evidencegaps
Primary study · included by 2 reviews

A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study

Fu 2020 · Fu Qiuyun, Chen Yehansen, Li Zhihang, et al. · eClinicalMedicine · 2020

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 Fu 2020 · 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
Detection of oral potentially malignant disorders and oral cancer with white light through deep learning: a systematic review and meta-analysis.
Non-Cochrane · DTA · 2026 · cited as Fu 2020 · Supplementary Table 3; reference list

The review does not say which of its outcomes this study informs.

2 including reviews in total.