Key points
Early diagnosis and treatment of vocal cord leukoplakia may prevent progression and malignancy.
Lesions of vocal cord leukoplakia were classified as nonsurgical group (NSG) and surgical group (SG) using pathology as a gold standard.
We applied deep learning (DL) with convolutional neural networks (CNNs) to segment and classify narrow band imaging (NBI) and white light imaging (WLI) of vocal cord leukoplakia .
The DL model could detect the lesions autonomously with average intersection-over-union (IoU) value exceeded 70%. For classification, the model was able to classify the surgical group (SG) by laryngoscope with a sensitivity of 0.93 and specificity of 0.94 in WLI, and a sensitivity of 0.99 and specificity of 0.97 in NBI. Moreover, this model achieved a mean average precision (mAP) of 0.81 in WLI and 0.92 in NBI with an IoU > 0.5.
The novel DL model shows promise in assisting in accurate diagnosis of vocal cord leukoplakia from WLI and NBI.