Diagnostic assessment of deep learning for melanocytic lesions using whole-slide pathological images.

ABSTRACT:

Background

Deep learning has the potential to improve diagnostic accuracy and efficiency in medical image recognition. In the current study, we developed a deep learning algorithm and assessed its performance in discriminating melanoma from nevus using whole-slide pathological images (WSIs).

Methods

The deep learning algorithm was trained and validated using a set of 781 WSIs (86 melanomas, 695 nevi) from PLA General Hospital. The diagnostic performance of the algorithm was tested on an independent test set of 104 WSIs (29 melanomas, 75 nevi) from Tianjin Chang Zheng Hospital. The same test set was also diagnostically classified by 7 expert dermatopathologists.

Results

The deep learning algorithm receiver operating characteristic (ROC) curve achieved a sensitivity 100

SUBMITTER: Ba W 

PROVIDER: S-EPMC8254118 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature