Convolutional Neural Network for Combined Classification of Fluorescent Biomarkers and Expert Annotations using White Light Images
| Autor: | David Edlund, Keith Angelino, Gregory Yauney, Pratik Shah |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Receiver operating characteristic
business.industry Computer science Pattern recognition 030206 dentistry Image segmentation 010501 environmental sciences 01 natural sciences Convolutional neural network 03 medical and health sciences 0302 clinical medicine Binary classification Medical imaging White light Biomarker (medicine) Diagnostic biomarker Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | BIBE |
| Popis: | Fluorescent biomarkers are important indicators of disease, but imaging them can require specialized and often-expensive devices. Periodontal and dental diseases resulting from microbial plaque biofilms, if diagnosed early with biomarker images and expert knowledge, can be treated to prevent occurrences of serious systemic illnesses. We report two convolutional neural network classifiers trained with dentist annotations of disease signatures and fluorescent porphyrin biomarker images to identify dental plaque in white light images as a per-pixel binary classification task. The classifiers were trained and tested with millions of image patches from two datasets collected from 27 consenting adults using handheld intraoral cameras. The areas under the receiver operating characteristic curves for the test sets were calculated to be 0.7694 and 0.8720. Once trained, the classifiers predict the location of plaque in white light images without requiring specialized biomarker imaging devices or expert intervention. This generalized approach can be useful in other domains where diagnostic biomarker predicting can augment expert knowledge using standard white light images. |
| Databáze: | OpenAIRE |
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