evidencegaps
Primary study · included by 1 review

Automatic classification and detection of oral cancer in photographic images using deep learning algorithms

Warin 2021 · Warin Kritsasith, Limprasert Wasit, Suebnukarn Siriwan, et al. · Journal of Oral Pathology & Medicine · 2021

Included by

Artificial intelligence for oral cancer diagnosis: a systematic review and meta-analysis of image-based and non-imaging models.
Non-Cochrane · DTA;prognostic · 2026 · cited as Warin 2021 · Table 1; Figures 3-5
Outcome this study informsPooled effectEvidenceCertainty
Diagnostic sensitivity
AI models for oral cancer diagnosis or risk assessment (image-based and text-based) vs Reference standard (histopathology where available, otherwise expert clinical diagnosis)
Pooled sensitivity (proportion) 0.90 [0.81, 0.98]13 studiesGRADE not reported
Diagnostic specificity
AI models for oral cancer diagnosis or risk assessment (image-based and text-based) vs Reference standard (histopathology where available, otherwise expert clinical diagnosis)
Pooled specificity (proportion) 0.89 [0.84, 0.95]14 studiesGRADE not reported

1 including review in total.