The Effectiveness of Artificial Intelligence in Detection of Oral Cancer

Segmentation of Oral Leukoplakia (OL) and Proliferative Verrucous Leukoplakia (PVL) Using Artificial Intelligence Techniques

“…As in our study, in work by Silva (2019), this feature proved useful in distinguishing the different lesions studied, with significant differences between healthy tissue and tissue with dysplasia; in the present work, a significance was obtained between OL and PVL. In this sense, it can be concluded that both entropy and the Moran Index can be used to detect changes in chromatin in premalignant lesions, a fact reinforced by the study by [ 20 – 22 ], who analyzed some nuclear characteristics and verified that the nuclear texture is an effective variable in differentiating the degrees of dysplasia in Barrett's ssophagus, in addition to being efficient in predicting progression to cancer, which, together with our results, further highlights the importance of evaluating the nuclear textures in an attempt to elucidate the pathological conditions of premalignant lesions.…”

Artificial intelligence in early diagnosis and prevention of oral cancer

“…They concluded that deep learning was more precise than supervised ML for OC early diagnosis. [6] A scoping review highlighted the effect that the variabilities of photographic images could have on the identification process of OC or OPMDs. [3] Warin et al conducted a study to develop an automated classification and detection system for OC screening.…”

“…AI technologies have been found to be effective in detecting breast, lung, and oral cancers. [6] The J o u r n a l P r e -p r o o f potential of AI to improve the efficiency of OC screening is the reason for its implementation in oncology. [6] AI can be divided into traditional machine learning and deep learning.…”

“…[6] The J o u r n a l P r e -p r o o f potential of AI to improve the efficiency of OC screening is the reason for its implementation in oncology. [6] AI can be divided into traditional machine learning and deep learning. Traditional machine learning uses algorithms and computer processes to calculate information and recognize input data patterns to offer a quantified diagnostic result.…”

“…[10,11] Presently these techniques are being assessed for more effective methods for diagnosis, especially for the screening of diseases where fewer doctors and trained experts are available. [6] AI can be used in many ways in the prevention of oral cancer and its early diagnosis. AI can precisely analyze a vast dataset of various imaging modalities, such as fluorescent, hyperspectral, cytological, histological, radiological, endoscopic, clinical, and infrared thermal modalities.…”