研究目的
To develop a low-cost, point-of-care, smartphone-based, dual-modality, dual-view oral cancer screening device with neural network classification for early detection of pre-cancerous and cancerous lesions in low-resource communities.
研究成果
The smartphone-based, dual-modality, dual-view oral cancer screening device shows promise for early detection of oral cancer in low-resource communities. Both the remote specialist and CNN achieved high sensitivity, specificity, PPV, and NPV compared to the gold-standard on-site specialist diagnosis. Future improvements could include adding benign cases to the training dataset and reducing the intraoral probe's size for better access to deep oral cavity sites.
研究不足
The study did not include benign cases in the training and classification processes, which could affect the CNN's performance. Additionally, the device's effectiveness in regions without cellular data or internet access was not addressed. The intraoral probe's size may limit access to deep oral cavity sites in some patients.
1:Experimental Design and Method Selection:
The study designed a dual-modality, dual-view oral cancer screening device using autofluorescence imaging (AFI) and white light imaging (WLI) on a smartphone platform. A custom Android application was developed to synchronize LED illumination and image capture.
2:Sample Selection and Data Sources:
Data was collected from 190 patients at three testing sites in India, with 170 image pairs of sufficient quality used for analysis.
3:List of Experimental Equipment and Materials:
The device includes a commercially available Android smartphone (LG G4), custom optical system for intraoral imaging, six 405 nm Luxeon UV U1 LEDs for AFI, four 4000 K Luxeon Z ES LEDs for WLI, and an emission filter for AFI.
4:Experimental Procedures and Operational Workflow:
Patients underwent conventional visual oral exams followed by smartphone-based imaging exams. Images were uploaded to a cloud server for remote specialist diagnosis and CNN classification.
5:Data Analysis Methods:
Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to compare remote specialist diagnosis and CNN results to the gold-standard on-site specialist diagnosis.
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