluated.

In the medical device market, AI-based software is highly anticipated, but there may be a lack of verified clinical data, and the adoption speed of medical infrastructure is slow. Currently, it is in the research and pilot stage, and market response is expected to vary depending on actual clinical application. (Technical completeness seems high, but there are challenges until market adoption)