AUTHOR=Marsden Helen , Kemos Polychronis , Venzi Marcello , Noy Mariana , Maheswaran Shameera , Francis Nicholas , Hyde Christopher , Mullarkey Daniel , Kalsi Dilraj , Thomas Lucy 

TITLE=Accuracy of an artificial intelligence as a medical device as part of a UK-based skin cancer teledermatology service

JOURNAL=Frontiers in Medicine

VOLUME=Volume 11 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1302363

DOI=10.3389/fmed.2024.1302363

ISSN=2296-858X

ABSTRACT=Introduction

An Artificial Intelligence as a medical device (AIaMD), built on convolutional-neural-networks, has demonstrated high sensitivity for melanoma. To be of clinical value, it needs to safely reduce referral rates.

The primary objective of this study was to demonstrate that the AIaMD had a higher rate of correctly classifying lesions that did not need to be referred for biopsy or urgent face-to-face dermatologist review, compared to teledermatology standard of care (SoC), while achieving the same sensitivity to detect malignancy. Secondary endpoints included the sensitivity, specificity, positive and negative predictive values and number needed to biopsy to identify one case of Melanoma or Squamous Cell Carcinoma (SCC) by both the AIaMD and SoC.

Methods

This prospective, single-centre, single-arm, masked, non-inferiority, adaptive, group-sequential design trial, recruited patients referred to a teledermatology cancer pathway (clinicaltrials.gov NCT04123678). Additional dermoscopic images of each suspicious lesion were taken using a smartphone with a dermoscopic lens attachment.
Images were assessed independently by a consultant dermatologist and the AIaMD. Outputs
were compared with the final histological or clinical diagnosis.

Results

700 patients with 867 lesions were recruited, of which 622 participants with 789 lesions were included in the Per protocol (PP) population. 63.3% PP participants were female; 89.0% identified as White, the median age was 51 (range 18-95); and all Fitzpatrick skin types were represented including 25/622 (4.0%) type IV-VI skin. 67 malignant lesions were identified, including 8 diagnosed as melanoma. The AIaMD sensitivity was set at 91% and 92.5%, to match the literature-defined clinician sensitivity (91.46%) as closely as possible. At both settings, the AIaMD identified had a significantly higher rate of identifying lesions that did not need a biopsy or urgent referral compared to SoC (p-value=0.001) with comparable sensitivity for skin cancer.

Discussion

The AIaMD identified significantly more lesions that did not need to be referred for biopsy or urgent face-to-face dermatologist review, compared to teledermatologists. This has the potential to reduce the burden of unnecessary referrals when used as part of a teledermatology service.