evidencegaps
Primary study · included by 1 review

Classifying histopathological images of oral squamous cell carcinoma using deep transfer learning

Panigrahi 2023 · Panigrahi Santisudha, Nanda Bhabani Sankar, Bhuyan Ruchi, et al. · Heliyon · 2023

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 Panigrahi 2023 · Table 1; Figures 3-5
Outcome this study informsPooled effectEvidenceCertainty
Diagnostic accuracy
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 accuracy (proportion) 0.89 [0.83, 0.95]11 studiesGRADE not reported

1 including review in total.