Artificial Intelligence Support for Skin Lesion Triage in Primary Care and Dermatology
ABSTRACT
Background:
Primary care providers, dermatology specialists, and healthcare access are key components of primary prevention, early diagnosis, and treatment of skin cancer. Artificial Intelligence (AI) offers the promise of diagnostic support for non-specialists, but real-world clinical validation in primary care is lacking.
Objective:
1. Assessment of the reliability of an AI-based clinical triage algorithm in classifying benign and malignant skin lesions 2. To evaluate the quality of images obtained in primary care using the study camera (3Gen Dermlite cam v4 or similar)
Methods:
This was a single-centre, prospective, double-blinded observational study with a predetermined study design. We recruited participants with suspected skin cancer in 20 primary care practices who were referred for assessment via teledermatology. A second set of photographs taken using a standardised camera was processed by the AI algorithm. We evaluated image quality and compared two teledermatologists’ diagnoses by consensus (the ‘gold standard’) with AI and histology where applicable.
Results:
Our primary outcome assessment stratified 391 skin lesions by management as benign, uncertain or malignant. Uncertain lesions were not included in the sensitivity/specificity analysis. Uncertain lesions included lesions that had either diagnostic or management uncertainties. Of the remaining 242 lesions, sensitivity was 97.26% (95% CI, 93.13% to 99.25%), and specificity was 97.92% (95% CI, 92.68% to 99.75%). The AI algorithm was compared with histological diagnoses for 123 lesions. The sensitivity was 100% (95% CI, 95.85% to 100.00%), specificity was 72.22% (95% CI, 54.81% to 85.80%).
Conclusions:
The AI algorithm demonstrates encouraging results, with high sensitivity and specificity, concordant with previous AI studies. It shows potential as a triage tool in conjunction with teledermatology to augment healthcare and improve access to dermatology. Further real-life studies need to be conducted on a bigger scale to assess its reliability, usability and cost-effectiveness in primary care.
Citation
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Copyright
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