Objective
This
study aimed to review and synthesize studies using artificial
intelligence (AI) for classifying, detecting, or segmenting oral mucosal
lesions on photographs.
Materials and method
Inclusion
criteria were (1) studies employing AI to (2) classify, detect, or
segment oral mucosa lesions, (3) on oral photographs of human subjects.
Included studies were assessed for risk of bias using Quality Assessment
of Diagnostic Accuracy Studies (QUADAS-2). A PubMed, Scopus, Embase,
Web of Science, IEEE, arXiv, medRxiv, and grey literature (Google
Scholar) search was conducted until June 2023, without language
limitation.
Results
After
initial searching, 36 eligible studies (from 8734 identified records)
were included. Based on QUADAS-2, only 7% of studies were at low risk of
bias for all domains. Studies employed different AI models and reported
a wide range of outcomes and metrics. The accuracy of AI for detecting
oral mucosal lesions ranged from 74 to 100%, while that for clinicians
un-aided by AI ranged from 61 to 98%. Pooled diagnostic odds ratio for
studies which evaluated AI for diagnosing or discriminating potentially
malignant lesions was 155 (95% confidence interval 23–1019), while that
for cancerous lesions was 114 (59–221).
Conclusions
AI
may assist in oral mucosa lesion screening while the expected accuracy
gains or further health benefits remain unclear so far.
Clinical relevance
Artificial
intelligence assists oral mucosa lesion screening and may foster more
targeted testing and referral in the hands of non-specialist providers,
for example. So far, it remains unclear if accuracy gains compared with
specialized can be realized.
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