AI-enhanced OCT Imaging for Pre-surgical Margin Detection of Basal Cell Carcinoma

N/ANot Yet RecruitingNCT07358637

Henry Ford Health System30 enrolled

Overview

Basal cell carcinomas (BCCs) are the most common human malignancy, affecting about 2 million Americans each year. Mohs micrographic surgery (MMS) removes tissue by sequential excision. Costs for MMS could be reduced if the number of necessary excision stages were decreased by a more accurate initial tumor margin assessment. The goal of this observational study is to learn if Optical Coherence Tomography (OCT) used in conjunction with artificial intelligence algorithms is accurate in the detection of superficial BCC margins prior to MMS. This study also aims to determine if AI-OCT guided margin delineation can reduce the number of stages in MMS. Researchers will first focus on validating AI-OCT as a method for accurately detecting BCCs. A follow-up study would then address the guided pre-surgical margin delineation.

Study Type

OBSERVATIONAL

Enrollment

Conditions

Basal Cell Carcinoma of Skin

Interventions

AI-OCTDIAGNOSTIC_TEST

Optical Coherence Tomography augmented by artificial intelligence software

Medical Language ↔ Plain English

Inclusion Criteria: * Male or Female, ages 18 or older * at least one biopsy proven superficial or nodular BCC * willingness to have photographs taken of the treatment area * ability to understand and willingness to sign a written informed consent document Exclusion Criteria: * infiltrative, micronodular, or morpheaform BCC * pregnant women * subjects not willing to have a biopsy taken from the treatment area * subjects with herpes simplex virus infection in the treatment area

AI-enhanced OCT Imaging for Pre-surgical Margin Detection of Basal Cell Carcinoma

Overview

Conditions

Interventions

Eligibility

Outcomes

Primary Outcomes