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RecruitingNCT06600165

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

Sponsor

LMU Klinikum

Enrollment

1,000 participants

Start Date

Mar 1, 2025

Study Type

OBSERVATIONAL

Conditions

Basal Cell Carcinoma

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.

Eligibility

Plain Language Summary

Simplified for easier understanding

This study is testing whether a new imaging technique called intraoperative confocal laser scanning microscopy — combined with artificial intelligence — can help surgeons more accurately remove basal cell carcinoma (the most common type of skin cancer) during surgery, reducing the chance of leaving cancer cells behind. **You may be eligible if...** - You have been diagnosed with basal cell carcinoma and are scheduled for surgical removal **You may NOT be eligible if...** - You are unable to provide informed consent for the procedure Talk to your doctor to see if this trial is right for you.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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Interventions

PROCEDUREEx 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.

Locations(1)

Clinic and Policlinic of Dermatology and Allergy, LMU Munich

Munich, Bavaria, Germany

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NCT06600165

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