عنوان انگلیسی مقاله:
Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope
ترجمه فارسی عنوان مقاله:
تشخیص سرطان پوست با الگوریتم های یادگیری عمیق و آنالیز صدا: یک مطالعه بالینی آینده نگر از یک درموسکوپ ابتدایی
Sciencedirect - Elsevier - EBioMedicine 43 (2019) 107–113
A. Dascalu a,⁎, E.O. Davidb
Background: Skin cancer (SC), especiallymelanoma, is a growing public health burden. Experimental studies have
indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities.
Previously, it was demonstrated that diagnostics by dermoscopy are improved by applying an additional
sonification (data to sound waves conversion) layer on DL algorithms. The aim of the study was to determine
the impact of image quality on accuracy of diagnosis by sonification employing a rudimentary skin magnifier
with polarized light (SMP).
Methods: Dermoscopy images acquired by SMP were processed by a first deep learning algorithm and sonified.
Audio output was further analyzed by a different secondary DL. Study criteria outcomes of SMP were specificity
and sensitivity,which were further processed by a F2-score, i.e. applying a twice extra weight to sensitivity over
positive predictive values.
Findings: Patients (n=73) fulfilling inclusion criteriawere referred to biopsy. SMP analysis metrics resulted in a
receiver operator characteristic curve AUCs of 0.814 (95% CI, 0.798–0.831). SMP achieved a F2-score sensitivity of
91.7%, specificity of 41.8% and positive predictive value of 57.3%.Diagnosing the sameset of patients lesions by an
advanced dermoscope resulted in a F2-score sensitivity of 89.5%, specificity of 57.8% and a positive predictive
value of 59.9% (P=NS).
Interpretation: DL processing of dermoscopic images followed by sonification results in an accurate diagnostic
output for SMP, implying that the quality of the dermoscope is not the major factor influencing DL diagnosis of
skin cancer. Present system might assist all healthcare providers as a feasible computer-assisted detection system.
Fund: Bostel Technologies.
Keywords: Skin cancer | Deep learning | Dermoscopy | Sonification | Melanoma | Telemedicine | Artificial intelligence