Real-time, in vivo skin cancer triage by laser-induced plasma spectroscopy combined with a deep learning-based diagnostic algorithm

Background: Although various skin cancer detection devices have been proposed, most of them are not used owing to their insufficient diagnostic accuracies. Laser-induced plasma spectroscopy (LIPS) can noninvasively extract biochemical information of skin lesions using an ultrashort pulsed laser. Objective: To investigate the diagnostic accuracy and safety of real-time noninvasive in vivo skin cancer diagnostics utilizing nondiscrete molecular LIPS combined with a deep neural network (DNN)-based diagnostic algorithm. Methods: In vivo LIPS spectra were acquired from 296 skin cancers (186 basal cell carcinomas, 96 squamous cell carcinomas, and 14 melanomas) and 316 benign lesions in a multisite clinical study. The diagnostic performance was validated using 10-fold cross-validations. Results: The sensitivity and specificity for differentiating skin cancers from benign lesions using LIPS and the DNN-based algorithm were 94.6% (95% CI: 92.0%-97.2%) and 88.9% (95% CI: 85.5%-92.4%), respectively. No adverse events, including macroscopic or microscopic visible marks or pigmentation due to laser irradiation, were observed. Limitations: The diagnostic performance was evaluated using a limited data set. More extensive clinical studies are needed to validate these results. Conclusions: This LIPS system with a DNN-based diagnostic algorithm is a promising tool to distinguish skin cancers from benign lesions with high diagnostic accuracy in real clinical settings. ( J Am Acad Dermatol 2023;89:99-105.)

Automated summary

This study investigates the diagnostic accuracy and safety of real-time noninvasive in vivo skin cancer diagnostics using nondiscrete molecular LIPS combined with a deep neural network (DNN)-based diagnostic algorithm. The results show that this LIPS system with a DNN-based diagnostic algorithm is a promising tool to distinguish skin cancers from benign lesions with high diagnostic accuracy in real clinical settings.