NCT06600165

Intraoperative Confocal Laser Scanning Microscopy With Use of AI for Optimized Surgical Excision of Basal Cell Carcinoma

Intraoperative Confocal Laser Scanning Microscopy and Artificial Intelligence for Optimized Surgical Excision of Basal Cell Carcinoma

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,000 (estimated)
• Sponsor: LMU Klinikum · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

Summary

The aim is to use AI to assist surgeons in analyzing CLSM tissue slide images obtained during BCC surgeries with the aim to integrate it in real time. We plan to use AI to analyze CLSM images of BCCs and distinguish between tumor tissue, inflammatory tissue, and non-tumor/non-inflammatory tissue. This approach would provide surgeons with real-time feedback and automated image analysis, leading to a more targeted and efficient approach to tissue analysis. By improving the accuracy and speed of tissue analysis, our proposal could ultimately improve operative patient outcomes and benefit healthcare professionals.

Conditions

• Basal Cell Carcinoma

Interventions

Type	Name	Description
PROCEDURE	Ex vivo confocal microscopy	The aim is to use AI to assist surgeons in analyzing CLSM tissue slide images obtained during BCC surgeries with the aim to integrate it in real time. We plan to use AI to analyze CLSM images of BCCs and distinguish between tumor tissue, inflammatory tissue, and non-tumor/non-inflammatory tissue. This approach would provide surgeons with real-time feedback and automated image analysis, leading to a more targeted and efficient approach to tissue analysis. By improving the accuracy and speed of tissue analysis, our proposal could ultimately improve operative patient outcomes and benefit healthcare professionals.

Timeline

• Start date: 2025-03-01
• Primary completion: 2026-12-31
• Completion: 2027-12-31
• First posted: 2024-09-19
• Last updated: 2025-09-09

Locations

1 site across 1 country: Germany