P22 Using VECTRA and AI analysis to monitor paediatric lesions: a review of cases.

Abstract

Paediatric melanoma is a rare but important diagnosis. In the paediatric cohort, diagnostic challenges arise due to lesion variability and the inherent difficulties associated with paediatric assessment. Clinical decision-making is further complicated by the need to balance caregivers' expectations and concerns with the complexities of paediatric surgery. Though the incidence of paediatric melanoma is fortunately rare, misdiagnosis and mismanagement can have disastrous outcomes. This study aims to review the role of VECTRA imaging combined with artificial intelligence (AI) analysis in the assessment and management of undifferentiated cutaneous lesions in a paediatric cohort. A retrospective review was conducted at a single-centre academic Dermatology clinic based in Sydney, Australia. Fourteen patients under the age of 18 underwent VECTRA imaging for undifferentiated cutaneous lesions. Clinical data and follow-up outcomes were extracted from electronic medical records. Patients in this cohort ranged in age from 3 months to 18 years, with a mean age of 8 years. The majority of lesions were small congenital naevi and were managed conservatively with annual follow-up (n = 10). One patient underwent surgical excision on account of patient and caregiver preference as opposed to clinical concern for malignancy. This was a small retrospective case series which considered the implications of VECTRA imaging and AI analysis as an adjunctive tool in assessing paediatric cutaneous lesions. The definitive diagnostic value of these technologies remains to be elucidated; however, integration may facilitate conservative management, reduce unnecessary excisions, and mitigate caregiver anxiety. Further research is required to validate these findings.