Primary study · included by 1 review

Noninvasive diagnosis of oral squamous cell carcinoma by multi‐level deep residual learning on optical coherence tomography images

Yuan 2023 · Yuan Wei, Yang Jinsuo, Yin Boya, et al. · Oral Diseases · 2022

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 Yuan 2023 · Table 1; Figures 3-5

Outcome this study informs	Pooled effect	Evidence	Certainty
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 studies	GRADE not reported
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 studies	GRADE 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 studies	GRADE not reported

1 including review in total.