Supplemental Online Content - Development and validation of a deep learning model for improving detection of non-melanoma skin carcinomas treated with Mohs micrographic surgery

Published: 8 August 2023| Version 2 | DOI: 10.17632/fh7sk5ksmk.2
Contributors:
Eugene Tan, Sophie Lim, Duncan Lamont, David Lim, Richard Epstein, Frank Lin

Description

Supplemental Online Content sTable 1. CLEAR Derm statement. sTable 2. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. sFigure 3. Convolutional Neural Network Image Classification with a Deep Learning Model Architecture. sFigure 4. Evaluation of Model Performance for Lesion Detection in Mohs frozen section Images. sFigure 5. Evaluation of concordance in pathology review. sTable 6. Evaluation of leave-one-out cross-validation for patient-level performance in the development cohort. sTable 7. Performance of prediction models in temporal validation cohort stratified by BCC subtypes.

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Categories

Artificial Intelligence, Skin Cancer, Mohs Surgery, Deep Learning

Funding

Australian Medical Research Future Fund

RARUR000125

Cancer Institute of New South Wales

2021/CBG003

Licence