AI in pituitary surgery: improve intraoperative anatomical recognition | Google ai course online | Generative ai certification microsoft azure | Generative ai course free with certificate | Turtles AI

A recent study has explored the use of AI to improve anatomical recognition during endoscopic surgery of pituitary adenoma. The AI ​​assistance has shown to enhance the recognition of the saddle, reducing errors and improving safety, especially among less experienced surgeons. The results suggest a significant impact in surgical training and clinical practice.

Key points:

• The assistance of AI improves anatomical recognition during the endoscopic resection of the pituitary adenoma.
• Junior students and trainees draw the greatest benefits from the use of AI, with significant improvements in the recognition of the saddle.
• Expert neurosurgeons show a marginal improvement, but AI reduces false positive errors.
• Technology has an application potential to improve surgical training and as a intraoperative decision -making support.

Endoscopic surgery for the resection of pituitary adenoma is one of the most complex challenges of neurosurgery, mainly due to the position of the tumor, which is located in a particularly critical anatomical area and rich in neurovascular structures. The difficulty in correctly identifying these structures during the intervention, such as optic nerves and internal carotid arteries, is one of the main obstacles to avoid collateral damage. The importance of a precise anatomical orientation is such that aids such as neuronavigation, micro-doppler and neurophysiological monitoring are frequently used, but these tools still have limits, especially in high complexity and real-time environments. In this scenario, AI is emerging as a promising technology, capable of improving the recognition of anatomical structures important for surgical safety.

In a recent study, the impact of AI was examined in improving the recognition of the Turkish saddle during the endoscopic resection of the pituital adenoma. The intent was to test whether the AI, able to segment the saddle anatomy, could support surgeons in outlining the critical areas not to harm with greater precision. The AI ​​model, developed in a preclinical phase, has made it possible to compare the performance of doctors with various levels of experience in anatomical recognition, using six radiological images of the Sellaro structures, before and after the introduction of the support of the AI. The participants, divided into four groups (medical students, junior trainees, intermediate and expert neurosurgeons), labeled the images without assistance and subsequently with the help of the AI ​​system.

The results showed a general improvement in precision scores, measured through the similarity coefficient says, going from 70.7% without assistance to 77.5% with the assistance of the AI. This improvement was statistically significant, with an average increase of +6.7% in global performance. Medicine students, in particular, benefited more than AI, improving their score from 66.2% to 78.9% (+12.8%), followed by junior trainees, who recorded an increase of 8, 1%. Intermediate intermediate internships showed a more content improvement (+4.8%), while already very performing experts saw a marginal improvement (+1.2%), but still statistically significant.

In addition, the assistance of AI has also had a positive impact on the recognition of the "centerid" of the saddle, the fundamental point for the safety of surgical access. Before the use of AI, only 79% of the participants included the center of the saddle in their annotations, but with the assistance of the AI, this percentage increased 100%. The groups with less experience (junior students and internships) have made the greatest number of changes to their annotations thanks to the support of the AI, with a modification rate of 69.4%, compared to 33.3% of intermediate intermediate trainees e 38.9% of experts. Despite these improvements, it was observed that some experts preferred not to make complete reliance on the AI, especially in the event that the anatomy was already clear or when the model suggested changes in the annotations, leading to a lower rate of changes.

One of the most relevant discoveries of the study concerns the reduction of errors, in particular of false positives. These errors, which could lead to accidental damage to the critical surrounding structures, have been significantly reduced thanks to the AI. False positives decreased to a greater magnitude than false negatives, offering a reassuring improvement in safety. This is particularly important in neurosurgery, where an identification error could cause permanent damage to vital structures.

The improvement of performance with AI was evident above all among groups with less experience. This suggests that AI could play a fundamental role in the formation of surgeons, helping trainees to achieve levels of competence similar to those of experts in relatively short times. With the integration of AI in surgical formation, it may be possible to optimize learning, allowing a real time display of anatomical structures during operations, which would be a precious support even in high pressure situations.

The possibility of using AI not only as a support tool in the pre-operative phase, but also as an assistant during the intervention, opens new scenarios for surgery. The combination of AI with technologies such as augmented reality could facilitate the recognition of anatomical structures in real time, further improving the safety and effectiveness of the interventions. In addition, with the use of AI to automatically identify anatomical structures in surgical images or videos, teaching databases could be created that allow medical students and trainees to improve their skills before dealing with real operations.

In the future, the improvement of AI models, combined with integration with advanced visualization systems, could revolutionize the way in which surgical interventions are performed, minimizing errors and increasing precision in the tumor resections. The challenge remains to perfect the technology for use in real time during operations, and to develop user interfaces that can integrate without problems with the workflow of surgeons. The road to the clinical adoption of these technologies is still long, but the progress made so far are promising.

The continuous evolution of AI Ai technologies applied to surgery seems destined to improve not only the safety of interventions, but also to transform medical education, making the learning of surgical skills more effective and safe.