Application of image enhancement in the auxiliary diagnosis of oral potentially malignant disorders.

OBJECTIVES: The images of oral potentially malignant disorders (OPMDs) frequently encounter problems related to visual quality and image distortion, which may lead to serious misdiagnosis and missed diagnosis. This study is to explore the auxiliary effects of different optical image enhancement algorithms on the object detection of OPMDs lesions. METHODS: Digital images of OPMDs were collected, including white plaques, white stripes, and erosive lesions. The dataset was divided into a training set (6,488 images) and a validation set (2,592 images). Original images were processed using multiscale retinex (MSR), adaptive histogram equalization (AHE), and adaptive contrast enhancement (ACE), respectively. Object detection models based on You Only Look Once version 8 (YOLOv8) were used for lesion detection, and the diagnostic performance was evaluated using 328 images taken at different times. RESULTS: The model performance in the MSR-enhanced image set was superior to that in the original image set, with total accuracy increased for all three lesion types, and the sensitivity of complete correct recognition for complex multi-lesion images improved. Models trained with AHE and ACE preprocessing showed reduced diagnostic performance. CONCLUSION: Image enhancement algorithms can enhance the visual quality of OPMDs images, and the MSR algorithm is capable of strengthening the object detection ability in the computer vision model. CLINICAL RELEVANCE: This study provides an approach to reduce the misdiagnosis and missed diagnosis of OPMDs lesions in object detection model.