Yoshizawa K, Ando H, Kimura Y et al. Automatic discrimination of Yamamoto-Kohama classification by machine learning approach for invasive pattern of oral squamous cell carcinoma using digital microscopic images: a retrospective study. Oral Surg Oral Med Oral Pathol Oral Radiol 2022; 133: 441-452.
Possible greater consistency.
Oral squamous cell carcinoma (OSCC) is invasive, leading to metastases and poor survival prognosis. The Yamamoto-Kohama (YK) classification differentiates the histopathological appearance of OSCC into grades 1 to 4D, the higher grades being the most invasive.
Here, historic (1989 to 2009) stained specimens of OSCC (n = 101) were graded according to the YK system. Using a two-stage machine learning approach - identifying a region of interest (the invasive front of the lesion) and evaluating the mode of invasion - results showed no significant differences in classification accuracy between clinician and machine (except for YK grade 2 cases, the reasons for which discrepancies are discussed).
It is known that visual examination of specimens leads to significant differences in determinations between examiners and the use of AI may introduce greater consistency. AI may also be useful in countries where there are few pathologists. However, where 'decision making and prediction' are left to the machine, caution is needed.
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Hellyer, P. AI in oral cancer diagnosis. Br Dent J 232, 629 (2022). https://doi.org/10.1038/s41415-022-4275-x